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Goldenholz DM, Eccleston C, Moss R, Westover MB. Prospective validation of a seizure diary forecasting falls short. Epilepsia 2024. [PMID: 38606580 DOI: 10.1111/epi.17984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 03/13/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.
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Affiliation(s)
- Daniel M Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Celena Eccleston
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | | | - M Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- McCance Center for Brain Health, Boston, Massachusetts, USA
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Goldenholz DM, Karoly PJ, Viana PF, Nurse E, Loddenkemper T, Schulze-Bonhage A, Vieluf S, Bruno E, Nasseri M, Richardson MP, Brinkmann BH, Westover MB. Minimum clinical utility standards for wearable seizure detectors: A simulation study. Epilepsia 2024; 65:1017-1028. [PMID: 38366862 PMCID: PMC11018505 DOI: 10.1111/epi.17917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/11/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.
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Affiliation(s)
- Daniel M Goldenholz
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Philippa J Karoly
- Department of Neurology, University of Melbourne, Melbourne, Victoria, Australia
| | - Pedro F Viana
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ewan Nurse
- Seer Medical, Melbourne, Victoria, Australia
| | - Tobias Loddenkemper
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center Freiburg-University of Freiburg, Freiburg, Germany
| | - Solveig Vieluf
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Elisa Bruno
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Mona Nasseri
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | | | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- McCace Center, Boston, Massachusetts, USA
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Goldenholz DM, Goldenholz SR, Habib S, Westover MB. Inductive reasoning with large language models: a simulated randomized controlled trial for epilepsy. medRxiv 2024:2024.03.18.24304493. [PMID: 38562831 PMCID: PMC10984041 DOI: 10.1101/2024.03.18.24304493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Importance The analysis of electronic medical records at scale to learn from clinical experience is currently very challenging. The integration of artificial intelligence (AI), specifically foundational large language models (LLMs), into an analysis pipeline may overcome some of the current limitations of modest input sizes, inaccuracies, biases, and incomplete knowledge bases. Objective To explore the effectiveness of using an LLM for generating realistic clinical data and other LLMs for summarizing and synthesizing information in a model system, simulating a randomized clinical trial (RCT) in epilepsy to demonstrate the potential of inductive reasoning via medical chart review. Design An LLM-generated simulated RCT based on a RCT for treatment with an antiseizure medication, cenobamate, including a placebo arm and a full-strength drug arm, evaluated by an LLM-based pipeline versus a human reader. Setting Simulation based on realistic seizure diaries, treatment effects, reported symptoms and clinical notes generated by LLMs with multiple different neurologist writing styles. Participants Simulated cohort of 240 patients, divided 1:1 into placebo and drug arms. Intervention Utilization of LLMs for the generation of clinical notes and for the synthesis of data from these notes, aiming to evaluate the efficacy and safety of cenobamate in seizure control either with a human evaluator or AI-pipeline. Measures The AI and human analysis focused on identifying the number of seizures, symptom reports, and treatment efficacy, with statistical analysis comparing the 50%-responder rate and median percentage change between the placebo and drug arms, as well as side effect rates in each arm. Results AI closely mirrored human analysis, demonstrating the drug's efficacy with marginal differences (<3%) in identifying both drug efficacy and reported symptoms. Conclusions and Relevance This study showcases the potential of LLMs accurately simulate and analyze clinical trials. Significantly, it highlights the ability of LLMs to reconstruct essential trial elements, identify treatment effects, and recognize reported symptoms, within a realistic clinical framework. The findings underscore the relevance of LLMs in future clinical research, offering a scalable, efficient alternative to traditional data mining methods without the need for specialized medical language training.
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Affiliation(s)
- Daniel M Goldenholz
- Department of Neurology, Harvard Medical School, Boston USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
| | - Shira R Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
| | - Sara Habib
- Department of Neurology, Harvard Medical School, Boston USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Boston USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston USA
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Fernandes M, Westover MB, Singhal AB, Zafar SF. Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing. medRxiv 2024:2024.03.08.24304011. [PMID: 38559062 PMCID: PMC10980121 DOI: 10.1101/2024.03.08.24304011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Multi-center electronic health records (EHR) can support quality improvement initiatives and comparative effectiveness research in stroke care. However, limitations of EHR-based research include challenges in abstracting key clinical variables from non-structured data at scale. This is further compounded by missing data. Here we develop a natural language processing (NLP) model that automatically reads EHR notes to determine the NIH stroke scale (NIHSS) score of patients with acute stroke. METHODS The study included notes from acute stroke patients (>= 18 years) admitted to the Massachusetts General Hospital (MGH) (2015-2022). The MGH data were divided into training (70%) and hold-out test (30%) sets. A two-stage model was developed to predict the admission NIHSS. A linear model with the least absolute shrinkage and selection operator (LASSO) was trained within the training set. For notes in the test set where the NIHSS was documented, the scores were extracted using regular expressions (stage 1), for notes where NIHSS was not documented, LASSO was used for prediction (stage 2). The reference standard for NIHSS was obtained from Get With The Guidelines Stroke Registry. The two-stage model was tested on the hold-out test set and validated in the MIMIC-III dataset (Medical Information Mart for Intensive Care-MIMIC III 2001-2012) v1.4, using root mean squared error (RMSE) and Spearman correlation (SC). RESULTS We included 4,163 patients (MGH = 3,876; MIMIC = 287); average age of 69 [SD 15] years; 53% male, and 72% white. 90% patients had ischemic stroke and 10% hemorrhagic stroke. The two-stage model achieved a RMSE [95% CI] of 3.13 [2.86-3.41] (SC = 0.90 [0.88-0. 91]) in the MGH hold-out test set and 2.01 [1.58-2.38] (SC = 0.96 [0.94-0.97]) in the MIMIC validation set. CONCLUSIONS The automatic NLP-based model can enable large-scale stroke severity phenotyping from EHR and therefore support real-world quality improvement and comparative effectiveness studies in stroke.
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Affiliation(s)
- Marta Fernandes
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, Massachusetts, United States
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, United States
| | - Aneesh B. Singhal
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, Massachusetts, United States
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, Massachusetts, United States
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Song J, Westover MB, Zhang R. A neural mass model for disturbance of alpha rhythm in the minimal hepatic encephalopathy. Mol Cell Neurosci 2024; 128:103918. [PMID: 38296121 DOI: 10.1016/j.mcn.2024.103918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/18/2024] [Accepted: 01/21/2024] [Indexed: 02/13/2024] Open
Abstract
One of the early markers of minimal hepatic encephalopathy (MHE) is the disruption of alpha rhythm observed in electroencephalogram (EEG) signals. However, the underlying mechanisms responsible for this occurrence remain poorly understood. To address this gap, we develop a novel biophysical model MHE-AWD-NCM, encompassing the communication dynamics between a cortical neuron population (CNP) and an astrocyte population (AP), aimed at investigating the relationship between alpha wave disturbance (AWD) and mechanistical principles, specifically concerning astrocyte-neuronal communication in the context of MHE. In addition, we introduce the concepts of peak power density and peak frequency within the alpha band as quantitative measures of AWD. Our model faithfully reproduces the characteristic EEG phenomenology during MHE and shows how impairments of communication between CNP and AP could promote AWD. The results suggest that the disruptions in feedback neurotransmission from AP to CNP, along with the inhibition of GABA uptake by AP from the extracellular space, contribute to the observed AWD. Moreover, we found that the variation of external excitatory stimuli on CNP may play a key role in AWD in MHE. Finally, the sensitivity analysis is also performed to assess the relative significance of above factors in influencing AWD. Our findings align with the physiological observations and provide a more comprehensive understanding of the complex interplay of astrocyte-neuronal communication that underlies the AWD observed in MHE, which potentially may help to explore the targeted therapeutic interventions for the early stage of hepatic encephalopathy.
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Affiliation(s)
- Jiangling Song
- The Medical Big Data Research Center, Northwest University, Xi'an, China
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Rui Zhang
- The Medical Big Data Research Center, Northwest University, Xi'an, China.
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Sun S, Lomachinsky V, Smith LH, Newhouse JP, Westover MB, Blacker D, Schwamm L, Haneuse S, Moura LMVR. Benzodiazepine Initiation and the Risk of Falls or Fall-Related Injuries in Older Adults Following Acute Ischemic Stroke. medRxiv 2024:2024.02.06.24302430. [PMID: 38370813 PMCID: PMC10871457 DOI: 10.1101/2024.02.06.24302430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background Benzodiazepine use in older adults following acute ischemic stroke (AIS) is common, yet short-term safety concerning falls or fall-related injuries remains unexplored. Methods We emulated a hypothetical randomized trial of benzodiazepine use during the acute post stroke recovery period to assess incidence of falls or fall related injuries in older adults. Using linked data from the Get With the Guidelines Registry and Mass General Brigham's electronic health records, we selected patients aged 65 and older admitted for Acute Ischemic Stroke (AIS) between 2014 and 2021 with no documented prior stroke and no benzodiazepine prescriptions in the previous 3 months. Potential for immortal-time and confounding biases was addressed via separate inverse-probability weighting strategies. Results The study included 495 patients who initiated inpatient benzodiazepines within three days of admission and 2,564 who did not. After standardization, the estimated 10-day risk of falls or fall-related injuries was 694 events per 1000 (95% confidence interval CI: 676-709) for the benzodiazepine initiation strategy and 584 events per 1000 (95% CI: 575-595) for the non-initiation strategy. Subgroup analyses showed risk differences of 142 events per 1000 (95% CI: 111-165) and 85 events per 1000 (95% CI: 64-107) for patients aged 65 to 74 years and for those aged 75 years or older, respectively. Risk differences were 187 events per 1000 (95% CI: 159-206) for patients with minor (NIHSS≤ 4) AIS and 32 events per 1000 (95% CI: 10-58) for those with moderate-to-severe AIS. Conclusions Initiating inpatient benzodiazepines within three days of AIS is associated with an elevated 10-day risk of falls or fall-related injuries, particularly for patients aged 65 to 74 years and for those with minor strokes. This underscores the need for caution with benzodiazepines, especially among individuals likely to be ambulatory during the acute and sub-acute post-stroke period.
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Wei R, Ganglberger W, Sun H, Hadar P, Gollub R, Pieper S, Billot B, Au R, Eugenio Iglesias J, Cash SS, Kim S, Shin C, Westover MB, Joseph Thomas R. Linking brain structure, cognition, and sleep: insights from clinical data. Sleep 2024; 47:zsad294. [PMID: 37950486 PMCID: PMC10851868 DOI: 10.1093/sleep/zsad294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/13/2023] [Indexed: 11/12/2023] Open
Abstract
STUDY OBJECTIVES To use relatively noisy routinely collected clinical data (brain magnetic resonance imaging (MRI) data, clinical polysomnography (PSG) recordings, and neuropsychological testing), to investigate hypothesis-driven and data-driven relationships between brain physiology, structure, and cognition. METHODS We analyzed data from patients with clinical PSG, brain MRI, and neuropsychological evaluations. SynthSeg, a neural network-based tool, provided high-quality segmentations despite noise. A priori hypotheses explored associations between brain function (measured by PSG) and brain structure (measured by MRI). Associations with cognitive scores and dementia status were studied. An exploratory data-driven approach investigated age-structure-physiology-cognition links. RESULTS Six hundred and twenty-three patients with sleep PSG and brain MRI data were included in this study; 160 with cognitive evaluations. Three hundred and forty-two participants (55%) were female, and age interquartile range was 52 to 69 years. Thirty-six individuals were diagnosed with dementia, 71 with mild cognitive impairment, and 326 with major depression. One hundred and fifteen individuals were evaluated for insomnia and 138 participants had an apnea-hypopnea index equal to or greater than 15. Total PSG delta power correlated positively with frontal lobe/thalamic volumes, and sleep spindle density with thalamic volume. rapid eye movement (REM) duration and amygdala volume were positively associated with cognition. Patients with dementia showed significant differences in five brain structure volumes. REM duration, spindle, and slow-oscillation features had strong associations with cognition and brain structure volumes. PSG and MRI features in combination predicted chronological age (R2 = 0.67) and cognition (R2 = 0.40). CONCLUSIONS Routine clinical data holds extended value in understanding and even clinically using brain-sleep-cognition relationships.
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Affiliation(s)
- Ruoqi Wei
- Division of Pulmonary Critical Care & Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Wolfgang Ganglberger
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Sleep and Health Zurich, University of Zurich, Zurich, Switzerland
| | - Haoqi Sun
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Peter N Hadar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Randy L Gollub
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Benjamin Billot
- Computer Science and Artificial Intelligence Lab, MIT, Boston, MA, USA
| | - Rhoda Au
- Anatomy& Neurobiology, Neurology, Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine and School of Public Health, Boston University, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Isomics, Inc. Cambridge, MA, USA
- Center for Medical Image Computing, University College London, London, UK
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Soriul Kim
- Institute of Human Genomic Study, College of Medicine, Kore University, Seoul, Republic of Korea
| | - Chol Shin
- Institute of Human Genomic Study, College of Medicine, Kore University, Seoul, Republic of Korea
- Biomedical Research Center, Korea University Ansan Hospital, Ansan, Republic of Korea
| | - M Brandon Westover
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert Joseph Thomas
- Division of Pulmonary Critical Care & Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
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Sun H, Adra N, Ayub MA, Ganglberger W, Ye E, Fernandes M, Paixao L, Fan Z, Gupta A, Ghanta M, Moura Junior VF, Rosand J, Westover MB, Thomas RJ. Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study. Neurol Clin Pract 2024; 14:e200225. [PMID: 38173542 PMCID: PMC10759032 DOI: 10.1212/cpj.0000000000200225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 10/04/2023] [Indexed: 01/05/2024]
Abstract
Background and Objectives Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes. Methods This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk. Results There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort. Discussion The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.
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Affiliation(s)
- Haoqi Sun
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Noor Adra
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Muhammad Abubakar Ayub
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Wolfgang Ganglberger
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Elissa Ye
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Marta Fernandes
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Luis Paixao
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Ziwei Fan
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Aditya Gupta
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Manohar Ghanta
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Valdery F Moura Junior
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jonathan Rosand
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - M Brandon Westover
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert J Thomas
- Department of Neurology (HS, NA, MAA, WG, EY, MF, LP, ZF, AG, MG, VFMJ, JR, MBW), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health at Mass General (HS, VFMJ, JR, MBW); Department of Neurology (HS, WG, AG, MG, VFMJ, MBW), Beth Israel Deaconess Medical Center, Boston, MA; Department of Neurology (MAA), Louisiana State University Health Sciences Center, Shreveport, LA; Department of Neurology (LP), Washington University School of Medicine in St. Louis, MO; and Division of Pulmonary (RJT), Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
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9
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Ke SY, Wu H, Sun H, Zhou A, Liu J, Zheng X, Liu K, Westover MB, Xu H, Kong XJ. Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study. Front Neurosci 2024; 18:1330556. [PMID: 38332856 PMCID: PMC10850305 DOI: 10.3389/fnins.2024.1330556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.
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Affiliation(s)
- Si Yang Ke
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
| | - Huiwen Wu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Haoqi Sun
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Aiqin Zhou
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Jianhua Liu
- Huangshi Maternity and Child Health Care Hospital, Huangshi, Hubei, China
| | - Xiaoyun Zheng
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Kevin Liu
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Haiqing Xu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Xue-jun Kong
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Beth Israel Deaconess Medical Center, Boston, MA, United States
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10
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McCullar KS, Abbaspour S, Wang W, Aguirre AD, Westover MB, Klerman EB. Timing of diuretic administration effects on urine volume in hospitalized patients. Front Physiol 2024; 14:1208324. [PMID: 38321985 PMCID: PMC10844419 DOI: 10.3389/fphys.2023.1208324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 12/27/2023] [Indexed: 02/08/2024] Open
Abstract
Importance: Some medications have effects that depend on the time of day they are given. Current knowledge of the time-of-day effects of specific medications in hospitalized patients with cardiovascular disease is very limited. In hospitalized patients, increased medication efficiency might reduce dose (and associated side effects) and/or the length of time in the Intensive Care Unit (ICU) or hospital-potentially improving patient outcomes and patient and family quality of life and reducing financial costs. We studied whether the time of day or night patients in Cardiac or Intensive Care Units receive a diuretic affects urine volume. Methods: In this observational study, data were collected from 7,685 patients (63% male, 18 to 98 years old) admitted to one hospital's Acute Care Cardiac units, Cardiac ICUs, Cardiac Surgery ICUs, and/or Non-cardiac ICUs who received intravenous furosemide (a diuretic), had measurements of urine volume, were hospitalized for ≥3 days between January 2016 to July 2021 and were older than 18 years. The outcomes of interest were urine volume normalized by the most recent (not older than 24 h) weight or body mass index (BMI), (i) in the hour after the time of diuretic administration, and (ii) when no diuretics were administered for the previous 3 h. Results: We identified diuretic medication administration time 23:00-04:59 as a predictor of higher urine volume response. For patients without recent diuretic medication, higher urine volume was predicted 11:00-16:59 and 17:00-22:59. Other factors that affected urine volume response to the diuretic were sex, age, medication dose, creatinine concentration, diagnoses, and hospital unit. Discussion: Time-of-day of medication administration may be a factor associated with increased medication efficiency. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of medication administration, that may affect short- and longer-term outcomes.
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Affiliation(s)
- Katie S. McCullar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States
| | - Sara Abbaspour
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States
| | - Wei Wang
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, United States
| | - Aaron D. Aguirre
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Elizabeth B. Klerman
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, United States
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, United States
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11
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Goldenholz DM, Eccleston C, Moss R, Westover MB. Prospective validation of a seizure diary forecasting falls short. medRxiv 2024:2024.01.11.24301175. [PMID: 38260666 PMCID: PMC10802655 DOI: 10.1101/2024.01.11.24301175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
OBJECTIVE Recently, a deep learning AI model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSS) compared random forecasts and simple moving average forecasts to the AI. RESULTS The AI had an AUC of 0.82. At the group level, the AI outperformed random forecasting (BSS=0.53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (non-verified) diaries (with presumed under-reporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE The previously developed AI forecasting tool did not outperform a very simple moving average forecasting this prospective cohort, suggesting that the AI model should be replaced.
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Affiliation(s)
- Daniel M Goldenholz
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
| | - Celena Eccleston
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
| | | | - M Brandon Westover
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
- Dept. of Neurology, Massachusetts General Hospital, Boston 02114 MA
- McCance Center for Brain Health, Boston, 02114 MA
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12
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Dhakal K, Rosenthal ES, Kulpanowski AM, Dodelson JA, Wang Z, Cudemus-Deseda G, Villien M, Edlow BL, Presciutti AM, Januzzi JL, Ning M, Taylor Kimberly W, Amorim E, Brandon Westover M, Copen WA, Schaefer PW, Giacino JT, Greer DM, Wu O. Increased task-relevant fMRI responsiveness in comatose cardiac arrest patients is associated with improved neurologic outcomes. J Cereb Blood Flow Metab 2024; 44:50-65. [PMID: 37728641 PMCID: PMC10905635 DOI: 10.1177/0271678x231197392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 09/21/2023]
Abstract
Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery. Task-specific reference images were created from group-level fMRI responses from the healthy controls. Dice scores measured the overlap of individual subject-level fMRI responses with the reference images. Task-relevant responsiveness index (Rindex) was calculated as the maximum Dice score across the four tasks. Correlation analyses showed that increased Dice scores were significantly associated with arousal recovery (P < 0.05) and emergence from the minimally conscious state (EMCS) by one year (P < 0.001) for all tasks except motor imagery. Greater Rindex was significantly correlated with improved arousal recovery (P = 0.002) and consciousness (P = 0.001). For patients who survived to discharge (n = 6), the Rindex's sensitivity was 75% for predicting EMCS (n = 4). Task-based fMRI holds promise for detecting covert consciousness in comatose cardiac arrest patients, but further studies are needed to confirm these findings. Caution is necessary when interpreting the absence of task-relevant fMRI responses as a surrogate for inevitable poor neurological prognosis.
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Affiliation(s)
- Kiran Dhakal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Eric S Rosenthal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Annelise M Kulpanowski
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jacob A Dodelson
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Zihao Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Gaston Cudemus-Deseda
- Department of Cardiac Anesthesiology and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Marjorie Villien
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Alexander M Presciutti
- Department of Psychiatry, Center for Health Outcomes and Interdisciplinary Research, Massachusetts General Hospital, Boston, MA, USA
| | - James L Januzzi
- Department of Medicine, Cardiology Division, Massachusetts General Hospital and Baim Institute for Clinical Research, Boston, MA, USA
| | - MingMing Ning
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - W Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Edilberto Amorim
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - William A Copen
- Department of Radiology, Neuroradiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Pamela W Schaefer
- Department of Radiology, Neuroradiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, MA, USA
| | - David M Greer
- Department of Neurology, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
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13
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Sun H, Li P, Gao L, Yang J, Yu L, Buchman AS, Bennett DA, Westover MB, Hu K. Altered Motor Activity Patterns within 10-Minute Timescale Predict Incident Clinical Alzheimer's Disease. J Alzheimers Dis 2024; 98:209-220. [PMID: 38393904 PMCID: PMC10977378 DOI: 10.3233/jad-230928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/30/2023] [Indexed: 02/25/2024]
Abstract
Background Fractal motor activity regulation (FMAR), characterized by self-similar temporal patterns in motor activity across timescales, is robust in healthy young humans but degrades with aging and in Alzheimer's disease (AD). Objective To determine the timescales where alterations of FMAR can best predict the clinical onset of AD. Methods FMAR was assessed from actigraphy at baseline in 1,077 participants who had annual follow-up clinical assessments for up to 15 years. Survival analysis combined with deep learning (DeepSurv) was used to examine how baseline FMAR at different timescales from 3 minutes up to 6 hours contributed differently to the risk for incident clinical AD. Results Clinical AD occurred in 270 participants during the follow-up. DeepSurv identified three potential regions of timescales in which FMAR alterations were significantly linked to the risk for clinical AD: <10, 20-40, and 100-200 minutes. Confirmed by the Cox and random survival forest models, the effect of FMAR alterations in the timescale of <10 minutes was the strongest, after adjusting for covariates. Conclusions Subtle changes in motor activity fluctuations predicted the clinical onset of AD, with the strongest association observed in activity fluctuations at timescales <10 minutes. These findings suggest that short actigraphy recordings may be used to assess the risk of AD.
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Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Peng Li
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lei Gao
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jingyun Yang
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Lei Yu
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | | | - Kun Hu
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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14
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research Consortium Electroencephalography Database. Crit Care Med 2023; 51:1802-1811. [PMID: 37855659 PMCID: PMC10841086 DOI: 10.1097/ccm.0000000000006074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
OBJECTIVES To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. DESIGN Multicenter cohort, partly prospective and partly retrospective. SETTING Seven academic or teaching hospitals from the United States and Europe. PATIENTS Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included. INTERVENTIONS Not applicable. MEASUREMENTS AND MAIN RESULTS Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. CONCLUSIONS The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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15
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Ramantani G, Westover MB, Gliske S, Sarnthein J, Sarma S, Wang Y, Baud MO, Stacey WC, Conrad EC. Passive and active markers of cortical excitability in epilepsy. Epilepsia 2023; 64 Suppl 3:S25-S36. [PMID: 36897228 PMCID: PMC10512778 DOI: 10.1111/epi.17578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.
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Affiliation(s)
- Georgia Ramantani
- Department of Neuropediatrics and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Data Science, Massachusetts General Hospital McCance Center for Brain Health, Boston, Massachusetts, USA
- Research Affiliate Faculty, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Research Affiliate Faculty, Broad Institute, Cambridge, Massachusetts, USA
| | - Stephen Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sridevi Sarma
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - William C Stacey
- Department of Neurology, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Division of Neurology, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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16
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Fernandes M, Sun H, Chemali Z, Mukerji SS, M V R Moura L, Zafar SF, Sonni A, Biffi A, Rosand J, Brandon Westover M. Brain health scores to predict neurological outcomes from electronic health records. Int J Med Inform 2023; 180:105270. [PMID: 37890202 PMCID: PMC10842359 DOI: 10.1016/j.ijmedinf.2023.105270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 03/30/2023] [Accepted: 10/21/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Preserving brain health is a critical priority in primary care, yet screening for these risk factors in face-to-face primary care visits is challenging to scale to large populations. We aimed to develop automated brain health risk scores calculated from data in the electronic health record (EHR) enabling population-wide brain health screening in advance of patient care visits. METHODS This retrospective cohort study included patients with visits to an outpatient neurology clinic at Massachusetts General Hospital, between January 2010 and March 2021. Survival analysis with an 11-year follow-up period was performed to predict the risk of intracranial hemorrhage, ischemic stroke, depression, death and composite outcome of dementia, Alzheimer's disease, and mild cognitive impairment. Variables included age, sex, vital signs, laboratory values, employment status and social covariates pertaining to marital, tobacco and alcohol status. Random sampling was performed to create a training (70%) set for hyperparameter tuning in internal 5-fold cross validation and an external hold-out testing (30%) set of patients, both stratified by age. Risk ratios for high and low risk groups were evaluated in the hold-out test set, using 1000 bootstrapping iterations to calculate 95% confidence intervals (CI). RESULTS The cohort comprised 17,040 patients with an average age of 49 ± 15.6 years; majority were males (57 %), White (78 %) and non-Hispanic (80 %). The low and high groups average risk ratios [95 % CI] were: intracranial hemorrhage 0.46 [0.45-0.48] and 2.07 [1.95-2.20], ischemic stroke 0.57 [0.57-0.59] and 1.64 [1.52-1.69], depression 0.68 [0.39-0.74] and 1.29 [0.78-1.38], composite of dementia 0.27 [0.26-0.28] and 3.52 [3.18-3.81] and death 0.24 [0.24-0.24] and 3.96 [3.91-4.00]. CONCLUSIONS Simple risk scores derived from routinely collected EHR accurately quantify the risk of developing common neurologic and psychiatric diseases. These scores can be computed automatically, prior to medical care visits, and may thus be useful for large-scale brain health screening.
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Affiliation(s)
- Marta Fernandes
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States.
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States.
| | - Zeina Chemali
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States.
| | - Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - Lidia M V R Moura
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - Akshata Sonni
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States.
| | - Alessandro Biffi
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States.
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Ge W, Rice HJ, Sheikh IS, Westover MB, Weathers AL, Jones LK, Moura L. Improving Neurology Clinical Care With Natural Language Processing Tools. Neurology 2023; 101:1010-1018. [PMID: 37816638 PMCID: PMC10727205 DOI: 10.1212/wnl.0000000000207853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/25/2023] [Indexed: 10/12/2023] Open
Abstract
The integration of natural language processing (NLP) tools into neurology workflows has the potential to significantly enhance clinical care. However, it is important to address the limitations and risks associated with integrating this new technology. Recent advances in transformer-based NLP algorithms (e.g., GPT, BERT) could augment neurology clinical care by summarizing patient health information, suggesting care options, and assisting research involving large datasets. However, these NLP platforms have potential risks including fabricated facts and data security and substantial barriers for implementation. Although these risks and barriers need to be considered, the benefits for providers, patients, and communities are substantial. With these systems achieving greater functionality and the pace of medical need increasing, integrating these tools into clinical care may prove not only beneficial but necessary. Further investigation is needed to design implementation strategies, mitigate risks, and overcome barriers.
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Affiliation(s)
- Wendong Ge
- From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA
| | - Hunter J Rice
- From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA
| | - Irfan S Sheikh
- From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA
| | - M Brandon Westover
- From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA
| | - Allison L Weathers
- From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA
| | - Lyell K Jones
- From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA
| | - Lidia Moura
- From the Department of Neurology (W.G., H.J.R., I.S.S., L.M.), Massachusetts General Hospital, Boston; Department of Neurology (M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Information Technology Division (A.L.W.), Cleveland Clinic, OH; Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN; and Department of Neurology (L.M.), Harvard Medical School, Boston, MA.
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18
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Fernandes M, Westover MB, Zafar SF. Identifying inpatient hospitalizations with continuous electroencephalogram monitoring from administrative data. BMC Health Serv Res 2023; 23:1234. [PMID: 37950245 PMCID: PMC10636942 DOI: 10.1186/s12913-023-10262-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Continuous electroencephalography (cEEG) is increasingly utilized in hospitalized patients to detect and treat seizures. Epidemiologic and observational studies using administrative datasets can provide insights into the comparative and cost effectiveness of cEEG utilization. Defining patient cohorts that underwent acute inpatient cEEG from administrative datasets is limited by the lack of validated codes differentiating elective epilepsy monitoring unit (EMU) admissions from acute inpatient hospitalization with cEEG utilization. Our aim was to develop hospital administrative data-based models to identify acute inpatient admissions with cEEG monitoring and distinguish them from EMU admissions. METHODS This was a single center retrospective cohort study of adult (≥ 18 years old) inpatient admissions with a cEEG procedure (EMU or acute inpatient) between January 2016-April 2022. The gold standard for acute inpatient cEEG vs. EMU was obtained from the local EEG recording platform. An extreme gradient boosting model was trained to classify admissions as acute inpatient cEEG vs. EMU using administrative data including demographics, diagnostic and procedure codes, and medications. RESULTS There were 9,523 patients in our cohort with 10,783 hospital admissions (8.5% EMU, 91.5% acute inpatient cEEG); with average age of 59 (SD 18.2) years; 46.2% were female. The model achieved an area under the receiver operating curve of 0.92 (95% CI [0.91-0.94]) and area under the precision-recall curve of 0.99 [0.98-0.99] for classification of acute inpatient cEEG. CONCLUSIONS Our model has the potential to identify cEEG monitoring admissions in larger cohorts and can serve as a tool to enable large-scale, administrative data-based studies of EEG utilization.
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Affiliation(s)
- Marta Fernandes
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA.
- Harvard Medical School, Boston, MA, USA.
| | - M Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
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19
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Amerineni R, Sun H, Fernandes MB, Brandon Westover M, Moura L, Patorno E, Hsu J, Zafar SF. Real-World Continuous EEG Utilization and Outcomes in Hospitalized Patients With Acute Cerebrovascular Diseases. J Clin Neurophysiol 2023:00004691-990000000-00111. [PMID: 37938032 PMCID: PMC11058112 DOI: 10.1097/wnp.0000000000001043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023] Open
Abstract
PURPOSE Continuous electroencephalography (cEEG) is recommended for hospitalized patients with cerebrovascular diseases and suspected seizures or unexplained neurologic decline. We sought to (1) identify areas of practice variation in cEEG utilization, (2) determine predictors of cEEG utilization, (3) evaluate whether cEEG utilization is associated with outcomes in patients with cerebrovascular diseases. METHODS This cohort study of the Premier Healthcare Database (2014-2020), included hospitalized patients age >18 years with cerebrovascular diseases (identified by ICD codes). Continuous electroencephalography was identified by International Classification of Diseases (ICD)/Current Procedural Terminology (CPT) codes. Multivariable lasso logistic regression was used to identify predictors of cEEG utilization and in-hospital mortality. Propensity score-matched analysis was performed to determine the relation between cEEG use and mortality. RESULTS 1,179,471 admissions were included; 16,777 (1.4%) underwent cEEG. Total number of cEEGs increased by 364% over 5 years (average 32%/year). On multivariable analysis, top five predictors of cEEG use included seizure diagnosis, hospitals with >500 beds, regions Northeast and South, and anesthetic use. Top predictors of mortality included use of mechanical ventilation, vasopressors, anesthetics, antiseizure medications, and age. Propensity analysis showed that cEEG was associated with lower in-hospital mortality (Average Treatment Effect -0.015 [95% confidence interval -0.028 to -0.003], Odds ratio 0.746 [95% confidence interval, 0.618-0.900]). CONCLUSIONS There has been a national increase in cEEG utilization for hospitalized patients with cerebrovascular diseases, with practice variation. cEEG utilization was associated with lower in-hospital mortality. Larger comparative studies of cEEG-guided treatments are indicated to inform best practices, guide policy changes for increased access, and create guidelines on triaging and transferring patients to centers with cEEG capability.
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Affiliation(s)
- Rajesh Amerineni
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Haoqi Sun
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Lidia Moura
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Elisabetta Patorno
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - John Hsu
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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20
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Palepu K, Sadeghi K, Kleinschmidt DF, Donoghue J, Chapman S, Arslan AR, Westover MB, Cash SS, Pathmanathan J. An examination of sleep spindle metrics in the Sleep Heart Health Study: superiority of automated spindle detection over total sigma power in assessing age-related spindle decline. BMC Neurol 2023; 23:359. [PMID: 37803266 PMCID: PMC10557170 DOI: 10.1186/s12883-023-03376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/08/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Sleep spindle activity is commonly estimated by measuring sigma power during stage 2 non-rapid eye movement (NREM2) sleep. However, spindles account for little of the total NREM2 interval and therefore sigma power over the entire interval may be misleading. This study compares derived spindle measures from direct automated spindle detection with those from gross power spectral analyses for the purposes of clinical trial design. METHODS We estimated spindle activity in a set of 8,440 overnight electroencephalogram (EEG) recordings from 5,793 patients from the Sleep Heart Health Study using both sigma power and direct automated spindle detection. Performance of the two methods was evaluated by determining the sample size required to detect decline in age-related spindle coherence with each method in a simulated clinical trial. RESULTS In a simulated clinical trial, sigma power required a sample size of 115 to achieve 95% power to identify age-related changes in sigma coherence, while automated spindle detection required a sample size of only 60. CONCLUSIONS Measurements of spindle activity utilizing automated spindle detection outperformed conventional sigma power analysis by a wide margin, suggesting that many studies would benefit from incorporation of automated spindle detection. These results further suggest that some previous studies which have failed to detect changes in sigma power or coherence may have failed simply because they were underpowered.
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Affiliation(s)
- Kalyan Palepu
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
| | - Kolia Sadeghi
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
| | - Dave F Kleinschmidt
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
| | - Jacob Donoghue
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
| | - Seth Chapman
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
| | - Alexander R Arslan
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
| | - M Brandon Westover
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
- Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Sydney S Cash
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA
- Clinical Data Animation Center (CDAC), Massachusetts General Hospital, 50 Staniford Street, Fruit St, Boston, MA, 02114, USA
| | - Jay Pathmanathan
- Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, Boston, MA, 02210, USA.
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Eckhardt CA, Sun H, Malik P, Quadri S, Santana Firme M, Jones DK, van Sleuwen M, Jain A, Fan Z, Jing J, Ge W, Danish HH, Jacobson CA, Rubin DB, Kimchi EY, Cash SS, Frigault MJ, Lee JW, Dietrich J, Westover MB. Automated detection of immune effector cell-associated neurotoxicity syndrome via quantitative EEG. Ann Clin Transl Neurol 2023; 10:1776-1789. [PMID: 37545104 PMCID: PMC10578889 DOI: 10.1002/acn3.51866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 07/22/2023] [Indexed: 08/08/2023] Open
Abstract
OBJECTIVE To develop an automated, physiologic metric of immune effector cell-associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor-T cell therapy. METHODS We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor-T cell therapy with a CD19 or B-cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell-associated neurotoxicity syndrome score. RESULTS The EEG immune effector cell-associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's R2 of 0.69 (95% CI of 0.59-0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R2 0.24, p = 0.008), minimum platelets (R2 -0.29, p = 0.001), and dexamethasone usage (R2 0.42, p < 0.0001). The score significantly correlated with duration of neurotoxicity (R2 0.31, p < 0.0001). INTERPRETATION The EEG immune effector cell-associated neurotoxicity syndrome score possesses high criterion, construct, and predictive validity, which substantiates its use as a physiologic method to detect the presence and severity of neurotoxicity among patients undergoing chimeric antigen receptor T-cell therapy.
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Namirembe GE, Baker S, Albanese M, Mueller A, Qu JZ, Mekonnen J, Wiredu K, Westover MB, Houle TT, Akeju O. Association Between Postoperative Delirium and Long-Term Subjective Cognitive Decline in Older Patients Undergoing Cardiac Surgery: A Secondary Analysis of the Minimizing Intensive Care Unit Neurological Dysfunction with Dexmedetomidine-Induced Sleep Trial. J Cardiothorac Vasc Anesth 2023; 37:1700-1706. [PMID: 37217424 PMCID: PMC10524446 DOI: 10.1053/j.jvca.2023.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/09/2023] [Accepted: 04/25/2023] [Indexed: 05/24/2023]
Abstract
OBJECTIVES This study aimed to evaluate whether a measure of subjective cognitive decline (SCD), the Patient-Reported Outcomes Measurement Information System (PROMIS) Applied Cognition-Abilities questionnaire, was associated with postoperative delirium. It was hypothesized that delirium during the surgical hospitalization would be associated with a decrease in subjective cognition up to 6 months after cardiac surgery. DESIGN This was a secondary analysis of data from the Minimizing Intensive Care Unit Neurological Dysfunction with Dexmedetomidine-induced Sleep randomized, placebo-controlled, parallel-arm superiority trial. SETTING Data from patients recruited between March 2017 and February 2022 at a tertiary medical center in Boston, Massachusetts were analyzed in February 2023. PARTICIPANTS Data from 337 patients aged 60 years or older who underwent cardiac surgery with cardiopulmonary bypass were included. INTERVENTIONS Patients were assessed preoperatively and postoperatively at 30, 90, and 180 days using the subjective PROMIS Applied Cognition-Abilities and telephonic Montreal Cognitive Assessment. MEASUREMENT AND MAIN RESULTS Postoperative delirium occurred within 3 days in 39 participants (11.6%). After adjusting for baseline function, participants who developed postoperative delirium self-reported worse cognitive function (mean difference [MD] -2.64 [95% CI -5.25, -0.04]; p = 0.047) up to 180 days after surgery, as compared with nondelirious patients. This finding was consistent with those obtained from objective t-MoCA assessments (MD -0.77 [95% CI -1.49, -0.04]; p = 0.04). CONCLUSIONS In this cohort of older patients undergoing cardiac surgery, in-hospital delirium was associated with SCD up to 180 days after surgery. This finding suggested that measures of SCD may enable population-level insights into the burden of cognitive decline associated with postoperative delirium.
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Affiliation(s)
- Grace E Namirembe
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Sarah Baker
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Marissa Albanese
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Ariel Mueller
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Jason Z Qu
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Jennifer Mekonnen
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Kwame Wiredu
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - M Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Timothy T Houle
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
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Ryan SL, Liu X, McKenna V, Ghanta M, Muniz C, Renwick R, Westover MB, Kimchi EY. Associations between early in-hospital medications and the development of delirium in patients with stroke. J Stroke Cerebrovasc Dis 2023; 32:107249. [PMID: 37536017 PMCID: PMC10529367 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107249] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/29/2023] [Accepted: 07/06/2023] [Indexed: 08/05/2023] Open
Abstract
OBJECTIVES Patients hospitalized with stroke develop delirium at higher rates than general hospitalized patients. While several medications are associated with existing delirium, it is unknown whether early medication exposures are associated with subsequent delirium in patients with stroke. Additionally, it is unknown whether delirium identification is associated with changes in the prescription of these medications. MATERIALS AND METHODS We conducted a retrospective cohort study of patients admitted to a comprehensive stroke center, who were assessed for delirium by trained nursing staff during clinical care. We analyzed exposures to multiple medication classes in the first 48 h of admission, and compared them between patients who developed delirium >48 hours after admission and those who never developed delirium. Statistical analysis was performed using univariate testing. Multivariable logistic regression was used further to evaluate the significance of univariately significant medications, while controlling for clinical confounders. RESULTS 1671 unique patients were included in the cohort, of whom 464 (27.8%) developed delirium >48 hours after admission. Delirium was associated with prior exposure to antipsychotics, sedatives, opiates, and antimicrobials. Antipsychotics, sedatives, and antimicrobials remained significantly associated with delirium even after accounting for several clinical covariates. Usage of these medications decreased in the 48 hours following delirium identification, except for atypical antipsychotics, whose use increased. Other medication classes such as steroids, benzodiazepines, and sleep aids were not initially associated with subsequent delirium, but prescription patterns still changed after delirium identification. CONCLUSIONS Early exposure to multiple medication classes is associated with the subsequent development of delirium in patients with stroke. Additionally, prescription patterns changed following delirium identification, suggesting that some of the associated medication classes may represent modifiable targets for future delirium prevention strategies, although future study is needed.
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Affiliation(s)
- Sophia L Ryan
- Department of Neurology, Massachusetts General Hospital, USA; Department of Neurology, Mount Sinai Medical Center, India
| | - Xiu Liu
- Department of Neurology, Massachusetts General Hospital, USA; Lawrence Center for Quality and Safety, Massachusetts General Hospital, USA
| | - Vanessa McKenna
- Department of Neurology, Massachusetts General Hospital, USA
| | - Manohar Ghanta
- Department of Neurology, Massachusetts General Hospital, USA
| | - Carlos Muniz
- Department of Neurology, Massachusetts General Hospital, USA; Department of Neurology, SUNY Upstate Medical University, USA
| | - Rachel Renwick
- Department of Neurology, Massachusetts General Hospital, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, USA; Department of Neurology, Beth Israel Deaconess Medical Center, USA
| | - Eyal Y Kimchi
- Department of Neurology, Massachusetts General Hospital, USA; Department of Neurology, Northwestern University, USA.
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Amorim E, Zheng WL, Jing J, Ghassemi MM, Lee JW, Wu O, Herman ST, Pang T, Sivaraju A, Gaspard N, Hirsch L, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest. Neurology 2023; 101:e940-e952. [PMID: 37414565 PMCID: PMC10501085 DOI: 10.1212/wnl.0000000000207537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 05/04/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest. METHODS Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months. RESULTS One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery. DISCUSSION Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.
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Affiliation(s)
- Edilberto Amorim
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands.
| | - Wei-Long Zheng
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jin Jing
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Mohammad M Ghassemi
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jong Woo Lee
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Ona Wu
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Susan T Herman
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Trudy Pang
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Adithya Sivaraju
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Nicolas Gaspard
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Lawrence Hirsch
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Barry J Ruijter
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Marleen C Tjepkema-Cloostermans
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jeannette Hofmeijer
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Michel J A M van Putten
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - M Brandon Westover
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database. medRxiv 2023:2023.08.28.23294672. [PMID: 37693458 PMCID: PMC10491275 DOI: 10.1101/2023.08.28.23294672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Objective To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. Design Multicenter cohort, partly prospective and partly retrospective. Setting Seven academic or teaching hospitals from the U.S. and Europe. Patients Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included. Interventions not applicable. Measurements and Main Results Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. Conclusions The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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Parikh H, Sun H, Amerineni R, Rosenthal ES, Volfovsky A, Rudin C, Westover MB, Zafar SF. How Many Patients Do You Need? Investigating Trial Designs for Anti-Seizure Treatment in Acute Brain Injury Patients. medRxiv 2023:2023.08.21.23294339. [PMID: 37662339 PMCID: PMC10473786 DOI: 10.1101/2023.08.21.23294339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Objectives Epileptiform activity (EA) worsens outcomes in patients with acute brain injuries (e.g., aneurysmal subarachnoid hemorrhage [aSAH]). Randomized trials (RCTs) assessing anti-seizure interventions are needed. Due to scant drug efficacy data and ethical reservations with placebo utilization, RCTs are lacking or hindered by design constraints. We used a pharmacological model-guided simulator to design and determine feasibility of RCTs evaluating EA treatment. Methods In a single-center cohort of adults (age >18) with aSAH and EA, we employed a mechanistic pharmacokinetic-pharmacodynamic framework to model treatment response using observational data. We subsequently simulated RCTs for levetiracetam and propofol, each with three treatment arms mirroring clinical practice and an additional placebo arm. Using our framework we simulated EA trajectories across treatment arms. We predicted discharge modified Rankin Scale as a function of baseline covariates, EA burden, and drug doses using a double machine learning model learned from observational data. Differences in outcomes across arms were used to estimate the required sample size. Results Sample sizes ranged from 500 for levetiracetam 7 mg/kg vs placebo, to >4000 for levetiracetam 15 vs. 7 mg/kg to achieve 80% power (5% type I error). For propofol 1mg/kg/hr vs. placebo 1200 participants were needed. Simulations comparing propofol at varying doses did not reach 80% power even at samples >1200. Interpretation Our simulations using drug efficacy show sample sizes are infeasible, even for potentially unethical placebo-control trials. We highlight the strength of simulations with observational data to inform the null hypotheses and assess feasibility of future trials of EA treatment.
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Affiliation(s)
| | - Haoqi Sun
- Beth Israel Deaconess Medical Center, Department of Neurology
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Barfuss JD, Nascimento FA, Duhaime E, Kapur S, Karakis I, Ng M, Herlopian A, Lam A, Maus D, Halford JJ, Cash S, Brandon Westover M, Jing J. On-demand EEG education through competition - A novel, app-based approach to learning to identify interictal epileptiform discharges. Clin Neurophysiol Pract 2023; 8:177-186. [PMID: 37681118 PMCID: PMC10480673 DOI: 10.1016/j.cnp.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/09/2023] Open
Abstract
Objective Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG. Methods We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings. Results Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice. Conclusions Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback. Significance This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.
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Affiliation(s)
- Jaden D. Barfuss
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fábio A. Nascimento
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | - Ioannis Karakis
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Marcus Ng
- Section of Neurology, Department of Internal Medicine, Health Sciences Centre, University of Manitoba, Winnipeg, MB, Canada
| | - Aline Herlopian
- Division of Epilepsy, Department of Neurology, Yale University, New Haven, CT, USA
| | - Alice Lam
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas Maus
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sydney Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Vlasac IM, Bormes GW, Do E, Benkhoukha SS, Diallo N, Fryou NL, Gioia S, Akeju O, Joseph C, Kuan A, Lapan J, Oluwadara D, Team TP, Rahman SA, Saxena R, Scheer FAJL, Westover MB, Winkelman JW, Woodson F, Lane JM. A Novel Home-Based Study of Circadian Rhythms: Design, Rationale, and Methods for the Circadia Study. Sleep 2023:zsad197. [PMID: 37555446 DOI: 10.1093/sleep/zsad197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Indexed: 08/10/2023] Open
Abstract
The Circadia Study (Circadia) is a novel "direct-to-participant" research study investigating the genetics of circadian rhythm disorders of advanced and delayed sleep phase and non-24 hour rhythms. The goals of the Circadia Study are twofold: (i) to create an easy-to-use toolkit for at-home circadian phase assessment for patients with circadian rhythm disorders through the use of novel in-home based surveys, tests, and collection kits; and (ii) create a richly phenotyped patient resource for genetic studies that will lead to new genetic loci associated with circadian rhythm disorders revealing possible loci of interest to target in the development of therapeutics for circadian rhythm disorders. Through these goals, we aim to broaden our understanding and elucidate the genetics of circadian rhythm disorders across a diverse patient population while increasing accessibility to circadian rhythm disorder diagnostics reducing health disparities through self-directed at-home dim light melatonin onset (DLMO) collections.
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Affiliation(s)
- Irma M Vlasac
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Gregory W Bormes
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth Do
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Selma S Benkhoukha
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Naby Diallo
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Noah L Fryou
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Siena Gioia
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Clarence Joseph
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anne Kuan
- Data Science Platform, Broad Institute, Cambridge, MA, USA
| | - Jennifer Lapan
- Data Science Platform, Broad Institute, Cambridge, MA, USA
| | | | | | - Shadab A Rahman
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank A J L Scheer
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Nutrition Obesity Research Center at Harvard (NORCH), Boston, MA, USA
- Division of Nutrition, Harvard Medical School, Boston, MA, USA
- The Mary Horrigan Connors Center for Women's Health and Gender Biology, Brigham and Women's Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - John W Winkelman
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Faraji Woodson
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jacqueline M Lane
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Wilcox DR, Rudmann EA, Ye E, Noori A, Magdamo C, Jain A, Alabsi H, Foy B, Triant VA, Robbins GK, Westover MB, Das S, Mukerji SS. Cognitive concerns are a risk factor for mortality in people with HIV and coronavirus disease 2019. AIDS 2023; 37:1565-1571. [PMID: 37195278 PMCID: PMC10355333 DOI: 10.1097/qad.0000000000003595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 05/03/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Data supporting dementia as a risk factor for coronavirus disease 2019 (COVID-19) mortality relied on ICD-10 codes, yet nearly 40% of individuals with probable dementia lack a formal diagnosis. Dementia coding is not well established for people with HIV (PWH), and its reliance may affect risk assessment. METHODS This retrospective cohort analysis of PWH with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR positivity includes comparisons to people without HIV (PWoH), matched by age, sex, race, and zipcode. Primary exposures were dementia diagnosis, by International Classification of Diseases (ICD)-10 codes, and cognitive concerns, defined as possible cognitive impairment up to 12 months before COVID-19 diagnosis after clinical review of notes from the electronic health record. Logistic regression models assessed the effect of dementia and cognitive concerns on odds of death [odds ratio (OR); 95% CI (95% confidence interval)]; models adjusted for VACS Index 2.0. RESULTS Sixty-four PWH were identified out of 14 129 patients with SARS-CoV-2 infection and matched to 463 PWoH. Compared with PWoH, PWH had a higher prevalence of dementia (15.6% vs. 6%, P = 0.01) and cognitive concerns (21.9% vs. 15.8%, P = 0.04). Death was more frequent in PWH ( P < 0.01). Adjusted for VACS Index 2.0, dementia [2.4 (1.0-5.8), P = 0.05] and cognitive concerns [2.4 (1.1-5.3), P = 0.03] were associated with increased odds of death. In PWH, the association between cognitive concern and death trended towards statistical significance [3.92 (0.81-20.19), P = 0.09]; there was no association with dementia. CONCLUSION Cognitive status assessments are important for care in COVID-19, especially among PWH. Larger studies should validate findings and determine long-term COVID-19 consequences in PWH with preexisting cognitive deficits.
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Affiliation(s)
- Douglas R. Wilcox
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Brigham and Women's Hospital
- Department of Neurology, Harvard Medical School
| | - Emily A. Rudmann
- Neuroimmunology and Neuro-Infectious Diseases Division, Department of Neurology, Massachusetts General Hospital, Boston
- Division of Infectious Diseases, Vaccine and Immunotherapy Center, Massachusetts General Hospital, Charlestown
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital
| | - Ayush Noori
- Department of Neurology, Massachusetts General Hospital
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital
| | - Haitham Alabsi
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Harvard Medical School
| | - Brody Foy
- Center for Systems Biology, Massachusetts General Hospital, and Department of Systems Biology, Harvard Medical School
| | - Virginia A. Triant
- Division of Infectious Diseases
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Harvard Medical School
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Harvard Medical School
| | - Shibani S. Mukerji
- Neuroimmunology and Neuro-Infectious Diseases Division, Department of Neurology, Massachusetts General Hospital, Boston
- Division of Infectious Diseases, Vaccine and Immunotherapy Center, Massachusetts General Hospital, Charlestown
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Parikh H, Hoffman K, Sun H, Zafar SF, Ge W, Jing J, Liu L, Sun J, Struck A, Volfovsky A, Rudin C, Westover MB. Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study. Lancet Digit Health 2023; 5:e495-e502. [PMID: 37295971 PMCID: PMC10528143 DOI: 10.1016/s2589-7500(23)00088-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/13/2023] [Accepted: 04/19/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Epileptiform activity is associated with worse patient outcomes, including increased risk of disability and death. However, the effect of epileptiform activity on neurological outcome is confounded by the feedback between treatment with antiseizure medications and epileptiform activity burden. We aimed to quantify the heterogeneous effects of epileptiform activity with an interpretability-centred approach. METHODS We did a retrospective, cross-sectional study of patients in the intensive care unit who were admitted to Massachusetts General Hospital (Boston, MA, USA). Participants were aged 18 years or older and had electrographic epileptiform activity identified by a clinical neurophysiologist or epileptologist. The outcome was the dichotomised modified Rankin Scale (mRS) at discharge and the exposure was epileptiform activity burden defined as mean or maximum proportion of time spent with epileptiform activity in 6 h windows in the first 24 h of electroencephalography. We estimated the change in discharge mRS if everyone in the dataset had experienced a specific epileptiform activity burden and were untreated. We combined pharmacological modelling with an interpretable matching method to account for confounding and epileptiform activity-antiseizure medication feedback. The quality of the matched groups was validated by the neurologists. FINDINGS Between Dec 1, 2011, and Oct 14, 2017, 1514 patients were admitted to Massachusetts General Hospital intensive care unit, 995 (66%) of whom were included in the analysis. Compared with patients with a maximum epileptiform activity of 0 to less than 25%, patients with a maximum epileptiform activity burden of 75% or more when untreated had a mean 22·27% (SD 0·92) increased chance of a poor outcome (severe disability or death). Moderate but long-lasting epileptiform activity (mean epileptiform activity burden 2% to <10%) increased the risk of a poor outcome by mean 13·52% (SD 1·93). The effect sizes were heterogeneous depending on preadmission profile-eg, patients with hypoxic-ischaemic encephalopathy or acquired brain injury were more adversely affected compared with patients without these conditions. INTERPRETATION Our results suggest that interventions should put a higher priority on patients with an average epileptiform activity burden 10% or greater, and treatment should be more conservative when maximum epileptiform activity burden is low. Treatment should also be tailored to individual preadmission profiles because the potential for epileptiform activity to cause harm depends on age, medical history, and reason for admission. FUNDING National Institutes of Health and National Science Foundation.
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Affiliation(s)
- Harsh Parikh
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Kentaro Hoffman
- Deptartment of Statistics and Operation Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Lin Liu
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Institute of Natural Sciences, MOELSC, School of Mathematical Sciences and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Jimeng Sun
- The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana-Champaign, IL, USA
| | - Aaron Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Cynthia Rudin
- Department of Computer Science, Duke University, Durham, NC, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
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Adra N, Dümmer LW, Paixao L, Tesh RA, Sun H, Ganglberger W, Westmeijer M, Da Silva Cardoso M, Kumar A, Ye E, Henry J, Cash SS, Kitchener E, Leveroni CL, Au R, Rosand J, Salinas J, Lam AD, Thomas RJ, Westover MB. Decoding information about cognitive health from the brainwaves of sleep. Sci Rep 2023; 13:11448. [PMID: 37454163 PMCID: PMC10349883 DOI: 10.1038/s41598-023-37128-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
Sleep electroencephalogram (EEG) signals likely encode brain health information that may identify individuals at high risk for age-related brain diseases. Here, we evaluate the correlation of a previously proposed brain age biomarker, the "brain age index" (BAI), with cognitive test scores and use machine learning to develop and validate a series of new sleep EEG-based indices, termed "sleep cognitive indices" (SCIs), that are directly optimized to correlate with specific cognitive scores. Three overarching cognitive processes were examined: total, fluid (a measure of cognitive processes involved in reasoning-based problem solving and susceptible to aging and neuropathology), and crystallized cognition (a measure of cognitive processes involved in applying acquired knowledge toward problem-solving). We show that SCI decoded information about total cognition (Pearson's r = 0.37) and fluid cognition (Pearson's r = 0.56), while BAI correlated only with crystallized cognition (Pearson's r = - 0.25). Overall, these sleep EEG-derived biomarkers may provide accessible and clinically meaningful indicators of neurocognitive health.
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Affiliation(s)
- Noor Adra
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Lisa W Dümmer
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- University of Groningen, Groningen, The Netherlands
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Sleep and Health Zurich, University of Zurich, Zurich, Switzerland
| | - Mike Westmeijer
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Utrecht University, Utrecht, The Netherlands
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Anagha Kumar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Jonathan Henry
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Erin Kitchener
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | | | - Rhoda Au
- Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Joel Salinas
- New York University Grossman School of Medicine, New York, NY, USA
| | - Alice D Lam
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Robert J Thomas
- Division of Pulmonary, Critical Care, and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA.
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA.
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA.
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Berger M, Ryu D, Reese M, McGuigan S, Evered LA, Price CC, Scott DA, Westover MB, Eckenhoff R, Bonanni L, Sweeney A, Babiloni C. A Real-Time Neurophysiologic Stress Test for the Aging Brain: Novel Perioperative and ICU Applications of EEG in Older Surgical Patients. Neurotherapeutics 2023; 20:975-1000. [PMID: 37436580 PMCID: PMC10457272 DOI: 10.1007/s13311-023-01401-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2023] [Indexed: 07/13/2023] Open
Abstract
As of 2022, individuals age 65 and older represent approximately 10% of the global population [1], and older adults make up more than one third of anesthesia and surgical cases in developed countries [2, 3]. With approximately > 234 million major surgical procedures performed annually worldwide [4], this suggests that > 70 million surgeries are performed on older adults across the globe each year. The most common postoperative complications seen in these older surgical patients are perioperative neurocognitive disorders including postoperative delirium, which are associated with an increased risk for mortality [5], greater economic burden [6, 7], and greater risk for developing long-term cognitive decline [8] such as Alzheimer's disease and/or related dementias (ADRD). Thus, anesthesia, surgery, and postoperative hospitalization have been viewed as a biological "stress test" for the aging brain, in which postoperative delirium indicates a failed stress test and consequent risk for later cognitive decline (see Fig. 3). Further, it has been hypothesized that interventions that prevent postoperative delirium might reduce the risk of long-term cognitive decline. Recent advances suggest that rather than waiting for the development of postoperative delirium to indicate whether a patient "passed" or "failed" this stress test, the status of the brain can be monitored in real-time via electroencephalography (EEG) in the perioperative period. Beyond the traditional intraoperative use of EEG monitoring for anesthetic titration, perioperative EEG may be a viable tool for identifying waveforms indicative of reduced brain integrity and potential risk for postoperative delirium and long-term cognitive decline. In principle, research incorporating routine perioperative EEG monitoring may provide insight into neuronal patterns of dysfunction associated with risk of postoperative delirium, long-term cognitive decline, or even specific types of aging-related neurodegenerative disease pathology. This research would accelerate our understanding of which waveforms or neuronal patterns necessitate diagnostic workup and intervention in the perioperative period, which could potentially reduce postoperative delirium and/or dementia risk. Thus, here we present recommendations for the use of perioperative EEG as a "predictor" of delirium and perioperative cognitive decline in older surgical patients.
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Affiliation(s)
- Miles Berger
- Department of Anesthesiology, Duke University Medical Center, Duke South Orange Zone Room 4315B, Box 3094, Durham, NC, 27710, USA.
- Duke Aging Center, Duke University Medical Center, Durham, NC, USA.
- Duke/UNC Alzheimer's Disease Research Center, Duke University Medical Center, Durham, NC, USA.
| | - David Ryu
- School of Medicine, Duke University, Durham, NC, USA
| | - Melody Reese
- Department of Anesthesiology, Duke University Medical Center, Duke South Orange Zone Room 4315B, Box 3094, Durham, NC, 27710, USA
- Duke Aging Center, Duke University Medical Center, Durham, NC, USA
| | - Steven McGuigan
- Department of Anaesthesia and Acute Pain Medicine, St Vincent's Hospital, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, Australia
| | - Lisbeth A Evered
- Department of Anaesthesia and Acute Pain Medicine, St Vincent's Hospital, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, Australia
- Weill Cornell Medicine, New York, NY, USA
| | - Catherine C Price
- Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - David A Scott
- Department of Anaesthesia and Acute Pain Medicine, St Vincent's Hospital, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, Australia
| | - M Brandon Westover
- Department of Neurology, Beth Israel Deaconess Hospital, Boston, MA, USA
| | - Roderic Eckenhoff
- Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Aoife Sweeney
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino, FR, Italy
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Sheikh Z, Selioutski O, Taraschenko O, Gilmore EJ, Westover MB, Cohen AB. Systematic Evaluation of Research Priorities in Critical Care Electroencephalography. J Clin Neurophysiol 2023; 40:426-433. [PMID: 35066530 PMCID: PMC9296700 DOI: 10.1097/wnp.0000000000000916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE The Critical Care EEG Monitoring Research Consortium (CCEMRC) is an international research group focusing on critical care EEG and epilepsy. As CCEMRC grew to include 50+ institutions over the past decade, members met to establish research priorities. METHODS The authors used an analytical hierarchy process-based research prioritization method, adapted from an approach previously applied to a Department of Defense health-related research program. Forty-six CCEMRC members identified and scored a set of eight clinical problems (CPs) and 15 research topic areas (RTAs) at an annual CCEMRC meeting. Members scored CPs on three criteria using a five-point ordinal scale: Incidence, Impact, and Gap Size; and RTAs on four additional criteria: Niche, Feasibility, Scientific Importance, and Medical Importance, each of which was assigned a weight. The first three RTA criteria were scored using a five-point scale, and CPs were mapped to RTAs using a four-point scale. The Medical Importance score was a weighted average of its mapping scores and the CP score. Finally, a Priority score was calculated for each RTA as a product of the four RTA criteria scores. RESULTS The CPs with the highest scores were "Altered mental status" and "Long-term neurologic disability after hospital discharge." The RTAs with the highest priority scores were "Development of risk prediction tools," "Multicenter observational studies," and "Outcome prediction." CONCLUSIONS Research prioritization helped CCEMRC evaluate its current research trajectory, identify high-priority near-term research pursuits, and create a roadmap for future research plans aligned with its mission. This approach may be helpful to other academic consortia and research programs.
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Affiliation(s)
- Zubeda Sheikh
- Department of Neurology, West Virginia University School of Medicine, Morgantown, West Virginia, U.S.A
| | - Olga Selioutski
- Epilepsy Division, Department of Neurology, University of Rochester School of Medicine and Dentistry, Rochester, New York, U.S.A
| | - Olga Taraschenko
- Comprehensive Epilepsy Center, Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, U.S.A
| | - Emily J Gilmore
- Division of Neurocritical Care, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A
- Harvard Medical School, Boston, Massachusetts, U.S.A
| | - Adam B Cohen
- The Johns Hopkins University Applied Physics Lab, National Health Mission Area, Laurel, Maryland, U.S.A.; and
- Department of Neurology, The Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
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Leone MJ, Dashti HS, Coughlin B, Tesh RA, Quadri SA, Bucklin AA, Adra N, Krishnamurthy PV, Ye EM, Hemmige A, Rajan S, Panneerselvam E, Higgins J, Ayub MA, Ganglberger W, Paixao L, Houle TT, Thompson BT, Johnson-Akeju O, Saxena R, Kimchi E, Cash SS, Thomas RJ, Westover MB. Sound and light levels in intensive care units in a large urban hospital in the United States. Chronobiol Int 2023; 40:759-768. [PMID: 37144470 PMCID: PMC10524721 DOI: 10.1080/07420528.2023.2207647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/18/2022] [Accepted: 04/21/2023] [Indexed: 05/06/2023]
Abstract
Intensive care units (ICUs) may disrupt sleep. Quantitative ICU studies of concurrent and continuous sound and light levels and timings remain sparse in part due to the lack of ICU equipment that monitors sound and light. Here, we describe sound and light levels across three adult ICUs in a large urban United States tertiary care hospital using a novel sensor. The novel sound and light sensor is composed of a Gravity Sound Level Meter for sound level measurements and an Adafruit TSL2561 digital luminosity sensor for light levels. Sound and light levels were continuously monitored in the room of 136 patients (mean age = 67.0 (8.7) years, 44.9% female) enrolled in the Investigation of Sleep in the Intensive Care Unit study (ICU-SLEEP; Clinicaltrials.gov: #NCT03355053), at the Massachusetts General Hospital. The hours of available sound and light data ranged from 24.0 to 72.2 hours. Average sound and light levels oscillated throughout the day and night. On average, the loudest hour was 17:00 and the quietest hour was 02:00. Average light levels were brightest at 09:00 and dimmest at 04:00. For all participants, average nightly sound levels exceeded the WHO guideline of < 35 decibels. Similarly, mean nightly light levels varied across participants (minimum: 1.00 lux, maximum: 577.05 lux). Sound and light events were more frequent between 08:00 and 20:00 than between 20:00 and 08:00 and were largely similar on weekdays and weekend days. Peaks in distinct alarm frequencies (Alarm 1) occurred at 01:00, 06:00, and at 20:00. Alarms at other frequencies (Alarm 2) were relatively consistent throughout the day and night, with a small peak at 20:00. In conclusion, we present a sound and light data collection method and results from a cohort of critically ill patients, demonstrating excess sound and light levels across multiple ICUs in a large tertiary care hospital in the United States. ClinicalTrials.gov, #NCT03355053. Registered 28 November 2017, https://clinicaltrials.gov/ct2/show/NCT03355053.
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Affiliation(s)
- Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Brain Data Science Platform, Broad Institute, Cambridge, Massachusetts, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Brian Coughlin
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Syed A Quadri
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Abigail A Bucklin
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Parimala Velpula Krishnamurthy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aashritha Hemmige
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Subapriya Rajan
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ezhil Panneerselvam
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jasmine Higgins
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Timothy T Houle
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - B Taylor Thompson
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Oluwaseun Johnson-Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Brain Data Science Platform, Broad Institute, Cambridge, Massachusetts, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Eyal Kimchi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Robert J Thomas
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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Coelho LMG, Blacker D, Hsu J, Newhouse JP, Westover MB, Zafar SF, Moura LMVR. Association of Early Seizure Prophylaxis With Posttraumatic Seizures and Mortality: A Meta-analysis With Evidence Quality Assessment. Neurol Clin Pract 2023; 13:e200145. [PMID: 37066107 PMCID: PMC10101717 DOI: 10.1212/cpj.0000000000200145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/11/2023] [Indexed: 04/18/2023]
Abstract
Purpose of the Review To evaluate the quality of evidence about the association of primary seizure prophylaxis with antiseizure medication (ASM) within 7 days postinjury and the 18- or 24-month epilepsy/late seizure risk or all-cause mortality in adults with new-onset traumatic brain injury (TBI), in addition to early seizure risk. Results Twenty-three studies met the inclusion criteria (7 randomized and 16 nonrandomized studies). We analyzed 9,202 patients, including 4,390 in the exposed group and 4,812 in the unexposed group (894 in placebo and 3,918 in no ASM groups). There was a moderate to serious bias risk based on our assessment. Within the limitations of existing studies, our data revealed a lower risk for early seizures in the ASM prophylaxis group compared with placebo or no ASM prophylaxis (risk ratio [RR] 0.43, 95% confidence interval [CI] 0.33-0.57, p < 0.00001, I 2 = 3%). We identified high-quality evidence in favor of acute, short-term primary ASM use to prevent early seizures. Early ASM prophylaxis was not associated with a substantial difference in the 18- or 24-month risk of epilepsy/late seizures (RR 1.01, 95% CI 0.61-1.68, p = 0.96, I 2 = 63%) or mortality (RR 1.16, 95% CI 0.89-1.51, p = 0.26, I 2 = 0%). There was no evidence of strong publication bias for each main outcome. The overall quality of evidence was low and moderate for post-TBI epilepsy risk and all-cause mortality, respectively. Summary Our data suggest that the evidence showing no association between early ASM use and 18- or 24-month epilepsy risk in adults with new-onset TBI was of low quality. The analysis indicated a moderate quality for the evidence showing no effect on all-cause mortality. Therefore, higher-quality evidence is needed as a supplement for stronger recommendations.
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Affiliation(s)
- Lilian Maria Godeiro Coelho
- Department of Neurology (LMGC, MBW, SFZ, LMVRM), Massachusetts General Hospital; Department of Neurology (MBW, SFZ, LMVRM), Harvard Medical School; Department of Epidemiology (DB), Harvard T.H. Chan School of Public Health; Department of Psychiatry (DB), Massachusetts General Hospital; Department of Psychiatry (DB), Harvard Medical School; Department of Health Care Policy (JH, JPN), Harvard Medical School; Mongan Institute (JH), Massachusetts General Hospital; Department of Medicine (JH), Harvard Medical School, Boston; National Bureau of Economic Research (JPN), Cambridge; Department of Health Policy and Management (JPN), Harvard T.H. Chan School of Public Health, Boston; and Harvard Kennedy School (JPN), Cambridge, MA
| | - Deborah Blacker
- Department of Neurology (LMGC, MBW, SFZ, LMVRM), Massachusetts General Hospital; Department of Neurology (MBW, SFZ, LMVRM), Harvard Medical School; Department of Epidemiology (DB), Harvard T.H. Chan School of Public Health; Department of Psychiatry (DB), Massachusetts General Hospital; Department of Psychiatry (DB), Harvard Medical School; Department of Health Care Policy (JH, JPN), Harvard Medical School; Mongan Institute (JH), Massachusetts General Hospital; Department of Medicine (JH), Harvard Medical School, Boston; National Bureau of Economic Research (JPN), Cambridge; Department of Health Policy and Management (JPN), Harvard T.H. Chan School of Public Health, Boston; and Harvard Kennedy School (JPN), Cambridge, MA
| | - John Hsu
- Department of Neurology (LMGC, MBW, SFZ, LMVRM), Massachusetts General Hospital; Department of Neurology (MBW, SFZ, LMVRM), Harvard Medical School; Department of Epidemiology (DB), Harvard T.H. Chan School of Public Health; Department of Psychiatry (DB), Massachusetts General Hospital; Department of Psychiatry (DB), Harvard Medical School; Department of Health Care Policy (JH, JPN), Harvard Medical School; Mongan Institute (JH), Massachusetts General Hospital; Department of Medicine (JH), Harvard Medical School, Boston; National Bureau of Economic Research (JPN), Cambridge; Department of Health Policy and Management (JPN), Harvard T.H. Chan School of Public Health, Boston; and Harvard Kennedy School (JPN), Cambridge, MA
| | - Joseph P Newhouse
- Department of Neurology (LMGC, MBW, SFZ, LMVRM), Massachusetts General Hospital; Department of Neurology (MBW, SFZ, LMVRM), Harvard Medical School; Department of Epidemiology (DB), Harvard T.H. Chan School of Public Health; Department of Psychiatry (DB), Massachusetts General Hospital; Department of Psychiatry (DB), Harvard Medical School; Department of Health Care Policy (JH, JPN), Harvard Medical School; Mongan Institute (JH), Massachusetts General Hospital; Department of Medicine (JH), Harvard Medical School, Boston; National Bureau of Economic Research (JPN), Cambridge; Department of Health Policy and Management (JPN), Harvard T.H. Chan School of Public Health, Boston; and Harvard Kennedy School (JPN), Cambridge, MA
| | - M Brandon Westover
- Department of Neurology (LMGC, MBW, SFZ, LMVRM), Massachusetts General Hospital; Department of Neurology (MBW, SFZ, LMVRM), Harvard Medical School; Department of Epidemiology (DB), Harvard T.H. Chan School of Public Health; Department of Psychiatry (DB), Massachusetts General Hospital; Department of Psychiatry (DB), Harvard Medical School; Department of Health Care Policy (JH, JPN), Harvard Medical School; Mongan Institute (JH), Massachusetts General Hospital; Department of Medicine (JH), Harvard Medical School, Boston; National Bureau of Economic Research (JPN), Cambridge; Department of Health Policy and Management (JPN), Harvard T.H. Chan School of Public Health, Boston; and Harvard Kennedy School (JPN), Cambridge, MA
| | - Sahar F Zafar
- Department of Neurology (LMGC, MBW, SFZ, LMVRM), Massachusetts General Hospital; Department of Neurology (MBW, SFZ, LMVRM), Harvard Medical School; Department of Epidemiology (DB), Harvard T.H. Chan School of Public Health; Department of Psychiatry (DB), Massachusetts General Hospital; Department of Psychiatry (DB), Harvard Medical School; Department of Health Care Policy (JH, JPN), Harvard Medical School; Mongan Institute (JH), Massachusetts General Hospital; Department of Medicine (JH), Harvard Medical School, Boston; National Bureau of Economic Research (JPN), Cambridge; Department of Health Policy and Management (JPN), Harvard T.H. Chan School of Public Health, Boston; and Harvard Kennedy School (JPN), Cambridge, MA
| | - Lidia M V R Moura
- Department of Neurology (LMGC, MBW, SFZ, LMVRM), Massachusetts General Hospital; Department of Neurology (MBW, SFZ, LMVRM), Harvard Medical School; Department of Epidemiology (DB), Harvard T.H. Chan School of Public Health; Department of Psychiatry (DB), Massachusetts General Hospital; Department of Psychiatry (DB), Harvard Medical School; Department of Health Care Policy (JH, JPN), Harvard Medical School; Mongan Institute (JH), Massachusetts General Hospital; Department of Medicine (JH), Harvard Medical School, Boston; National Bureau of Economic Research (JPN), Cambridge; Department of Health Policy and Management (JPN), Harvard T.H. Chan School of Public Health, Boston; and Harvard Kennedy School (JPN), Cambridge, MA
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Fernandes M, Cardall A, Jing J, Ge W, Moura LMVR, Jacobs C, McGraw C, Zafar SF, Westover MB. Identification of patients with epilepsy using automated electronic health records phenotyping. Epilepsia 2023; 64:1472-1481. [PMID: 36934317 PMCID: PMC10239346 DOI: 10.1111/epi.17589] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/20/2023]
Abstract
OBJECTIVE Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy. METHODS The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping. RESULTS Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set. SIGNIFICANCE A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.
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Affiliation(s)
- Marta Fernandes
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aidan Cardall
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lidia M. V. R. Moura
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Claire Jacobs
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher McGraw
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
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Boncompte G, Sun H, Elgueta MF, Benavides J, Carrasco M, Morales MI, Calderón N, Contreras V, Westover MB, Cortínez LI, Akeju O, Pedemonte JC. Intraoperative electroencephalographic marker of preoperative frailty: A prospective cohort study. J Clin Anesth 2023; 86:111069. [PMID: 36738630 PMCID: PMC10074446 DOI: 10.1016/j.jclinane.2023.111069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/25/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023]
Affiliation(s)
- Gonzalo Boncompte
- Neurodynamics of Cognition Laboratory, Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile; División de Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Haoqi Sun
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Boston, MA, USA; Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, USA
| | - María F Elgueta
- División de Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javiera Benavides
- División de Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Marcela Carrasco
- Sección de Geriatría, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - María I Morales
- Sección de Geriatría, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Natalia Calderón
- División de Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Victor Contreras
- División de Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile; Departamento del Adulto, Escuela de Enfermería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - M Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Boston, MA, USA; Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, USA
| | - Luis I Cortínez
- División de Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Boston, MA, USA
| | - Juan C Pedemonte
- División de Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile; Programa de Farmacología y Toxicología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
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38
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Bucklin AA, Ganglberger W, Quadri SA, Tesh RA, Adra N, Da Silva Cardoso M, Leone MJ, Krishnamurthy PV, Hemmige A, Rajan S, Panneerselvam E, Paixao L, Higgins J, Ayub MA, Shao YP, Ye EM, Coughlin B, Sun H, Cash SS, Thompson BT, Akeju O, Kuller D, Thomas RJ, Westover MB. High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study. Sleep Breath 2023; 27:1013-1026. [PMID: 35971023 PMCID: PMC9931933 DOI: 10.1007/s11325-022-02698-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/08/2022] [Accepted: 08/08/2022] [Indexed: 01/05/2023]
Abstract
PURPOSE Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals. METHODS Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments. RESULTS Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor. CONCLUSIONS Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO2 signals, while risk factors were insufficient to predict AHI severity.
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Affiliation(s)
- Abigail A Bucklin
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Sleep & Health Zurich, University of Zurich, Zurich, Switzerland
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Syed A Quadri
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Parimala Velpula Krishnamurthy
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Aashritha Hemmige
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Subapriya Rajan
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Ezhil Panneerselvam
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Jasmine Higgins
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | - Brian Coughlin
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
| | | | - Oluwaseun Akeju
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, MGH, Boston, MA, USA
| | | | - Robert J Thomas
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA.
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA.
- Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, USA.
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Rathmell CS, Akeju O, Inouye SK, Westover MB. Estimating the number of cases of dementia that might be prevented by preventing delirium. Br J Anaesth 2023; 130:e477-e478. [PMID: 37031027 PMCID: PMC10329187 DOI: 10.1016/j.bja.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/24/2023] [Accepted: 03/01/2023] [Indexed: 04/09/2023] Open
Affiliation(s)
- Cara S Rathmell
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Clinical Data Animation Center, Massachusetts General Hospital, Boston, MA, USA.
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Sharon K Inouye
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Clinical Data Animation Center, Massachusetts General Hospital, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
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Nasiri S, Ganglberger W, Sun H, Thomas RJ, Westover MB. Exploiting labels from multiple experts in automated sleep scoring. Sleep 2023; 46:zsad034. [PMID: 36795078 PMCID: PMC10171620 DOI: 10.1093/sleep/zsad034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Indexed: 02/17/2023] Open
Affiliation(s)
- Samaneh Nasiri
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Wolfgang Ganglberger
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Haoqi Sun
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert J Thomas
- Harvard Medical School, Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
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Fernandes M, Westover MB, Zafar SF. Identifying inpatient hospitalizations with continuous electroencephalogram monitoring from administrative data. Res Sq 2023:rs.3.rs-2882806. [PMID: 37214908 PMCID: PMC10197757 DOI: 10.21203/rs.3.rs-2882806/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Background Continuous electroencephalography (cEEG) is increasingly utilized in hospitalized patients to detect and treat seizures. Epidemiologic and observational studies using administrative datasets can provide insights into the comparative and cost effectiveness of cEEG utilization. Defining patient cohorts that underwent acute inpatient cEEG from administrative datasets is limited by the lack of validated codes differentiating elective epilepsy monitoring unit (EMU) admissions from acute inpatient hospitalization with cEEG utilization. Our aim was to develop hospital administrative data-based models to identify acute inpatient admissions with cEEG monitoring and distinguish them from EMU admissions. Methods This was a single center retrospective cohort study of adult (≥ 18 years old) inpatient admissions with a cEEG procedure (EMU or acute inpatient) between January 2016-April 2022. The gold standard for acute inpatient cEEG vs. EMU was obtained from the local EEG recording platform. An extreme gradient boosting model was trained to classify admissions as acute inpatient cEEG vs. EMU using administrative data including demographics, diagnostic and procedure codes, and medications. Results There were 9,523 patients in our cohort with 10,783 hospital admissions (8.5% EMU, 91.5% acute inpatient cEEG); with average age of 59 (SD 18.2) years; 46.2% were female. The model achieved an area under the receiver operating curve of 0.92 (95% CI [0.91-0.94]) and area under the precision-recall curve of 0.99 [0.98-0.99] for classification of acute inpatient cEEG. Conclusions Our model has the potential to identify cEEG monitoring admissions in larger cohorts and can serve as a tool to enable large-scale, administrative data-based studies of EEG utilization.
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Chen PM, Stekhoven SS, Haider A, Jing J, Ge W, Rosenthal ES, Westover MB, Zafar SF. Association of Epileptiform Activity With Outcomes in Toxic-Metabolic Encephalopathy. Crit Care Explor 2023; 5:e0913. [PMID: 37168691 PMCID: PMC10166342 DOI: 10.1097/cce.0000000000000913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
The clinical significance of epileptiform abnormalities (EAs) specific to toxic-metabolic encephalopathy (TME) is unknown. OBJECTIVES To quantify EA burden in patients with TME and its association with neurologic outcomes. DESIGN SETTING AND PARTICIPANT This is a retrospective study. A cohort of patients with TME and EA (positive) were age, Sequential Organ Failure Assessment Score, Acute Physiology and Chronic Health Evaluation II (APACHE-II) score matched to a cohort of TME patients without EA (control). Univariate analysis compared EA-positive patients against controls. Multivariable logistical regression adjusting for underlying disease etiology was performed to examine the relationship between EA burden and probability of poor neurologic outcome (modified Rankin Score [mRS] 4-6) at discharge. Consecutive admissions to inpatient floors or ICUs that underwent continuous electroencephalography (cEEG) monitoring at a single center between 2012 and 2019. Inclusion criteria were 1) patients with TME diagnosis, 2) age greater than 18 years, and 3) greater than or equal to 16 hours of cEEG. Patients with acute brain injury and cardiac arrest were excluded. MAIN OUTCOMES AND MEASURES Poor neurologic outcome defined by mRS (mRS 4-6). RESULTS One hundred sixteen patients were included, 58 with EA and 58 controls without EA, where matching was performed on age and APACHE-II score. The median age was 66 (Q1-Q3, 57-75) and median APACHE II score was 18 (Q1-Q3, 13-22). Overall cohort discharge mortality was 22% and 70% had a poor neurologic outcome. Peak EA burden was defined as the 12-hour window of recording with the highest prevalence of EAs. In multivariable analysis adjusted for Charlson Comorbidity Index and primary diagnosis, presence of EAs was associated with poor outcome (odds ratio 3.89; CI [1.05-14.2], p = 0.041). Increase in peak EA burden from 0% to 100% increased probability of poor discharge neurologic outcome by 30%. CONCLUSIONS AND RELEVANCE Increasing burden of EA is associated with worse discharge outcomes in patients with TME. Future studies are needed to determine whether short-term treatment with anti-seizure medications while medically treating the underlying metabolic derangement improves outcomes.
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Affiliation(s)
- Patrick M Chen
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Department of Neurology, University of California Irvine, Orange, CA
| | - Sophie Schuurmans Stekhoven
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Department of Internal Medicine, Spaarne Gasthuis, Hoofddorp/Haarlem, the Netherlands
| | - Adnan Haider
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Department of Internal Medicine, Schmidt College of Medicine, Florida Atlantic University Hospital, Boca Raton, FL
| | - Jin Jing
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Eric S Rosenthal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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Jing J, Ge W, Struck AF, Fernandes MB, Hong S, An S, Fatima S, Herlopian A, Karakis I, Halford JJ, Ng MC, Johnson EL, Appavu BL, Sarkis RA, Osman G, Kaplan PW, Dhakar MB, Jayagopal LA, Sheikh Z, Taraschenko O, Schmitt S, Haider HA, Kim JA, Swisher CB, Gaspard N, Cervenka MC, Rodriguez Ruiz AA, Lee JW, Tabaeizadeh M, Gilmore EJ, Nordstrom K, Yoo JY, Holmes MG, Herman ST, Williams JA, Pathmanathan J, Nascimento FA, Fan Z, Nasiri S, Shafi MM, Cash SS, Hoch DB, Cole AJ, Rosenthal ES, Zafar SF, Sun J, Westover MB. Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs. Neurology 2023; 100:e1737-e1749. [PMID: 36460472 PMCID: PMC10136018 DOI: 10.1212/wnl.0000000000201670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/25/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.
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Affiliation(s)
- Jin Jing
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Wendong Ge
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aaron F Struck
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marta Bento Fernandes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Shenda Hong
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sungtae An
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Safoora Fatima
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aline Herlopian
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ioannis Karakis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jonathan J Halford
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marcus C Ng
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily L Johnson
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Brian L Appavu
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Rani A Sarkis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Gamaleldin Osman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Peter W Kaplan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Monica B Dhakar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Lakshman Arcot Jayagopal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Zubeda Sheikh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Olga Taraschenko
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sarah Schmitt
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Hiba A Haider
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Kim
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Christa B Swisher
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Nicolas Gaspard
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mackenzie C Cervenka
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andres A Rodriguez Ruiz
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jong Woo Lee
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mohammad Tabaeizadeh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily J Gilmore
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Kristy Nordstrom
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ji Yeoun Yoo
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Manisha G Holmes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Susan T Herman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Williams
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jay Pathmanathan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Fábio A Nascimento
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ziwei Fan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Samaneh Nasiri
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mouhsin M Shafi
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sydney S Cash
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Daniel B Hoch
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andrew J Cole
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Eric S Rosenthal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sahar F Zafar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jimeng Sun
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - M Brandon Westover
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL.
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Jing J, Ge W, Hong S, Fernandes MB, Lin Z, Yang C, An S, Struck AF, Herlopian A, Karakis I, Halford JJ, Ng MC, Johnson EL, Appavu BL, Sarkis RA, Osman G, Kaplan PW, Dhakar MB, Arcot Jayagopal L, Sheikh Z, Taraschenko O, Schmitt S, Haider HA, Kim JA, Swisher CB, Gaspard N, Cervenka MC, Rodriguez Ruiz AA, Lee JW, Tabaeizadeh M, Gilmore EJ, Nordstrom K, Yoo JY, Holmes MG, Herman ST, Williams JA, Pathmanathan J, Nascimento FA, Fan Z, Nasiri S, Shafi MM, Cash SS, Hoch DB, Cole AJ, Rosenthal ES, Zafar SF, Sun J, Westover MB. Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation. Neurology 2023; 100:e1750-e1762. [PMID: 36878708 PMCID: PMC10136013 DOI: 10.1212/wnl.0000000000207127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/12/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.
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Affiliation(s)
- Jin Jing
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Wendong Ge
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Shenda Hong
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Marta Bento Fernandes
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Zhen Lin
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Chaoqi Yang
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sungtae An
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Aaron F Struck
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Aline Herlopian
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ioannis Karakis
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jonathan J Halford
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Marcus C Ng
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Emily L Johnson
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Brian L Appavu
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Rani A Sarkis
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Gamaleldin Osman
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Peter W Kaplan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Monica B Dhakar
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Lakshman Arcot Jayagopal
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Zubeda Sheikh
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Olga Taraschenko
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sarah Schmitt
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Hiba A Haider
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jennifer A Kim
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Christa B Swisher
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Nicolas Gaspard
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mackenzie C Cervenka
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Andres A Rodriguez Ruiz
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jong Woo Lee
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mohammad Tabaeizadeh
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Emily J Gilmore
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Kristy Nordstrom
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ji Yeoun Yoo
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Manisha G Holmes
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Susan T Herman
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jennifer A Williams
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jay Pathmanathan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Fábio A Nascimento
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ziwei Fan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Samaneh Nasiri
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mouhsin M Shafi
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sydney S Cash
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Daniel B Hoch
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Andrew J Cole
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Eric S Rosenthal
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sahar F Zafar
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jimeng Sun
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - M Brandon Westover
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA.
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Ong CS, Reinertsen E, Sun H, Moonsamy P, Mohan N, Funamoto M, Kaneko T, Shekar PS, Schena S, Lawton JS, D'Alessandro DA, Westover MB, Aguirre AD, Sundt TM. Prediction of operative mortality for patients undergoing cardiac surgical procedures without established risk scores. J Thorac Cardiovasc Surg 2023; 165:1449-1459.e15. [PMID: 34607725 PMCID: PMC8918430 DOI: 10.1016/j.jtcvs.2021.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 08/11/2021] [Accepted: 09/03/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Current cardiac surgery risk models do not address a substantial fraction of procedures. We sought to create models to predict the risk of operative mortality for an expanded set of cases. METHODS Four supervised machine learning models were trained using preoperative variables present in the Society of Thoracic Surgeons (STS) data set of the Massachusetts General Hospital to predict and classify operative mortality in procedures without STS risk scores. A total of 424 (5.5%) mortality events occurred out of 7745 cases. Models included logistic regression with elastic net regularization (LogReg), support vector machine, random forest (RF), and extreme gradient boosted trees (XGBoost). Model discrimination was assessed via area under the receiver operating characteristic curve (AUC), and calibration was assessed via calibration slope and expected-to-observed event ratio. External validation was performed using STS data sets from Brigham and Women's Hospital (BWH) and the Johns Hopkins Hospital (JHH). RESULTS Models performed comparably with the highest mean AUC of 0.83 (RF) and expected-to-observed event ratio of 1.00. On external validation, the AUC was 0.81 in BWH (RF) and 0.79 in JHH (LogReg/RF). Models trained and applied on the same institution's data achieved AUCs of 0.81 (BWH: LogReg/RF/XGBoost) and 0.82 (JHH: LogReg/RF/XGBoost). CONCLUSIONS Machine learning models trained on preoperative patient data can predict operative mortality at a high level of accuracy for cardiac surgical procedures without established risk scores. Such procedures comprise 23% of all cardiac surgical procedures nationwide. This work also highlights the value of using local institutional data to train new prediction models that account for institution-specific practices.
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Affiliation(s)
- Chin Siang Ong
- Division of Cardiac Surgery, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass
| | - Erik Reinertsen
- Division of Cardiology, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass; Center for Systems Biology, Massachusetts General Hospital, Boston, Mass; Research Laboratory for Electronics, Massachusetts Institute of Technology, Cambridge, Mass
| | - Haoqi Sun
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston, Mass
| | - Philicia Moonsamy
- Division of Cardiac Surgery, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass
| | - Navyatha Mohan
- Division of Cardiac Surgery, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass
| | - Masaki Funamoto
- Division of Cardiac Surgery, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass
| | - Tsuyoshi Kaneko
- Division of Cardiac Surgery, Brigham and Women's Hospital, Boston, Mass
| | - Prem S Shekar
- Division of Cardiac Surgery, Brigham and Women's Hospital, Boston, Mass
| | - Stefano Schena
- Division of Cardiac Surgery, Johns Hopkins Hospital, Baltimore, Md
| | | | - David A D'Alessandro
- Division of Cardiac Surgery, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass
| | - M Brandon Westover
- Division of Clinical Neurophysiology, Department of Neurology, Massachusetts General Hospital, Boston, Mass; Clinical Data AI Center, Massachusetts General Hospital, Boston, Mass
| | - Aaron D Aguirre
- Division of Cardiology, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass; Center for Systems Biology, Massachusetts General Hospital, Boston, Mass; Wellman Center for Photomedicine, Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Healthcare Transformation Lab, Massachusetts General Hospital, Boston, Mass.
| | - Thoralf M Sundt
- Division of Cardiac Surgery, Massachusetts General Hospital and Corrigan Minehan Heart Center, Boston, Mass
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Sun H, Ye E, Paixao L, Ganglberger W, Chu CJ, Zhang C, Rosand J, Mignot E, Cash SS, Gozal D, Thomas RJ, Westover MB. The sleep and wake electroencephalogram over the lifespan. Neurobiol Aging 2023; 124:60-70. [PMID: 36739622 PMCID: PMC9957961 DOI: 10.1016/j.neurobiolaging.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 12/29/2022] [Accepted: 01/11/2023] [Indexed: 01/20/2023]
Abstract
Both sleep and wake encephalograms (EEG) change over the lifespan. While prior studies have characterized age-related changes in the EEG, the datasets span a particular age group, or focused on sleep and wake macrostructure rather than the microstructure. Here, we present sex-stratified data from 3372 community-based or clinic-based otherwise neurologically and psychiatrically healthy participants ranging from 11 days to 80 years of age. We estimate age norms for key sleep and wake EEG parameters including absolute and relative powers in delta, theta, alpha, and sigma bands, as well as sleep spindle density, amplitude, duration, and frequency. To illustrate the potential use of the reference measures developed herein, we compare them to sleep EEG recordings from age-matched participants with Alzheimer's disease, severe sleep apnea, depression, osteoarthritis, and osteoporosis. Although the partially clinical nature of the datasets may bias the findings towards less normal and hence may underestimate pathology in practice, age-based EEG reference values enable objective screening of deviations from healthy aging among individuals with a variety of disorders that affect brain health.
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Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, MA, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Can Zhang
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, MA, USA
| | - Emmanuel Mignot
- Center for Sleep Sciences and Medicine, Stanford University, Stanford, CA USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - David Gozal
- Department of Child Health, University of Missouri, Columbia, MO, USA
| | - Robert J Thomas
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, MA, USA.
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McInnis RP, Ayub MA, Jing J, Halford JJ, Mateen FJ, Westover MB. Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography. Epilepsy Behav 2023; 141:109135. [PMID: 36871319 PMCID: PMC10082472 DOI: 10.1016/j.yebeh.2023.109135] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 08/10/2022] [Accepted: 02/02/2023] [Indexed: 03/07/2023]
Abstract
OBJECTIVE To construct a tool for non-experts to calculate the probability of epilepsy based on easily obtained clinical information combined with an artificial intelligence readout of the electroencephalogram (AI-EEG). MATERIALS AND METHODS We performed a chart review of 205 consecutive patients aged 18 years or older who underwent routine EEG. We created a point system to calculate the pre-EEG probability of epilepsy in a pilot study cohort. We also computed a post-test probability based on AI-EEG results. RESULTS One hundred and four (50.7%) patients were female, the mean age was 46 years, and 110 (53.7%) were diagnosed with epilepsy. Findings favoring epilepsy included developmental delay (12.6% vs 1.1%), prior neurological injury (51.4% vs 30.9%), childhood febrile seizures (4.6% vs 0.0%), postictal confusion (43.6% vs 20.0%), and witnessed convulsions (63.6% vs 21.1%); findings favoring alternative diagnoses were lightheadedness (3.6% vs 15.8%) or onset after prolonged sitting or standing (0.9% vs 7.4%). The final point system included 6 predictors: Presyncope (-3 points), cardiac history (-1), convulsion or forced head turn (+3), neurological disease history (+2), multiple prior spells (+1), postictal confusion (+2). Total scores of ≤1 point predicted <5% probability of epilepsy, while cumulative scores ≥7 predicted >95%. The model showed excellent discrimination (AUROC: 0.86). A positive AI-EEG substantially increases the probability of epilepsy. The impact is greatest when the pre-EEG probability is near 30%. SIGNIFICANCE A decision tool using a small number of historical clinical features accurately predicts the probability of epilepsy. In indeterminate cases, AI-assisted EEG helps resolve uncertainty. This tool holds promise for use by healthcare workers without specialty epilepsy training if validated in an independent cohort.
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Affiliation(s)
- Robert P. McInnis
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Department of Neurology, University of San Francisco, California, San Francisco, CA, United States
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Department of Neurology, Lousiana State University Health Sciences Center, Shreveport, LA, United States
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Jonathan J. Halford
- Department of Neurology, Medical University of South Carolina, Charleston, SC, United States
| | - Farrah J. Mateen
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
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Fernandes MB, Valizadeh N, Alabsi HS, Quadri SA, Tesh RA, Bucklin AA, Sun H, Jain A, Brenner LN, Ye E, Ge W, Collens SI, Lin S, Das S, Robbins GK, Zafar SF, Mukerji SS, Westover MB. Classification of neurologic outcomes from medical notes using natural language processing. Expert Syst Appl 2023; 214:119171. [PMID: 36865787 PMCID: PMC9974159 DOI: 10.1016/j.eswa.2022.119171] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely 'good recovery', 'moderate disability', 'severe disability', and 'death' and on the Modified Rankin Scale (mRS), with 7 classes, namely 'no symptoms', 'no significant disability', 'slight disability', 'moderate disability', 'moderately severe disability', 'severe disability', and 'death'. For 428 patients' notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93-0.95) and 0.77 (0.75-0.80) for GOS, and 0.90 (0.89-0.91) and 0.59 (0.57-0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.
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Affiliation(s)
- Marta B. Fernandes
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Navid Valizadeh
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Haitham S. Alabsi
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Syed A. Quadri
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Ryan A. Tesh
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Abigail A. Bucklin
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Laura N. Brenner
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, MGH, Boston, MA, United States
- Division of General Internal Medicine, MGH, Boston, MA, United States
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Sarah I. Collens
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
| | - Stacie Lin
- Harvard Medical School, Boston, MA, United States
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Gregory K. Robbins
- Harvard Medical School, Boston, MA, United States
- Division of Infectious Diseases, MGH, Boston, MA, United States
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Shibani S. Mukerji
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Vaccine and Immunotherapy Center, Division of Infectious Diseases, MGH, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
- McCance Center for Brain Health, MGH, Boston, MA, United States
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49
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Ye EM, Sun H, Krishnamurthy PV, Adra N, Ganglberger W, Thomas RJ, Lam AD, Westover MB. Dementia detection from brain activity during sleep. Sleep 2023; 46:zsac286. [PMID: 36448766 PMCID: PMC9995788 DOI: 10.1093/sleep/zsac286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/10/2022] [Indexed: 12/03/2022] Open
Abstract
STUDY OBJECTIVES Dementia is a growing cause of disability and loss of independence in the elderly, yet remains largely underdiagnosed. Early detection and classification of dementia can help close this diagnostic gap and improve management of disease progression. Altered oscillations in brain activity during sleep are an early feature of neurodegenerative diseases and be used to identify those on the verge of cognitive decline. METHODS Our observational cross-sectional study used a clinical dataset of 10 784 polysomnography from 8044 participants. Sleep macro- and micro-structural features were extracted from the electroencephalogram (EEG). Microstructural features were engineered from spectral band powers, EEG coherence, spindle, and slow oscillations. Participants were classified as dementia (DEM), mild cognitive impairment (MCI), or cognitively normal (CN) based on clinical diagnosis, Montreal Cognitive Assessment, Mini-Mental State Exam scores, clinical dementia rating, and prescribed medications. We trained logistic regression, support vector machine, and random forest models to classify patients into DEM, MCI, and CN groups. RESULTS For discriminating DEM versus CN, the best model achieved an area under receiver operating characteristic curve (AUROC) of 0.78 and area under precision-recall curve (AUPRC) of 0.22. For discriminating MCI versus CN, the best model achieved an AUROC of 0.73 and AUPRC of 0.18. For discriminating DEM or MCI versus CN, the best model achieved an AUROC of 0.76 and AUPRC of 0.32. CONCLUSIONS Our dementia classification algorithms show promise for incorporating dementia screening techniques using routine sleep EEG. The findings strengthen the concept of sleep as a window into neurodegenerative diseases.
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Affiliation(s)
- Elissa M Ye
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
| | - Parimala V Krishnamurthy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
| | - Robert J Thomas
- Division of Pulmonary, Critical Care and Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alice D Lam
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
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50
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Chen Y, Li S, Ge W, Jing J, Chen HY, Doherty D, Herman A, Kaleem S, Ding K, Osman G, Swisher CB, Smith C, Maciel CB, Alkhachroum A, Lee JW, Dhakar MB, Gilmore EJ, Sivaraju A, Hirsch LJ, Omay SB, Blumenfeld H, Sheth KN, Struck AF, Edlow BL, Westover MB, Kim JA. Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy. J Neurol Neurosurg Psychiatry 2023; 94:245-249. [PMID: 36241423 PMCID: PMC9931627 DOI: 10.1136/jnnp-2022-329542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1). METHODS We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. RESULTS In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). CONCLUSIONS Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.
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Affiliation(s)
- Yilun Chen
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Songlu Li
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Wendong Ge
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jin Jing
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hsin Yi Chen
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Daniel Doherty
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alison Herman
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Safa Kaleem
- Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Kan Ding
- Neurology, UT Southwestern Medical Center, Dallas, Texas, USA
| | | | - Christa B Swisher
- Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christine Smith
- Neurology, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Carolina B Maciel
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Neurology, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Ayham Alkhachroum
- Neurology, University of Miami Miller School of Medicine, Miami, Florida, USA
- Neurology, Jackson Memorial Hospital, Miami, Florida, USA
| | - Jong Woo Lee
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Monica B Dhakar
- Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Emily J Gilmore
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | - Sacit B Omay
- Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA
| | - Hal Blumenfeld
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kevin N Sheth
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Aaron F Struck
- Neurology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Neurology, William S Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Brian L Edlow
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Jennifer A Kim
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
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