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Glicksberg BS, Timsina P, Patel D, Sawant A, Vaid A, Raut G, Charney AW, Apakama D, Carr BG, Freeman R, Nadkarni GN, Klang E. Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room. J Am Med Inform Assoc 2024:ocae103. [PMID: 38771093 DOI: 10.1093/jamia/ocae103] [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: 11/29/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) and large language models (LLMs) can play a critical role in emergency room operations by augmenting decision-making about patient admission. However, there are no studies for LLMs using real-world data and scenarios, in comparison to and being informed by traditional supervised machine learning (ML) models. We evaluated the performance of GPT-4 for predicting patient admissions from emergency department (ED) visits. We compared performance to traditional ML models both naively and when informed by few-shot examples and/or numerical probabilities. METHODS We conducted a retrospective study using electronic health records across 7 NYC hospitals. We trained Bio-Clinical-BERT and XGBoost (XGB) models on unstructured and structured data, respectively, and created an ensemble model reflecting ML performance. We then assessed GPT-4 capabilities in many scenarios: through Zero-shot, Few-shot with and without retrieval-augmented generation (RAG), and with and without ML numerical probabilities. RESULTS The Ensemble ML model achieved an area under the receiver operating characteristic curve (AUC) of 0.88, an area under the precision-recall curve (AUPRC) of 0.72 and an accuracy of 82.9%. The naïve GPT-4's performance (0.79 AUC, 0.48 AUPRC, and 77.5% accuracy) showed substantial improvement when given limited, relevant data to learn from (ie, RAG) and underlying ML probabilities (0.87 AUC, 0.71 AUPRC, and 83.1% accuracy). Interestingly, RAG alone boosted performance to near peak levels (0.82 AUC, 0.56 AUPRC, and 81.3% accuracy). CONCLUSIONS The naïve LLM had limited performance but showed significant improvement in predicting ED admissions when supplemented with real-world examples to learn from, particularly through RAG, and/or numerical probabilities from traditional ML models. Its peak performance, although slightly lower than the pure ML model, is noteworthy given its potential for providing reasoning behind predictions. Further refinement of LLMs with real-world data is necessary for successful integration as decision-support tools in care settings.
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Affiliation(s)
- Benjamin S Glicksberg
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Prem Timsina
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Dhaval Patel
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Ashwin Sawant
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Akhil Vaid
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Ganesh Raut
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Alexander W Charney
- The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Donald Apakama
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Brendan G Carr
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Robert Freeman
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Eyal Klang
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
- The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
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2
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Gleason A, Richter F, Beller N, Arivazhagan N, Feng R, Holmes E, Glicksberg BS, Morton SU, Vega-Talbott ML, Fields M, Guttmann K, Nadkarni GN, Richter F. Accurate prediction of neurologic changes in critically ill infants using pose AI. medRxiv 2024:2024.04.17.24305953. [PMID: 38699362 PMCID: PMC11064996 DOI: 10.1101/2024.04.17.24305953] [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: 05/05/2024]
Abstract
Importance Infant alertness and neurologic changes are assessed by exam, which can be intermittent and subjective. Reliable, continuous methods are needed. Objective We hypothesized that our computer vision method to track movement, pose AI, could predict neurologic changes. Design Retrospective observational study from 2021-2022. Setting A level four urban neonatal intensive care unit (NICU). Participants Infants with corrected age ≤1 year, comprising 115 patients with 4,705 hours of video data linked to electroencephalograms (EEG), including 46% female and 25.2% white non-Hispanic. Exposures Pose AI prediction of anatomic landmark position and an XGBoost classifier trained on one-minute variance in pose. Main outcomes and measures Outcomes were cerebral dysfunction, diagnosed from EEG readings by an epileptologist, and sedation, defined by the administration of sedative medications. Measures of algorithm performance were receiver operating characteristic-area under the curves (ROC-AUCs) on cross-validation and on two test datasets comprised of held-out infants and held-out video frames from infants used in training. Results Infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants (respective ROC-AUCs 0.94, 0.83, 0.89). Median movement increased with age and, after accounting for age, was lower with sedative medications and in infants with cerebral dysfunction (all P<5×10-3, 10,000 permutations). Sedation prediction had high performance on cross-validation, held-out frames, and held-out infants (ROC-AUCs 0.90, 0.91, 0.87), as did prediction of cerebral dysfunction (ROC-AUCs 0.91, 0.90, 0.76). Conclusions and Relevance We used pose AI to predict sedation and cerebral dysfunction in 4,705 hours of video from a large, diverse cohort of infants. Pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.
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Affiliation(s)
- Alec Gleason
- Albert Einstein College of Medicine, New York, NY
| | | | - Nathalia Beller
- Department of Genetics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Naveen Arivazhagan
- Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rui Feng
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Emma Holmes
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Newborn Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Sarah U Morton
- Department of Pediatrics, Harvard Medical School, Boston, MA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA
| | - Maite La Vega-Talbott
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Madeline Fields
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Katherine Guttmann
- Division of Newborn Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N Nadkarni
- Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Felix Richter
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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3
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Artsi Y, Sorin V, Konen E, Glicksberg BS, Nadkarni G, Klang E. Large language models for generating medical examinations: systematic review. BMC Med Educ 2024; 24:354. [PMID: 38553693 PMCID: PMC10981304 DOI: 10.1186/s12909-024-05239-y] [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] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 02/28/2024] [Indexed: 04/01/2024]
Abstract
BACKGROUND Writing multiple choice questions (MCQs) for the purpose of medical exams is challenging. It requires extensive medical knowledge, time and effort from medical educators. This systematic review focuses on the application of large language models (LLMs) in generating medical MCQs. METHODS The authors searched for studies published up to November 2023. Search terms focused on LLMs generated MCQs for medical examinations. Non-English, out of year range and studies not focusing on AI generated multiple-choice questions were excluded. MEDLINE was used as a search database. Risk of bias was evaluated using a tailored QUADAS-2 tool. RESULTS Overall, eight studies published between April 2023 and October 2023 were included. Six studies used Chat-GPT 3.5, while two employed GPT 4. Five studies showed that LLMs can produce competent questions valid for medical exams. Three studies used LLMs to write medical questions but did not evaluate the validity of the questions. One study conducted a comparative analysis of different models. One other study compared LLM-generated questions with those written by humans. All studies presented faulty questions that were deemed inappropriate for medical exams. Some questions required additional modifications in order to qualify. CONCLUSIONS LLMs can be used to write MCQs for medical examinations. However, their limitations cannot be ignored. Further study in this field is essential and more conclusive evidence is needed. Until then, LLMs may serve as a supplementary tool for writing medical examinations. 2 studies were at high risk of bias. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
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Affiliation(s)
- Yaara Artsi
- Azrieli Faculty of Medicine, Bar-Ilan University, Ha'Hadas St. 1, Rishon Le Zion, Zefat, 7550598, Israel.
| | - Vera Sorin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel
- Tel-Aviv University School of Medicine, Tel Aviv, Israel
- DeepVision Lab, Chaim Sheba Medical Center, Ramat Gan, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel
- Tel-Aviv University School of Medicine, Tel Aviv, Israel
| | - Benjamin S Glicksberg
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Sorin V, Glicksberg BS, Artsi Y, Barash Y, Konen E, Nadkarni GN, Klang E. Utilizing large language models in breast cancer management: systematic review. J Cancer Res Clin Oncol 2024; 150:140. [PMID: 38504034 PMCID: PMC10950983 DOI: 10.1007/s00432-024-05678-6] [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: 12/30/2023] [Accepted: 03/01/2024] [Indexed: 03/21/2024]
Abstract
PURPOSE Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field. METHODS We searched MEDLINE for relevant studies published before December 22, 2023. Keywords included: "large language models", "LLM", "GPT", "ChatGPT", "OpenAI", and "breast". The risk bias was evaluated using the QUADAS-2 tool. RESULTS Six studies evaluating either ChatGPT-3.5 or GPT-4, met our inclusion criteria. They explored clinical notes analysis, guideline-based question-answering, and patient management recommendations. Accuracy varied between studies, ranging from 50 to 98%. Higher accuracy was seen in structured tasks like information retrieval. Half of the studies used real patient data, adding practical clinical value. Challenges included inconsistent accuracy, dependency on the way questions are posed (prompt-dependency), and in some cases, missing critical clinical information. CONCLUSION LLMs hold potential in breast cancer care, especially in textual information extraction and guideline-driven clinical question-answering. Yet, their inconsistent accuracy underscores the need for careful validation of these models, and the importance of ongoing supervision.
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Affiliation(s)
- Vera Sorin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel.
- DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel.
| | - Benjamin S Glicksberg
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yaara Artsi
- Azrieli Faculty of Medicine, Bar-Ilan University, Zefat, Israel
| | - Yiftach Barash
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel
- DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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5
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El Sherbini A, Rosenson RS, Al Rifai M, Virk HUH, Wang Z, Virani S, Glicksberg BS, Lavie CJ, Krittanawong C. Artificial intelligence in preventive cardiology. Prog Cardiovasc Dis 2024:S0033-0620(24)00038-0. [PMID: 38460897 DOI: 10.1016/j.pcad.2024.03.002] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 03/03/2024] [Indexed: 03/11/2024]
Abstract
Artificial intelligence (AI) is a field of study that strives to replicate aspects of human intelligence into machines. Preventive cardiology, a subspeciality of cardiovascular (CV) medicine, aims to target and mitigate known risk factors for CV disease (CVD). AI's integration into preventive cardiology may introduce novel treatment interventions and AI-centered clinician assistive tools to reduce the risk of CVD. AI's role in nutrition, weight loss, physical activity, sleep hygiene, blood pressure, dyslipidemia, smoking, alcohol, recreational drugs, and mental health has been investigated. AI has immense potential to be used for the screening, detection, and monitoring of the mentioned risk factors. However, the current literature must be supplemented with future clinical trials to evaluate the capabilities of AI interventions for preventive cardiology. This review discusses present examples, potentials, and limitations of AI's role for the primary and secondary prevention of CVD.
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Affiliation(s)
- Adham El Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
| | - Robert S Rosenson
- Cardiometabolics Unit, Mount Sinai Hospital, Mount Sinai Heart, NY, United States of America
| | - Mahmoud Al Rifai
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Hafeez Ul Hassan Virk
- Harrington Heart & Vascular Institute, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America
| | - Zhen Wang
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Salim Virani
- Section of Cardiology, The Aga Khan University, Texas Heart Institute, Baylor College of Medicine, Houston, TX, United States of America
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Carl J Lavie
- John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine, New Orleans, LA, USA
| | - Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health and NYU School of Medicine, New York, NY, United States of America.
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6
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Oh W, Jayaraman P, Tandon P, Chaddha US, Kovatch P, Charney AW, Glicksberg BS, Nadkarni GN. A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19. Artif Intell Med 2024; 148:102750. [PMID: 38325922 PMCID: PMC10864255 DOI: 10.1016/j.artmed.2023.102750] [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: 03/06/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024]
Abstract
Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.
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Affiliation(s)
- Wonsuk Oh
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Pushkala Jayaraman
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pranai Tandon
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Udit S Chaddha
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia Kovatch
- Department of Scientific Computing, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Character Biosciences, New York, NY, USA
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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7
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Zang C, Zhang H, Xu J, Zhang H, Fouladvand S, Havaldar S, Cheng F, Chen K, Chen Y, Glicksberg BS, Chen J, Bian J, Wang F. High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data. Nat Commun 2023; 14:8180. [PMID: 38081829 PMCID: PMC10713627 DOI: 10.1038/s41467-023-43929-1] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Sajjad Fouladvand
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kun Chen
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Chen
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA.
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8
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Tessler I, Gecel NA, Glicksberg BS, Shivatzki S, Shapira Y, Zimlichman E, Alon EE, Klang E, Wolfovitz A. A Five-Decade Text Mining Analysis of Cochlear Implant Research: Where We Started and Where We Are Heading. Medicina (Kaunas) 2023; 59:1891. [PMID: 38003940 PMCID: PMC10673015 DOI: 10.3390/medicina59111891] [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] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/09/2023] [Accepted: 10/19/2023] [Indexed: 11/26/2023]
Abstract
Background and Objectives: Since its invention in the 1970s, the cochlear implant (CI) has been substantially developed. We aimed to assess the trends in the published literature to characterize CI. Materials and Methods: We queried PubMed for all CI-related entries published during 1970-2022. The following data were extracted: year of publication, publishing journal, title, keywords, and abstract text. Search terms belonged to the patient's age group, etiology for hearing loss, indications for CI, and surgical methodological advancement. Annual trends of publications were plotted. The slopes of publication trends were calculated by fitting regression lines to the yearly number of publications. Results: Overall, 19,428 CIs articles were identified. Pediatric-related CI was the most dominant sub-population among the age groups, with the highest rate and slope during the years (slope 5.2 ± 0.3, p < 0.001), while elderly-related CIs had significantly fewer publications. Entries concerning hearing preservation showed the sharpest rise among the methods, from no entries in 1980 to 46 entries in 2021 (slope 1.7 ± 0.2, p < 0.001). Entries concerning robotic surgery emerged in 2000, with a sharp increase in recent years (slope 0.5 ± 0.1, p < 0.001). Drug-eluting electrodes and CI under local-anesthesia have been reported only in the past five years, with a gradual rise. Conclusions: Publications regarding CI among pediatrics outnumbered all other indications, supporting the rising, pivotal role of CI in the rehabilitation of children with sensorineural hearing loss. Hearing-preservation publications have recently rapidly risen, identified as the primary trend of the current era, followed by a sharp rise of robotic surgery that is evolving and could define the next revolution.
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Affiliation(s)
- Idit Tessler
- Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan 52621, Israel (S.S.); (Y.S.); (E.E.A.); (A.W.)
- ARC Innovation Center, Sheba Medical Center, Ramat Gan 52621, Israel; (E.Z.); (E.K.)
| | - Nir A. Gecel
- Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan 52621, Israel (S.S.); (Y.S.); (E.E.A.); (A.W.)
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Shaked Shivatzki
- Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan 52621, Israel (S.S.); (Y.S.); (E.E.A.); (A.W.)
| | - Yisgav Shapira
- Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan 52621, Israel (S.S.); (Y.S.); (E.E.A.); (A.W.)
| | - Eyal Zimlichman
- ARC Innovation Center, Sheba Medical Center, Ramat Gan 52621, Israel; (E.Z.); (E.K.)
| | - Eran E. Alon
- Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan 52621, Israel (S.S.); (Y.S.); (E.E.A.); (A.W.)
| | - Eyal Klang
- ARC Innovation Center, Sheba Medical Center, Ramat Gan 52621, Israel; (E.Z.); (E.K.)
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amit Wolfovitz
- Department of Otolaryngology and Head and Neck Surgery, Sheba Medical Center, Ramat Gan 52621, Israel (S.S.); (Y.S.); (E.E.A.); (A.W.)
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9
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Brin D, Sorin V, Vaid A, Soroush A, Glicksberg BS, Charney AW, Nadkarni G, Klang E. Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments. Sci Rep 2023; 13:16492. [PMID: 37779171 PMCID: PMC10543445 DOI: 10.1038/s41598-023-43436-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [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: 07/26/2023] [Accepted: 09/23/2023] [Indexed: 10/03/2023] Open
Abstract
The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models' consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT's 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine.
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Affiliation(s)
- Dana Brin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel.
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Vera Sorin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ali Soroush
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eyal Klang
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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10
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Sorin V, Soffer S, Glicksberg BS, Barash Y, Konen E, Klang E. Adversarial attacks in radiology - A systematic review. Eur J Radiol 2023; 167:111085. [PMID: 37699278 DOI: 10.1016/j.ejrad.2023.111085] [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: 05/12/2023] [Revised: 08/04/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023]
Abstract
PURPOSE The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to systematically review the literature on adversarial attacks in radiology. METHODS We searched for studies on adversarial attacks in radiology published up to April 2023, using MEDLINE and Google Scholar databases. RESULTS A total of 22 studies published between March 2018 and April 2023 were included, primarily focused on image classification algorithms. Fourteen studies evaluated white-box attacks, three assessed black-box attacks and five investigated both. Eleven of the 22 studies targeted chest X-ray classification algorithms, while others involved chest CT (6/22), brain MRI (4/22), mammography (2/22), abdominal CT (1/22), hepatic US (1/22), and thyroid US (1/22). Some attacks proved highly effective, reducing the AUC of algorithm performance to 0 and achieving success rates up to 100 %. CONCLUSIONS Adversarial attacks are a growing concern. Although currently the threats are more theoretical than practical, they still represent a potential risk. It is important to be alert to such attacks, reinforce cybersecurity measures, and influence the formulation of ethical and legal guidelines. This will ensure the safe use of deep learning technology in medicine.
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Affiliation(s)
- Vera Sorin
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Shelly Soffer
- Internal Medicine B, Assuta Medical Center, Ashdod, Israel; Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Department of Genetics and Genomic Sciences, New York, NY, USA; Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiftach Barash
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel; Sami Sagol AI Hub, ARC, Sheba Medical Center, Ramat-Gan, Israel
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11
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Yeung AWK, Torkamani A, Butte AJ, Glicksberg BS, Schuller B, Rodriguez B, Ting DSW, Bates D, Schaden E, Peng H, Willschke H, van der Laak J, Car J, Rahimi K, Celi LA, Banach M, Kletecka-Pulker M, Kimberger O, Eils R, Islam SMS, Wong ST, Wong TY, Gao W, Brunak S, Atanasov AG. The promise of digital healthcare technologies. Front Public Health 2023; 11:1196596. [PMID: 37822534 PMCID: PMC10562722 DOI: 10.3389/fpubh.2023.1196596] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 09/04/2023] [Indexed: 10/13/2023] Open
Abstract
Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, University of Hong Kong, Hong Kong, China
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Ali Torkamani
- Department of Integrative Structural and Computational Biology, Scripps Research Translational Institute, La Jolla, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Benjamin S. Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Björn Schuller
- Department of Computing, Imperial College London, London, United Kingdom
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Daniel S. W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - David Bates
- Department of General Internal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Eva Schaden
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Harald Willschke
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Josip Car
- Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
- Centre for Population Health Sciences, LKC Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kazem Rahimi
- Deep Medicine Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), Lodz, Poland
- Department of Cardiology and Adult Congenital Heart Diseases, Polish Mother’s Memorial Hospital Research Institute (PMMHRI), Lodz, Poland
| | - Maria Kletecka-Pulker
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute for Ethics and Law in Medicine, University of Vienna, Vienna, Austria
| | - Oliver Kimberger
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University of Vienna, Vienna, Austria
| | - Roland Eils
- Digital Health Center, Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | | | - Stephen T. Wong
- Department of Systems Medicine and Bioengineering, Houston Methodist Cancer Center, T. T. and W. F. Chao Center for BRAIN, Houston Methodist Academic Institute, Houston Methodist Hospital, Houston, TX, United States
- Departments of Radiology, Pathology and Laboratory Medicine and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
| | - Tien Yin Wong
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Atanas G. Atanasov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland
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12
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He Z, Zhang R, Diallo G, Huang Z, Glicksberg BS. Editorial: Explainable artificial intelligence for critical healthcare applications. Front Artif Intell 2023; 6:1282800. [PMID: 37771610 PMCID: PMC10523392 DOI: 10.3389/frai.2023.1282800] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/30/2023] Open
Affiliation(s)
- Zhe He
- School of Information, Florida State University, Tallahassee, FL, United States
| | - Rui Zhang
- Division of Computational Health Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Gayo Diallo
- Team AHeaD, Bordeaux Population Health INSERM U1219, University of Bordeaux, Bordeaux, France
| | - Zhengxing Huang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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13
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Paranjpe I, Wang X, Anandakrishnan N, Haydak JC, Van Vleck T, DeFronzo S, Li Z, Mendoza A, Liu R, Fu J, Forrest I, Zhou W, Lee K, O'Hagan R, Dellepiane S, Menon KM, Gulamali F, Kamat S, Gusella GL, Charney AW, Hofer I, Cho JH, Do R, Glicksberg BS, He JC, Nadkarni GN, Azeloglu EU. Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the Rho pathway. medRxiv 2023:2023.09.06.23295120. [PMID: 37732187 PMCID: PMC10508814 DOI: 10.1101/2023.09.06.23295120] [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: 09/22/2023]
Abstract
Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two clusters with differential prevalence of end-stage kidney disease. Exome-wide associations identify a novel variant in ARHGEF18, a Rho guanine exchange factor specifically expressed in glomeruli. Overexpression of ARHGEF18 in human podocytes leads to impairments in focal adhesion architecture, cytoskeletal dynamics, cellular motility, and RhoA/Rac1 activation. Mutant GEF18 is resistant to ubiquitin mediated degradation leading to pathologically increased protein levels. Our findings uncover the first known disease-causing genetic variant that affects protein stability of a cytoskeletal regulator through impaired degradation, a potentially novel class of expression quantitative trait loci that can be therapeutically targeted.
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14
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Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13:2429. [PMID: 37510174 PMCID: PMC10378231 DOI: 10.3390/diagnostics13142429] [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: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
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Affiliation(s)
- Jonathan A Tangsrivimol
- Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Ethan Schonfeld
- Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Anand Veeravagu
- Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
| | - Roger Härtl
- Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
| | - Michael T Lawton
- Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
| | - Adham H El-Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel M Prevedello
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chayakrit Krittanawong
- Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
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15
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Paranjpe I, Jayaraman P, Su CY, Zhou S, Chen S, Thompson R, Del Valle DM, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Gonzalez-Kozlova E, Kauffman J, Kumar A, Paranjpe M, Hagan RO, Kamat S, Gulamali FF, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin MA, He JC, Suarez-Farinas M, Coca SG, Chan L, Azeloglu EU, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-Schulze S, Richards B, Glicksberg BS, Charney AW, Nadkarni GN. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection. Commun Med (Lond) 2023; 3:81. [PMID: 37308534 PMCID: PMC10258469 DOI: 10.1038/s43856-023-00307-8] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/18/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.
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Affiliation(s)
- Ishan Paranjpe
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Pushkala Jayaraman
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chen-Yang Su
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Computer Science, Quantitative Life Sciences, McGill University, Montreal, QC, Canada
| | - Sirui Zhou
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Steven Chen
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan Thompson
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Diane Marie Del Valle
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ephraim Kenigsberg
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shan Zhao
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suraj Jaladanki
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kumardeep Chaudhary
- Clinical Informatics, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India
| | - Steven Ascolillo
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Gonzalez-Kozlova
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justin Kauffman
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arvind Kumar
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manish Paranjpe
- Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Ross O Hagan
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Kamat
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faris F Gulamali
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hui Xie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joceyln Harris
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manishkumar Patel
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kimberly Argueta
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Craig Batchelor
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sergio Dellepiane
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leisha Scott
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew A Levin
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John Cijiang He
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mayte Suarez-Farinas
- Department of Biostatistics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven G Coca
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lili Chan
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evren U Azeloglu
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Beckmann
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miram Merad
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Computer Science, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Department of Twin Research, King's College London, London, GB, UK
| | | | - Alexander W Charney
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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16
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Menon KM, Das S, Shervey M, Johnson M, Glicksberg BS, Levin MA. Automated electrocardiogram signal quality assessment based on Fourier analysis and template matching. J Clin Monit Comput 2023; 37:829-837. [PMID: 36464761 PMCID: PMC9734499 DOI: 10.1007/s10877-022-00948-5] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 11/10/2022] [Indexed: 12/12/2022]
Abstract
We developed and tested a novel template matching approach for signal quality assessment on electrocardiogram (ECG) data. A computational method was developed that uses a sinusoidal approximation to the QRS complex to generate a correlation value at every point of an ECG. The strength of this correlation can be numerically adapted into a 'score' for each segment of an ECG, which can be used to stratify signal quality. The algorithm was tested on lead II ECGs of intensive care unit (ICU) patients admitted to the Mount Sinai Hospital (MSH) from January to July 2020 and on records from the MIT BIH arrhythmia database. The algorithm was found to be 98.9% specific and 99% sensitive on test data from the MSH ICU patients. The routine performs in linear O(n) time and occupies O(1) heap space in runtime. This approach can be used to lower the burden of pre-processing in ECG signal analysis. Given its runtime (O(n)) and memory (O(1)) complexity, there are potential applications for signal quality stratification and arrhythmia detection in wearable devices or smartphones.
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Affiliation(s)
- Kartikeya M Menon
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Subrat Das
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mark Shervey
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew Johnson
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew A Levin
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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17
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Krittanawong C, Omar AMS, Narula S, Sengupta PP, Glicksberg BS, Narula J, Argulian E. Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review. Life (Basel) 2023; 13:life13041029. [PMID: 37109558 PMCID: PMC10145844 DOI: 10.3390/life13041029] [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: 02/17/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.
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Affiliation(s)
- Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health, NYU School of Medicine, New York, NY 10016, USA
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
| | - Alaa Mabrouk Salem Omar
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Sukrit Narula
- Department of Medicine, Yale School of Medicine, New Haven, CT 06512, USA
| | - Partho P Sengupta
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
| | - Edgar Argulian
- Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY 10029, USA
- Division of Cardiovascular Medicine, Icahn School of Medicine at Mount Sinai Morningside, Mount Sinai Heart, New York, NY 10029, USA
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18
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Ryu E, Jenkins GD, Wang Y, Olfson M, Talati A, Lepow L, Coombes BJ, Charney AW, Glicksberg BS, Mann JJ, Weissman MM, Wickramaratne P, Pathak J, Biernacka JM. The importance of social activity to risk of major depression in older adults. Psychol Med 2023; 53:2634-2642. [PMID: 34763736 PMCID: PMC9095757 DOI: 10.1017/s0033291721004566] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [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: 06/14/2021] [Revised: 10/04/2021] [Accepted: 10/20/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults. METHODS We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis. RESULTS Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]. CONCLUSION Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.
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Affiliation(s)
- Euijung Ryu
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Gregory D. Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Yanshan Wang
- Department of AI and Informatics, Mayo Clinic, Rochester, USA
| | - Mark Olfson
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Ardesheer Talati
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Lauren Lepow
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Brandon J. Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Alexander W. Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Benjamin S. Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
| | - J. John Mann
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Myrna M. Weissman
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | - Priya Wickramaratne
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, USA
| | | | - Joanna M. Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, USA
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19
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Sanders LM, Scott RT, Yang JH, Qutub AA, Garcia Martin H, Berrios DC, Hastings JJA, Rask J, Mackintosh G, Hoarfrost AL, Chalk S, Kalantari J, Khezeli K, Antonsen EL, Babdor J, Barker R, Baranzini SE, Beheshti A, Delgado-Aparicio GM, Glicksberg BS, Greene CS, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Mason CE, Matar M, Mias GI, Miller J, Myers JG, Nelson C, Oribello J, Park SM, Parsons-Wingerter P, Prabhu RK, Reynolds RJ, Saravia-Butler A, Saria S, Sawyer A, Singh NK, Snyder M, Soboczenski F, Soman K, Theriot CA, Van Valen D, Venkateswaran K, Warren L, Worthey L, Zitnik M, Costes SV. Biological research and self-driving labs in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00618-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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20
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Scott RT, Sanders LM, Antonsen EL, Hastings JJA, Park SM, Mackintosh G, Reynolds RJ, Hoarfrost AL, Sawyer A, Greene CS, Glicksberg BS, Theriot CA, Berrios DC, Miller J, Babdor J, Barker R, Baranzini SE, Beheshti A, Chalk S, Delgado-Aparicio GM, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Kalantari J, Khezeli K, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Garcia Martin H, Mason CE, Matar M, Mias GI, Myers JG, Nelson C, Oribello J, Parsons-Wingerter P, Prabhu RK, Qutub AA, Rask J, Saravia-Butler A, Saria S, Singh NK, Snyder M, Soboczenski F, Soman K, Van Valen D, Venkateswaran K, Warren L, Worthey L, Yang JH, Zitnik M, Costes SV. Biomonitoring and precision health in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00617-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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21
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Vaid A, Argulian E, Lerakis S, Beaulieu-Jones BK, Krittanawong C, Klang E, Lampert J, Reddy VY, Narula J, Nadkarni GN, Glicksberg BS. Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction. Commun Med (Lond) 2023; 3:24. [PMID: 36788316 PMCID: PMC9929085 DOI: 10.1038/s43856-023-00240-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [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: 01/05/2022] [Accepted: 01/09/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making. METHODS In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment. RESULTS We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures. CONCLUSIONS Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Section of Biomedical Data Science, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Eyal Klang
- Sheba Medical Center, Department of Diagnostic Imaging, Tel Hashomer, Israel
- Sackler Medical School, Tel Aviv University, Tel Aviv, 52621, Israel
| | - Joshua Lampert
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine (D3M), The Department of Medicine, Icahn School of Medicine at Mount Siniai, New York, NY, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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22
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Thompson RC, Simons NW, Wilkins L, Cheng E, Del Valle DM, Hoffman GE, Cervia C, Fennessy B, Mouskas K, Francoeur NJ, Johnson JS, Lepow L, Le Berichel J, Chang C, Beckmann AG, Wang YC, Nie K, Zaki N, Tuballes K, Barcessat V, Cedillo MA, Yuan D, Huckins L, Roussos P, Marron TU, Glicksberg BS, Nadkarni G, Heath JR, Gonzalez-Kozlova E, Boyman O, Kim-Schulze S, Sebra R, Merad M, Gnjatic S, Schadt EE, Charney AW, Beckmann ND. Molecular states during acute COVID-19 reveal distinct etiologies of long-term sequelae. Nat Med 2023; 29:236-246. [PMID: 36482101 PMCID: PMC9873574 DOI: 10.1038/s41591-022-02107-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/25/2022] [Indexed: 12/13/2022]
Abstract
Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are debilitating, clinically heterogeneous and of unknown molecular etiology. A transcriptome-wide investigation was performed in 165 acutely infected hospitalized individuals who were followed clinically into the post-acute period. Distinct gene expression signatures of post-acute sequelae were already present in whole blood during acute infection, with innate and adaptive immune cells implicated in different symptoms. Two clusters of sequelae exhibited divergent plasma-cell-associated gene expression patterns. In one cluster, sequelae associated with higher expression of immunoglobulin-related genes in an anti-spike antibody titer-dependent manner. In the other, sequelae associated independently of these titers with lower expression of immunoglobulin-related genes, indicating lower non-specific antibody production in individuals with these sequelae. This relationship between lower total immunoglobulins and sequelae was validated in an external cohort. Altogether, multiple etiologies of post-acute sequelae were already detectable during SARS-CoV-2 infection, directly linking these sequelae with the acute host response to the virus and providing early insights into their development.
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Affiliation(s)
- Ryan C Thompson
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | - Esther Cheng
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Diane Marie Del Valle
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Department of Genetics and Genomic Sciences, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carlo Cervia
- Department of Immunology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Brian Fennessy
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Konstantinos Mouskas
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Susan and Leonard Feinstein Inflammatory Bowel Disease Clinical Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nancy J Francoeur
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Lauren Lepow
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica Le Berichel
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christie Chang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Ying-Chih Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicholas Zaki
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kevin Tuballes
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vanessa Barcessat
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mario A Cedillo
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dan Yuan
- Institute for Systems Biology, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Laura Huckins
- Department of Genetics and Genomic Sciences, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Thomas U Marron
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James R Heath
- Institute for Systems Biology, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Edgar Gonzalez-Kozlova
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Onur Boyman
- Department of Immunology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Seunghee Kim-Schulze
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Sema4, a Mount Sinai venture, Stamford, CT, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miriam Merad
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Sema4, a Mount Sinai venture, Stamford, CT, USA
| | - Alexander W Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Noam D Beckmann
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Medicine, Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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23
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Dellepiane S, Paranjpe I, Rajagopal M, Kamat S, O’Hagan R, Gulamali F, Rein JL, Charney AW, Do R, Coca S, Glicksberg BS, Nadkarni GN. Cannabis Use and CKD: Epidemiological Associations and Mendelian Randomization. Kidney Med 2022; 5:100582. [PMID: 36712313 PMCID: PMC9879977 DOI: 10.1016/j.xkme.2022.100582] [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] [Indexed: 12/14/2022] Open
Abstract
Rationale & Objective The association between cannabis use and chronic kidney disease (CKD) is controversial. We aimed to assess association of CKD with cannabis use in a large cohort study and then assess causality using Mendelian randomization with a genome-wide association study (GWAS). Study Design Retrospective cohort study and genome-wide association study. Setting & Participants The retrospective study was conducted on the All of Us cohort (N=223,354). Genetic instruments for cannabis use disorder were identified from 3 GWAS: the Psychiatric Genomics Consortium Substance Use Disorders, iPSYCH, and deCODE (N=384,032). Association between genetic instruments and CKD was investigated in the CKDGen GWAS (N > 1.2 million). Exposure Cannabis consumption. Outcomes CKD outcomes included: cystatin-C and creatinine-based kidney function, proteinuria, and blood urea nitrogen. Analytical Approach We conducted association analyses to test for frequency of cannabis use and CKD. To evaluate causality, we performed a 2-sample Mendelian randomization. Results In the retrospective study, compared to former users, less than monthly (OR, 1.01; 95% CI, 0.87-1.18; P = 0.87) and monthly cannabis users (OR, 1.15; 95% CI, 0.86-1.52; P = 0.33) did not have higher CKD odds. Conversely, weekly (OR, 1.28; 95% CI, 1.01-1.60; P = 0.04) and daily use (OR, 1.25; 95% CI, 1.04-1.50; P = 0.02) was significantly associated with CKD, adjusted for multiple confounders. In Mendelian randomization, genetic liability to cannabis use disorder was not associated with increased odds for CKD (OR, 1.00; 95% CI, 0.99-1.01; P = 0.96). These results were robust across different Mendelian randomization techniques and multiple kidney traits. Limitations Likely underreporting of cannabis use. In Mendelian randomization, genetic instruments were identified in the GWAS that included individuals primarily of European ancestry. Conclusions Despite the epidemiological association between cannabis use and CKD, there was no evidence of a causal effect, indicating confounding in observational studies.
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Affiliation(s)
- Sergio Dellepiane
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY,Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ishan Paranjpe
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Madhumitha Rajagopal
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY
| | - Samir Kamat
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ross O’Hagan
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Faris Gulamali
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Joshua L. Rein
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexander W. Charney
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ron Do
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY,Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Steven Coca
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY,Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Benjamin S. Glicksberg
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY,Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY,Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY,Department of Medicine, Division of Data Driven and Precision Medicine, Icahn School of Medicine at Mount Sinai, New York, NY,Address for Correspondence: Girish N Nadkarni, MD, Mount Sinai Clinical Intelligence Institute, Division of Data Driven and Digital Medicine (D3M), The Barbara T Murphy Division of Nephrology and Hypertension, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
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24
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Liu K, Witteveen-Lane M, Glicksberg BS, Kulkarni O, Shankar R, Chekalin E, Paithankar S, Yang J, Chesla D, Chen B. BGLM: big data-guided LOINC mapping with multi-language support. JAMIA Open 2022; 5:ooac099. [PMID: 36448022 PMCID: PMC9696745 DOI: 10.1093/jamiaopen/ooac099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 12/15/2021] [Revised: 06/08/2022] [Accepted: 11/13/2022] [Indexed: 09/27/2023] Open
Abstract
MOTIVATION Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support. MATERIALS AND METHODS We introduce a simple, yet effective tool called big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping. Distinguishing from existing methods, BGLM conducts mapping based on distributional similarity. RESULTS We validated the performance of BGLM with real-world datasets and showed that high mapping precision could be achieved under proper false discovery rate control. In addition, we showed that the mapping results of BGLM could be used to boost the performance of Regenstrief LOINC Mapping Assistant (RELMA), one of the most widely used LOINC code mappers. CONCLUSIONS BGLM paves a new way for LOINC code mapping and therefore could be applied to EHR systems without the restriction of languages. BGLM is freely available at https://github.com/Bin-Chen-Lab/BGLM.
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Affiliation(s)
- Ke Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | | | - Benjamin S Glicksberg
- Icahn School of Medicine at Mount Sinai, The Hasso Plattner Institute for Digital Health at Mount Sinai, New York City, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Omkar Kulkarni
- Office of Research, Spectrum Health, Grand Rapids, Michigan, USA
| | - Rama Shankar
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Evgeny Chekalin
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Shreya Paithankar
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Jeanne Yang
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan, USA
| | - Dave Chesla
- Office of Research, Spectrum Health, Grand Rapids, Michigan, USA
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Bin Chen
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
- Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
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25
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Bicak M, Perez‐Garcia GE, Buros J, Haure‐Mirande J, Perez GE, Otero‐Pagan A, Gama‐Sosa ME, DeGasperi RE, Sano M, Gage FH, Barlow C, Dudley J, Glicksberg BS, Wang Y, Readhead B, Ehrlich ME, Elder GA, Gandy S. BCI‐838, an orally active mGluR2/3 antagonist pro‐drug, mimics the beneficial effects of physical exercise on neurogenesis, behavior and exercise‐related molecular pathways in an Alzheimer’s disease mouse model. Alzheimers Dement 2022. [DOI: 10.1002/alz.062751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Mesude Bicak
- Icahn School of Medicine at Mount Sinai New York NY USA
| | - Georgina E. Perez‐Garcia
- Icahn School of Medicine at Mount Sinai New York NY USA
- James J. Peters Veterans Affairs Medical Center Bronx NY USA
| | - Jacqueline Buros
- Icahn School of Medicine at Mount Sinai New York NY USA
- Generable Inc New York NY USA
| | | | - Gissel E. Perez
- James J. Peters Veterans Affairs Medical Center Bronx NY USA
| | | | | | - Rita E. DeGasperi
- Icahn School of Medicine at Mount Sinai New York NY USA
- James J. Peters Veterans Affairs Medical Center Bronx NY USA
| | - Mary Sano
- Icahn School of Medicine at Mount Sinai New York NY USA
- James J. Peters Veterans Affairs Medical Center Bronx NY USA
| | - Fred H. Gage
- BrainCells Inc. La Jolla CA USA
- Salk Institute for Biological Studies La Jolla CA USA
| | - Carrolee Barlow
- BrainCells Inc. La Jolla CA USA
- Escape Bio, South San Fransisco CA USA
| | - Joel Dudley
- Icahn School of Medicine at Mount Sinai New York NY USA
| | | | | | | | | | - Gregory A. Elder
- Icahn School of Medicine at Mount Sinai New York NY USA
- James J. Peters Veterans Affairs Medical Center Bronx NY USA
| | - Sam Gandy
- Icahn School of Medicine at Mount Sinai New York NY USA
- James J. Peters Veterans Affairs Medical Center Bronx NY USA
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26
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Zhao G, Newbury P, Ishi Y, Chekalin E, Zeng B, Glicksberg BS, Wen A, Paithankar S, Sasaki T, Suri A, Nazarian J, Pacold ME, Brat DJ, Nicolaides T, Chen B, Hashizume R. Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma. Acta Neuropathol Commun 2022; 10:150. [PMID: 36274161 PMCID: PMC9590174 DOI: 10.1186/s40478-022-01463-z] [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] [Received: 03/19/2022] [Accepted: 10/13/2022] [Indexed: 11/25/2022] Open
Abstract
Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.
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Affiliation(s)
- Guisheng Zhao
- grid.137628.90000 0004 1936 8753Department of Pediatrics, New York University Langone Health, 160 East 32nd St., New York, NY 10016 USA
| | - Patrick Newbury
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Yukitomo Ishi
- grid.16753.360000 0001 2299 3507Department of Pediatrics, Northwestern University Feinberg School of Medicine, 303 East Superior St., Simpson Querrey 4-514, Chicago, IL 60611 USA ,grid.413808.60000 0004 0388 2248Division of Hematology, Oncology, Neuro-Oncology & Stem Cell Transplantation, Ann & Robert H. Lurie Children’s Hospital of Chicago, 225 East Chicago Avenue, Box 205, Chicago, IL 60611 USA
| | - Eugene Chekalin
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Billy Zeng
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Benjamin S. Glicksberg
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029 USA ,grid.416167.30000 0004 0442 1996Icahn School of Medicine at Mount Sinai, Hasso Plattner Institute for Digital Health at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029 USA
| | - Anita Wen
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Shreya Paithankar
- grid.17088.360000 0001 2150 1785Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI 49503 USA
| | - Takahiro Sasaki
- grid.16753.360000 0001 2299 3507Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, 303 East Superior St., Chicago, IL 60611 USA ,grid.412857.d0000 0004 1763 1087Department of Neurological Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama, Japan
| | - Amreena Suri
- grid.16753.360000 0001 2299 3507Department of Pediatrics, Northwestern University Feinberg School of Medicine, 303 East Superior St., Simpson Querrey 4-514, Chicago, IL 60611 USA ,grid.413808.60000 0004 0388 2248Division of Hematology, Oncology, Neuro-Oncology & Stem Cell Transplantation, Ann & Robert H. Lurie Children’s Hospital of Chicago, 225 East Chicago Avenue, Box 205, Chicago, IL 60611 USA
| | - Javad Nazarian
- grid.239560.b0000 0004 0482 1586Children’s National Medical Center, 111 Michigan Avenue NW, Washington, DC 20010 USA ,grid.412341.10000 0001 0726 4330University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland
| | - Michael E. Pacold
- grid.137628.90000 0004 1936 8753Department of Radiation Oncology, New York University Langone Health, 550 First Avenue, New York, NY 10016 USA
| | - Daniel J. Brat
- grid.16753.360000 0001 2299 3507Department of Pathology, Robert H. Lurie Cancer Center, Northwestern University Feinberg School of Medicine, 303 E. Chicago Ave., Chicago, IL 60611 USA
| | - Theodore Nicolaides
- grid.137628.90000 0004 1936 8753Department of Pediatrics, New York University Langone Health, 160 East 32nd St., New York, NY 10016 USA
| | - Bin Chen
- Department of Pediatrics and Human Development, Michigan State University, Secchia Center, Room 732, 15 Michigan St. NE, Grand Rapids, MI, 49503, USA. .,Department of Pharmacology and Toxicology, Michigan State University, 1355 Bogue St, East Lansing, MI, 48824, USA. .,Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, MI, 48824, USA.
| | - Rintaro Hashizume
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, 303 East Superior St., Simpson Querrey 4-514, Chicago, IL, 60611, USA. .,Division of Hematology, Oncology, Neuro-Oncology & Stem Cell Transplantation, Ann & Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Avenue, Box 205, Chicago, IL, 60611, USA. .,Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, 303 East Superior St., Chicago, IL, 60611, USA.
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27
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Lee J, Wanyan T, Chen Q, Keenan TDL, Glicksberg BS, Chew EY, Lu Z, Wang F, Peng Y. Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning. Mach Learn Med Imaging 2022; 13583:11-20. [PMID: 36656604 PMCID: PMC9842432 DOI: 10.1007/978-3-031-21014-3_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] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Accurately predicting a patient's risk of progressing to late age-related macular degeneration (AMD) is difficult but crucial for personalized medicine. While existing risk prediction models for progression to late AMD are useful for triaging patients, none utilizes longitudinal color fundus photographs (CFPs) in a patient's history to estimate the risk of late AMD in a given subsequent time interval. In this work, we seek to evaluate how deep neural networks capture the sequential information in longitudinal CFPs and improve the prediction of 2-year and 5-year risk of progression to late AMD. Specifically, we proposed two deep learning models, CNN-LSTM and CNN-Transformer, which use a Long-Short Term Memory (LSTM) and a Transformer, respectively with convolutional neural networks (CNN), to capture the sequential information in longitudinal CFPs. We evaluated our models in comparison to baselines on the Age-Related Eye Disease Study, one of the largest longitudinal AMD cohorts with CFPs. The proposed models outperformed the baseline models that utilized only single-visit CFPs to predict the risk of late AMD (0.879 vs 0.868 in AUC for 2-year prediction, and 0.879 vs 0.862 for 5-year prediction). Further experiments showed that utilizing longitudinal CFPs over a longer time period was helpful for deep learning models to predict the risk of late AMD. We made the source code available at https://github.com/bionlplab/AMD_prognosis_mlmi2022 to catalyze future works that seek to develop deep learning models for late AMD prediction.
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Affiliation(s)
- Junghwan Lee
- Columbia University, New York, USA,Weill Cornell Medicine, New York, USA
| | - Tingyi Wanyan
- Indiana University, Bloomington, USA,Ichan School of Medicine at Mount Sinai, New York, USA,Weill Cornell Medicine, New York, USA
| | - Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | | | | | - Emily Y. Chew
- National Eye Institute, National Institutes of Health, Bethesda, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, USA
| | - Fei Wang
- Weill Cornell Medicine, New York, USA
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28
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Paranjpe I, Jayaraman P, Su CY, Zhou S, Chen S, Thompson R, Del Valle DM, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Kumar A, Kozlova E, Paranjpe M, O’Hagan R, Kamat S, Gulamali FF, Kauffman J, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin MA, He JC, Suarez-Farinas M, Coca SG, Chan L, Azeloglu EU, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-Schulze S, Richards B, Glicksberg BS, Charney AW, Nadkarni GN. Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection. medRxiv 2022:2021.12.09.21267548. [PMID: 36093350 PMCID: PMC9460972 DOI: 10.1101/2021.12.09.21267548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.
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Affiliation(s)
- Ishan Paranjpe
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Stanford University, San Francisco, California, United States of America
| | - Pushkala Jayaraman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Chen-Yang Su
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Computer Science, McGill University, Montréal, Québec, Canada
| | - Sirui Zhou
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
| | - Steven Chen
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan Thompson
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Diane Marie Del Valle
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ephraim Kenigsberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shan Zhao
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Suraj Jaladanki
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kumardeep Chaudhary
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven Ascolillo
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arvind Kumar
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Kozlova
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manish Paranjpe
- Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Ross O’Hagan
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Kamat
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faris F. Gulamali
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justin Kauffman
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Medicine, Stanford University, San Francisco, California, United States of America
| | - Hui Xie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joceyln Harris
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manishkumar Patel
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kimberly Argueta
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Craig Batchelor
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sergio Dellepiane
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Leisha Scott
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew A Levin
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - John Cijiang He
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Mayte Suarez-Farinas
- Department of Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Steven G Coca
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Lili Chan
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Evren U Azeloglu
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Beckmann
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miram Merad
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Twin Research, King’s College London, London, United Kingdom
| | - Benjamin S Glicksberg
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Girish N Nadkarni
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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Honarvar H, Agarwal C, Somani S, Vaid A, Lampert J, Wanyan T, Reddy VY, Nadkarni GN, Miotto R, Zitnik M, Wang F, Glicksberg BS. Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation. Cardiovascular Digital Health Journal 2022; 3:220-231. [PMID: 36310683 PMCID: PMC9596304 DOI: 10.1016/j.cvdhj.2022.07.074] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural networks (CNNs) and traditionally use the full duration of raw ECG waveforms that create redundancies in feature learning and result in inaccurate predictions with large uncertainties. Objective For enhancing these predictions, we introduced a sub-waveform representation that leverages the rhythmic pattern of ECG waveforms (data-centric approach) rather than changing the CNN architecture (model-centric approach). Results We applied the proposed representation to a population with 92,446 patients to identify left ventricular dysfunction. We found that the sub-waveform representation increases the performance metrics compared to the full-waveform representation. We observed a 2% increase for area under the receiver operating characteristic curve and 10% increase for area under the precision-recall curve. We also carefully examined three reliability components of explainability, interpretability, and fairness. We provided an explanation for enhancements obtained by heartbeat alignment mechanism. By developing a new scoring system, we interpreted the clinical relevance of ECG features and showed that sub-waveform representation further pushes the scores towards clinical predictions. Finally, we showed that the new representation significantly reduces prediction uncertainties within subgroups that contributes to individual fairness. Conclusion We expect that this added control over the granularity of ECG data will improve the DL modeling for new artificial intelligence technologies in the cardiovascular space.
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Affiliation(s)
- Hossein Honarvar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Chirag Agarwal
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Akhil Vaid
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joshua Lampert
- Helmsley Center for Cardiac Electrophysiology, Mount Sinai Hospital, New York, New York
| | - Tingyi Wanyan
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana
| | - Vivek Y. Reddy
- Helmsley Center for Cardiac Electrophysiology, Mount Sinai Hospital, New York, New York
| | - Girish N. Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Address reprint requests and correspondence: Dr Benjamin S. Glicksberg, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029.
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Vaid A, Jiang JJ, Sawant A, Singh K, Kovatch P, Charney AW, Charytan DM, Divers J, Glicksberg BS, Chan L, Nadkarni GN. Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis. Clin J Am Soc Nephrol 2022; 17:1017-1025. [PMID: 35667835 PMCID: PMC9269621 DOI: 10.2215/cjn.16481221] [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: 12/20/2021] [Accepted: 04/27/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND AND OBJECTIVES Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: (1) trained from scratch in patients on hemodialysis, (2) pretrained on a publicly available set of natural images (ImageNet), (3) pretrained on all patients not on hemodialysis, and (4) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve. RESULTS We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n=18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% (n=461), 41%-50% (n=398), and >50% (n=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59). CONCLUSION A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period. PODCAST This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.
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Affiliation(s)
- Akhil Vaid
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joy J Jiang
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ashwin Sawant
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Karandeep Singh
- Department of Learning Health Systems, University of Michigan Medical School, Ann Arbor, Michigan
| | - Patricia Kovatch
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander W Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - David M Charytan
- Division of Nephrology, Department of Medicine, New York University Langone Medical Center and New York University Grossman School of Medicine, New York, New York
| | - Jasmin Divers
- Division of Health Services, Department of Medicine, New York University Langone Medical Center, New York, New York
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lili Chan
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York .,The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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Soffer S, Glicksberg BS, Zimlichman E, Klang E. BERT for the Processing of Radiological Reports: An Attention-based Natural Language Processing Algorithm. Acad Radiol 2022; 29:634-635. [PMID: 34362663 DOI: 10.1016/j.acra.2021.03.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 03/29/2021] [Indexed: 11/19/2022]
Affiliation(s)
- Shelly Soffer
- Internal Medicine B, Assuta Medical Center, Ashdod, Israel, and Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Eyal Zimlichman
- Hospital Management, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; Sheba Talpiot Medical Leadership Program, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Klang
- Sheba Talpiot Medical Leadership Program, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
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Soffer S, Zimlichman E, Levin MA, Zebrowski AM, Glicksberg BS, Freeman R, Reich DL, Klang E. Machine learning to predict in‐hospital mortality among patients with severe obesity: Proof of concept study. Obes Sci Pract 2022; 8:474-482. [PMID: 35949284 PMCID: PMC9358726 DOI: 10.1002/osp4.571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/25/2021] [Accepted: 10/01/2021] [Indexed: 11/22/2022] Open
Abstract
Objectives Hospitalized patients with severe obesity require adapted hospital management. The aim of this study was to evaluate a machine learning model to predict in‐hospital mortality among this population. Methods Data of unselected consecutive emergency department admissions of hospitalized patients with severe obesity (BMI ≥ 40 kg/m2) was analyzed. Data was retrieved from five hospitals from the Mount Sinai health system, New York. The study time frame was between January 2011 and December 2019. Data was used to train a gradient‐boosting machine learning model to identify in‐hospital mortality. The model was trained and evaluated based on the data from four hospitals and externally validated on held‐out data from the fifth hospital. Results A total of 14,078 hospital admissions of inpatients with severe obesity were included. The in‐hospital mortality rate was 297/14,078 (2.1%). In univariate analysis, albumin (area under the curve [AUC] = 0.77), blood urea nitrogen (AUC = 0.76), acuity level (AUC = 0.73), lactate (AUC = 0.72), and chief complaint (AUC = 0.72) were the best single predictors. For Youden’s index, the model had a sensitivity of 0.77 (95% CI: 0.67–0.86) with a false positive rate of 1:9. Conclusion A machine learning model trained on clinical measures provides proof of concept performance in predicting mortality in patients with severe obesity. This implies that such models may help to adopt specific decision support tools for this population.
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Affiliation(s)
- Shelly Soffer
- Internal Medicine B Assuta Medical Center Ashdod Israel
- Ben‐Gurion University of the Negev Be’er Sheva Israel
| | - Eyal Zimlichman
- Hospital Management Sheba Medical Center Tel Hashomer Israel
- Sackler Medical School Tel Aviv University Tel Aviv Israel
- Sheba Talpiot Medical Leadership Program Tel Hashomer Israel
| | - Matthew A. Levin
- Department of Population Health Science and Policy Institute for Healthcare Delivery Science Icahn School of Medicine at Mount Sinai New York New York USA
- Department of Anesthesiology, Perioperative and Pain Medicine Icahn School of Medicine at Mount Sinai New York New York USA
| | - Alexis M. Zebrowski
- Department of Emergency Medicine Icahn School of Medicine at Mount Sinai New York New York USA
- Department of Population Health Science and Policy Institute for Translational Epidemiology Icahn School of Medicine at Mount Sinai New York New York USA
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York New York USA
- Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York New York USA
| | - Robert Freeman
- Department of Population Health Science and Policy Institute for Healthcare Delivery Science Icahn School of Medicine at Mount Sinai New York New York USA
| | - David L. Reich
- Department of Anesthesiology, Perioperative and Pain Medicine Icahn School of Medicine at Mount Sinai New York New York USA
| | - Eyal Klang
- Sackler Medical School Tel Aviv University Tel Aviv Israel
- Sheba Talpiot Medical Leadership Program Tel Hashomer Israel
- Department of Diagnostic Imaging Sheba Medical Center Tel Hashomer Israel
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Wang B, Wang Z, Poundarik AA, Zaki MJ, Bockman RS, Glicksberg BS, Nadkarni GN, Vashishth D. Unmasking Fracture Risk in Type 2 Diabetes: The Association of Longitudinal Glycemic Hemoglobin Level and Medications. J Clin Endocrinol Metab 2022; 107:e1390-e1401. [PMID: 34888676 PMCID: PMC8947783 DOI: 10.1210/clinem/dgab882] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [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: 08/13/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT Fracture risk is underestimated in people with type 2 diabetes (T2D). OBJECTIVE To investigate the longitudinal relationship of glycated hemoglobin (HbA1c) and common medications on fracture risk in people with T2D. METHODS This retrospective population-based cohort study was conducted using de-identified claims and electronic health record data obtained from the OptumLabs Data Warehouse for the period January 1, 2007, to September 30, 2015. For each individual, the study was conducted within a 2-year HbA1c observation period and a 2-year fracture follow-up period. A cohort of 157 439 individuals with T2D [age ≥ 55 years with mean HbA1c value ≥ 6%] were selected from 4 018 250 US Medicare Advantage/Commercial enrollees with a T2D diagnosis. All fractures and fragility fractures were measured. RESULTS With covariates adjusted, poor glycemic control in T2D individuals was associated with an 29% increase of all fracture risk, compared with T2D individuals who had adequate glycemic control (HR: 1.29; 95% CI, 1.22-1.36). Treatment with metformin (HR: 0.88; 95% CI, 0.85-0.92) and DPP4 inhibitors (HR: 0.93; 95% CI, 0.88-0.98) was associated with a reduced all fracture risk, while insulin (HR: 1.26; 95% CI, 1.21-1.32), thiazolidinediones (HR: 1.23; 95% CI, 1.18-1.29), and meglitinides (HR: 1.12; 95% CI, 1.00-1.26) were associated with an increased all fracture risk (All P value < 0.05). Bisphosphonates were associated similarly with increased fracture risk in the T2D and nondiabetic groups. CONCLUSION Longitudinal 2-year HbA1c is independently associated with elevated all fracture risk in T2D individuals during a 2-year follow-up period. Metformin and DPP4 inhibitors can be used for management of T2D fracture risk.
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Affiliation(s)
- Bowen Wang
- Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- OptumLabs Visiting Fellow, Eden Prairie, MN 55344, USA
| | - Zehai Wang
- Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Atharva A Poundarik
- Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Mohammed J Zaki
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Richard S Bockman
- Division of Endocrinology and Metabolic Bone Disease, Hospital for Special Surgery, New York, NY 10021, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Deepak Vashishth
- Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- OptumLabs Visiting Fellow, Eden Prairie, MN 55344, USA
- Correspondence: Deepak Vashishth, PhD, Center for Biotechnology & Interdisciplinary Studies, Professor of Biomedical Engineering, 110 8th Street, BT 2213, Troy NY, USA 12180-3590.
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Tang AS, Oskotsky T, Havaldar S, Mantyh WG, Bicak M, Solsberg CW, Woldemariam S, Zeng B, Hu Z, Oskotsky B, Dubal D, Allen IE, Glicksberg BS, Sirota M. Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations. Nat Commun 2022; 13:675. [PMID: 35115528 PMCID: PMC8814236 DOI: 10.1038/s41467-022-28273-0] [Citation(s) in RCA: 2] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 01/18/2022] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.
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Affiliation(s)
- Alice S Tang
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.
- Graduate Program in Bioengineering, UCSF, San Francisco, CA, USA.
- School of Medicine, UCSF, San Francisco, CA, USA.
| | - Tomiko Oskotsky
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Department of Pediatrics, UCSF, San Francisco, CA, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - William G Mantyh
- Department of Neurology, University of Minnesota School of Medicine, Minneapolis, MN, USA
| | - Mesude Bicak
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Caroline Warly Solsberg
- Pharmaceutical Sciences and Pharmacogenomics, UCSF, San Francisco, CA, USA
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
- Memory and Aging Center, UCSF, San Francisco, CA, USA
| | - Sarah Woldemariam
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
| | - Billy Zeng
- School of Medicine, UCSF, San Francisco, CA, USA
| | - Zicheng Hu
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
| | - Boris Oskotsky
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
| | - Dena Dubal
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Isabel E Allen
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA.
- Department of Pediatrics, UCSF, San Francisco, CA, USA.
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Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022; 29 Suppl 2:S226-S235. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVES High-resolution computed tomography (HRCT) is paramount in the assessment of interstitial lung disease (ILD). Yet, HRCT interpretation of ILDs may be hampered by inter- and intra-observer variability. Recently, artificial intelligence (AI) has revolutionized medical image analysis. This technology has the potential to advance patient care in ILD. We aimed to systematically evaluate the application of AI for the analysis of ILD in HRCT. MATERIALS AND METHODS We searched MEDLINE/PubMed databases for original publications of deep learning for ILD analysis on chest CT. The search included studies published up to March 1, 2021. The risk of bias evaluation included tailored Quality Assessment of Diagnostic Accuracy Studies and the modified Joanna Briggs Institute Critical Appraisal checklist. RESULTS Data was extracted from 19 retrospective studies. Deep learning techniques included detection, segmentation, and classification of ILD on HRCT. Most studies focused on the classification of ILD into different morphological patterns. Accuracies of 78%-91% were achieved. Two studies demonstrated near-expert performance for the diagnosis of idiopathic pulmonary fibrosis (IPF). The Quality Assessment of Diagnostic Accuracy Studies tool identified a high risk of bias in 15/19 (78.9%) of the studies. CONCLUSION AI has the potential to contribute to the radiologic diagnosis and classification of ILD. However, the accuracy performance is still not satisfactory, and research is limited by a small number of retrospective studies. Hence, the existing published data may not be sufficiently reliable. Only well-designed prospective controlled studies can accurately assess the value of existing AI tools for ILD evaluation.
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Soffer S, Zimlichman E, Glicksberg BS, Efros O, Levin MA, Freeman R, Reich DL, Klang E. Obesity as a mortality risk factor in the medical ward: a case control study. BMC Endocr Disord 2022; 22:13. [PMID: 34991575 PMCID: PMC8733434 DOI: 10.1186/s12902-021-00912-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 12/05/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Research regarding the association between severe obesity and in-hospital mortality is inconsistent. We evaluated the impact of body mass index (BMI) levels on mortality in the medical wards. The analysis was performed separately before and during the COVID-19 pandemic. METHODS We retrospectively retrieved data of adult patients admitted to the medical wards at the Mount Sinai Health System in New York City. The study was conducted between January 1, 2011, to March 23, 2021. Patients were divided into two sub-cohorts: pre-COVID-19 and during-COVID-19. Patients were then clustered into groups based on BMI ranges. A multivariate logistic regression analysis compared the mortality rate among the BMI groups, before and during the pandemic. RESULTS Overall, 179,288 patients were admitted to the medical wards and had a recorded BMI measurement. 149,098 were admitted before the COVID-19 pandemic and 30,190 during the pandemic. Pre-pandemic, multivariate analysis showed a "J curve" between BMI and mortality. Severe obesity (BMI > 40) had an aOR of 0.8 (95% CI:0.7-1.0, p = 0.018) compared to the normal BMI group. In contrast, during the pandemic, the analysis showed a "U curve" between BMI and mortality. Severe obesity had an aOR of 1.7 (95% CI:1.3-2.4, p < 0.001) compared to the normal BMI group. CONCLUSIONS Medical ward patients with severe obesity have a lower risk for mortality compared to patients with normal BMI. However, this does not apply during COVID-19, where obesity was a leading risk factor for mortality in the medical wards. It is important for the internal medicine physician to understand the intricacies of the association between obesity and medical ward mortality.
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Affiliation(s)
- Shelly Soffer
- Internal Medicine B, Assuta Medical Center, Ashdod, Israel.
- Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Eyal Zimlichman
- Hospital Management, Sheba Medical Center, Tel Hashomer, Israel
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
- Sheba Talpiot Medical Leadership Program, Tel Hashomer, Israel
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Orly Efros
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
- Thrombosis & Hemostasis Unit, Sheba Medical Center, Tel Hashomer, Israel
| | - Matthew A Levin
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Robert Freeman
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA
| | - David L Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Eyal Klang
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
- Sheba Talpiot Medical Leadership Program, Tel Hashomer, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel
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Vunikili R, Glicksberg BS, Johnson KW, Dudley JT, Subramanian L, Shameer K. Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients. Front Artif Intell 2021; 4:742723. [PMID: 34957391 PMCID: PMC8702828 DOI: 10.3389/frai.2021.742723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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] [Received: 07/16/2021] [Accepted: 10/25/2021] [Indexed: 01/16/2023] Open
Abstract
Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage. Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern. Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse. Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.
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Affiliation(s)
- Ramya Vunikili
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States.,Department of Information Services, Center for Research Informatics and Innovation, Northwell Health, New York, NY, United States
| | - Benjamin S Glicksberg
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Kipp W Johnson
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States
| | - Lakshminarayanan Subramanian
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States
| | - Khader Shameer
- Department of Information Services, Center for Research Informatics and Innovation, Northwell Health, New York, NY, United States.,Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States
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Taubes A, Nova P, Zalocusky KA, Kosti I, Bicak M, Zilberter MY, Hao Y, Yoon SY, Oskotsky T, Pineda S, Chen B, Aery Jones EA, Choudhary K, Grone B, Balestra ME, Chaudhry F, Paranjpe I, De Freitas J, Koutsodendris N, Chen N, Wang C, Chang W, An A, Glicksberg BS, Sirota M, Huang Y. Author Correction: Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease. Nat Aging 2021; 1:1202. [PMID: 37117528 DOI: 10.1038/s43587-021-00144-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Affiliation(s)
- Alice Taubes
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA
| | - Phil Nova
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA
| | - Kelly A Zalocusky
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Idit Kosti
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, CA, USA
| | - Mesude Bicak
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Misha Y Zilberter
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA, USA
| | - Yanxia Hao
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA, USA
| | - Seo Yeon Yoon
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
| | - Tomiko Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, CA, USA
| | - Silvia Pineda
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Surgery, University of California, San Francisco, CA, USA
| | - Bin Chen
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Emily A Aery Jones
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA
| | - Krishna Choudhary
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Brian Grone
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Maureen E Balestra
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
| | - Fayzan Chaudhry
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ishan Paranjpe
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole Koutsodendris
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
- Development and Stem Cell Biology Graduate Program, University of California, San Francisco, CA, USA
| | - Nuo Chen
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
| | - Celine Wang
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
| | - William Chang
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
| | - Alice An
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
- Department of Pediatrics, University of California, San Francisco, CA, USA.
| | - Yadong Huang
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA.
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA, USA.
- Department of Neurology, University of California, San Francisco, CA, USA.
- Department of Pathology, University of California, San Francisco, CA, USA.
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Somani SS, Honarvar H, Narula S, Landi I, Lee S, Khachatoorian Y, Rehmani A, Kim A, De Freitas JK, Teng S, Jaladanki S, Kumar A, Russak A, Zhao SP, Freeman R, Levin MA, Nadkarni GN, Kagen AC, Argulian E, Glicksberg BS. Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening. Eur Heart J Digit Health 2021; 3:56-66. [PMID: 35355847 PMCID: PMC8946569 DOI: 10.1093/ehjdh/ztab101] [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] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/16/2021] [Accepted: 11/19/2021] [Indexed: 01/30/2023]
Abstract
Aims Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods and results We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (AUROC) 0.81 ± 0.01] outperforms both the ECG model (AUROC 0.59 ± 0.01) and EHR model (AUROC 0.65 ± 0.01). On a sample of 100 patients from the test set, the Fusion model also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells' Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50-0.58, specificity 0.00-0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66-0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77-0.84) subgroups. Conclusion Synergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE.
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Affiliation(s)
- Sulaiman S Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA
| | - Hossein Honarvar
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Sukrit Narula
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 20 Copeland Ave, Hamilton, ON L8L 2X2, Canada,Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main St W, Hamilton, ON L8S 4L8, Canada
| | - Isotta Landi
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Shawn Lee
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Yeraz Khachatoorian
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Arsalan Rehmani
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Andrew Kim
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Shelly Teng
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA
| | - Suraj Jaladanki
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA
| | - Arvind Kumar
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA
| | - Adam Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA,Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Shan P Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA,Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Robert Freeman
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA,Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Matthew A Levin
- Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Alexander C Kagen
- Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
| | - Edgar Argulian
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA,Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA
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Paranjpe I, Tsao NL, De Freitas JK, Judy R, Chaudhary K, Forrest IS, Jaladanki SK, Paranjpe M, Sharma P, Glicksberg BS, Narula J, Do R, Damrauer SM, Nadkarni GN. Derivation and Validation of Genome-Wide Polygenic Score for Ischemic Heart Failure. J Am Heart Assoc 2021; 10:e021916. [PMID: 34713709 PMCID: PMC8751935 DOI: 10.1161/jaha.121.021916] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022]
Abstract
Background Despite advances in cardiovascular disease and risk factor management, mortality from ischemic heart failure (HF) in patients with coronary artery disease (CAD) remains high. Given the partial role of genetics in HF and lack of reliable risk stratification tools, we developed and validated a polygenic risk score for HF in patients with CAD, which we term HF-PRS. Methods and Results Using summary statistics from a recent genome-wide association study for HF, we developed candidate PRSs in the Mount Sinai BioMe CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred. We validated the best score in the Penn Medicine BioBank (N=7250) and performed a subgroup analysis in a high-risk cohort who had undergone coronary catheterization. We observed a significant association between HF-PRS score and ischemic HF even after adjusting for evidence of obstructive CAD in patients of European ancestry in both BioMe (odds ratio [OR], 1.14 per SD; 95% CI, 1.05-1.24; P=0.003) and Penn Medicine BioBank (OR, 1.07 per SD; 95% CI, 1.01-1.13; P=0.016). In European patients with CAD in Penn Medicine BioBank who had undergone coronary catheterization, individuals in the top 10th percentile of PRS had a 2-fold increased odds of ischemic HF (OR, 2.0; 95% CI, 1.1-3.7; P=0.02) compared with the bottom 10th percentile. Conclusions A PRS for HF enables risk stratification in patients with CAD. Future prospective studies aimed at demonstrating clinical utility are warranted for adoption in the patient setting.
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Affiliation(s)
- Ishan Paranjpe
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Noah L. Tsao
- Department of SurgeryPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA
| | - Jessica K. De Freitas
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Renae Judy
- Department of SurgeryPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA
| | - Kumardeep Chaudhary
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Iain S. Forrest
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Suraj K. Jaladanki
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Manish Paranjpe
- Division of Health Science and TechnologyHarvard Medical SchoolBostonMA
| | | | - CBIPM Genomics Team
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
| | | | - Benjamin S. Glicksberg
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Jagat Narula
- Zena and Michael A. Wiener Cardiovascular InstituteIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Ron Do
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Scott M. Damrauer
- Department of SurgeryPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
- Division of NephrologyDepartment of MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Renal ProgramJames J. Peters VA Medical Center at BronxNew YorkNY
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Klang E, Soffer S, Zimlichman E, Zebrowski A, Glicksberg BS, Grossman E, Reich DL, Freeman R, Levin MA. Synergistic effect of hypoalbuminaemia and hypotension in predicting in-hospital mortality and intensive care admission: a retrospective cohort study. BMJ Open 2021; 11:e050216. [PMID: 34706952 PMCID: PMC8552132 DOI: 10.1136/bmjopen-2021-050216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE Hypoalbuminaemia is an important prognostic factor. It may be associated with poor nutritional states, chronic heart and kidney disease, long-standing infection and cancer. Hypotension is a hallmark of circulatory failure. We evaluated hypoalbuminaemia and hypotension synergism as predictor of in-hospital mortality and intensive care unit (ICU) admission. DESIGN We retrospectively analysed emergency department (ED) visits from January 2011 to December 2019. SETTING Data were retrieved from five Mount Sinai health system hospitals, New York. PARTICIPANTS We included consecutive ED patients ≥18 years with albumin measurements. PRIMARY AND SECONDARY OUTCOME MEASURES Clinical outcomes were in-hospital mortality and ICU admission. The rates of these outcomes were stratified by systolic blood pressure (SBP) (<90 vs ≥90 mm Hg) and albumin levels. Variables included demographics, presenting vital signs, comorbidities (measured as ICD codes) and other common blood tests. Multivariable logistic regression models analysed the adjusted OR of different levels of albumin and SBP for predicting ICU admission and in-hospital mortality. The models were adjusted for demographics, vital signs, comorbidities and common laboratory results. Patients with albumin 3.5-4.5 g/dL and SBP ≥90 mm Hg were used as reference. RESULTS The cohort included 402 123 ED arrivals (27.9% of total adult ED visits). The rates of in-hospital mortality, ICU admission and overall admission were 1.7%, 8.4% and 47.1%, respectively. For SBP <90 mm Hg and albumin <2.5 g/dL, mortality and ICU admission rates were 34.0% and 40.6%, respectively; for SBP <90 mm Hg and albumin ≥2.5 g/dL 8.2% and 24.1%, respectively; for SBP ≥90 mm Hg and albumin <2.5 g/dL 11.4% and 18.6%, respectively; for SBP ≥90 mm Hg and albumin 3.5-4.5 g/dL 0.5% and 6.4%, respectively. Multivariable analysis showed that in patients with hypotension and albumin <2.5 g/dL the adjusted OR for in-hospital mortality was 37.1 (95% CI 32.3 to 42.6), and for ICU admission was 5.4 (95% CI 4.8 to 6.1). CONCLUSION Co-occurrence of hypotension and hypoalbuminaemia is associated with poor hospital outcomes.
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Affiliation(s)
- Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Israel
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
| | - Shelly Soffer
- Internal Medicine B, Assuta Medical Center, Ashdod, Israel
- Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Eyal Zimlichman
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
- Hospital management, Sheba Medical Center, Tel Hashomer, Israel
| | - Alexis Zebrowski
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - E Grossman
- Sackler Medical School, Tel Aviv University, Tel Aviv, Israel
- Internal medicine Wing, Sheba Medical Center, Tel Hashomer, Israel
| | - David L Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert Freeman
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matthew A Levin
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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43
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Wanyan T, Honarvar H, Jaladanki SK, Zang C, Naik N, Somani S, De Freitas JK, Paranjpe I, Vaid A, Zhang J, Miotto R, Wang Z, Nadkarni GN, Zitnik M, Azad A, Wang F, Ding Y, Glicksberg BS. Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients. Patterns (N Y) 2021; 2:100389. [PMID: 34723227 PMCID: PMC8542449 DOI: 10.1016/j.patter.2021.100389] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 09/12/2021] [Accepted: 10/21/2021] [Indexed: 12/30/2022]
Abstract
Deep Learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus-disease 2019 (COVID-19) pandemic where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL) which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR data set to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full data set and a restricted data set. CL models consistently outperform CEL models with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC.
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Affiliation(s)
- Tingyi Wanyan
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Hossein Honarvar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suraj K Jaladanki
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Nidhi Naik
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica K De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ishan Paranjpe
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jing Zhang
- Renmin University of China, Beijing, China
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA
| | - Girish N Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, USA
| | - Ariful Azad
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Ying Ding
- Dell Medical School, University of Texas at Austin, Austin, TX, USA.,School of Informatics, University of Texas at Austin, Austin, TX, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Paranjpe MD, Belonwu S, Wang JK, Oskotsky T, Gupta A, Taubes A, Zalocusky KA, Paranjpe I, Glicksberg BS, Huang Y, Sirota M. Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer's Disease. Front Aging Neurosci 2021; 13:735611. [PMID: 34658838 PMCID: PMC8515049 DOI: 10.3389/fnagi.2021.735611] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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] [Received: 07/02/2021] [Accepted: 09/01/2021] [Indexed: 01/09/2023] Open
Abstract
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer's Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer's disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD. Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.
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Affiliation(s)
- Manish D Paranjpe
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.,Harvard-MIT Program in Health Sciences and Technology, Harvard Medical School, Boston, MA, United States
| | - Stella Belonwu
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.,Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA, United States
| | - Jason K Wang
- Harvard-MIT Program in Health Sciences and Technology, Harvard Medical School, Boston, MA, United States
| | - Tomiko Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.,Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Aarzu Gupta
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Alice Taubes
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.,The Gladstone Institute of Neurological Disease, San Francisco, CA, United States
| | - Kelly A Zalocusky
- The Gladstone Institute of Neurological Disease, San Francisco, CA, United States.,Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Ishan Paranjpe
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Yadong Huang
- The Gladstone Institute of Neurological Disease, San Francisco, CA, United States.,Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.,Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
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Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, Russak A, Zhao S, Levin MA, Freeman RS, Charney AW, Kukar A, Kim B, Danilov T, Lerakis S, Argulian E, Narula J, Nadkarni GN, Glicksberg BS. Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram. JACC Cardiovasc Imaging 2021; 15:395-410. [PMID: 34656465 PMCID: PMC8917975 DOI: 10.1016/j.jcmg.2021.08.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/29/2021] [Accepted: 08/05/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVES This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. BACKGROUND Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right- ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. METHODS A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. RESULTS We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation. CONCLUSIONS DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.
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Affiliation(s)
- Akhil Vaid
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kipp W Johnson
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Sulaiman S Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mesude Bicak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Isotta Landi
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adam Russak
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matthew A Levin
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert S Freeman
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander W Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Atul Kukar
- Department of Cardiology, Mount Sinai Queens Hospital, Astoria, New York, USA, and Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Cardiology, Mount Sinai West Hospital and Icahn School of Medicine at Mount Sinai, New York, New York USA
| | - Bette Kim
- Mount Sinai Beth Israel Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Tatyana Danilov
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stamatios Lerakis
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Bienstock SW, Tandon P, Govindarajulu U, Leibner E, Glicksberg BS, Samtani R, Giustino G, Gidwani U, Kohli-Seth R, Goldman ME. Impact of Myocardial Injury in Hospitalized Patients With COVID-19 in 2 Peak Time Periods. J Am Coll Cardiol 2021; 78:1482-1483. [PMID: 34593130 PMCID: PMC8475637 DOI: 10.1016/j.jacc.2021.08.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/30/2021] [Accepted: 08/04/2021] [Indexed: 11/30/2022]
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Thompson RC, Simons NW, Wilkins L, Cheng E, Del Valle DM, Hoffman GE, Fennessy B, Mouskas K, Francoeur NJ, Johnson JS, Lepow L, Le Berichel J, Chang C, Beckmann AG, Wang YC, Nie K, Zaki N, Tuballes K, Barcessat V, Cedillo MA, Huckins L, Roussos P, Marron TU, Glicksberg BS, Nadkarni G, Gonzalez-Kozlova E, Kim-Schulze S, Sebra R, Merad M, Gnjatic S, Schadt EE, Charney AW, Beckmann ND. Acute COVID-19 gene-expression profiles show multiple etiologies of long-term sequelae. medRxiv 2021:2021.10.04.21264434. [PMID: 34642700 PMCID: PMC8509101 DOI: 10.1101/2021.10.04.21264434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Two years into the SARS-CoV-2 pandemic, the post-acute sequelae of infection are compounding the global health crisis. Often debilitating, these sequelae are clinically heterogeneous and of unknown molecular etiology. Here, a transcriptome-wide investigation of this new condition was performed in a large cohort of acutely infected patients followed clinically into the post-acute period. Gene expression signatures of post-acute sequelae were already present in whole blood during the acute phase of infection, with both innate and adaptive immune cells involved. Plasma cells stood out as driving at least two distinct clusters of sequelae, one largely dependent on circulating antibodies against the SARS-CoV-2 spike protein and the other antibody-independent. Altogether, multiple etiologies of post-acute sequelae were found concomitant with SARS-CoV-2 infection, directly linking the emergence of these sequelae with the host response to the virus.
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Affiliation(s)
- Ryan C. Thompson
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nicole W. Simons
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lillian Wilkins
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Esther Cheng
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Diane Marie Del Valle
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
| | - Gabriel E. Hoffman
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Brian Fennessy
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Konstantinos Mouskas
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- The Susan and Leonard Feinstein Inflammatory Bowel Disease Clinical Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nancy J. Francoeur
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Lauren Lepow
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Christie Chang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Ying-chih Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nicholas Zaki
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kevin Tuballes
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
| | - Vanessa Barcessat
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
| | - Mario A. Cedillo
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura Huckins
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panagiotis Roussos
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Thomas U. Marron
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Medicine, Mount Sinai, New York, NY 10029, USA
- Department of Medicine, Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Benjamin S. Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Girish Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Edgar Gonzalez-Kozlova
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Seunghee Kim-Schulze
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Oncological Sciences, Mount Sinai, New York, NY 10029, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Sema4, a Mount Sinai venture, Stamford CT, 06902, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Miriam Merad
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
- Department of Oncological Sciences, Mount Sinai, New York, NY 10029, USA
| | - Sacha Gnjatic
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Mount Sinai, New York, NY 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Medicine, Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Oncological Sciences, Mount Sinai, New York, NY 10029, USA
| | - Eric E. Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Sema4, a Mount Sinai venture, Stamford CT, 06902, USA
| | - Alexander W. Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Noam D. Beckmann
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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48
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Taubes A, Nova P, Zalocusky KA, Kosti I, Bicak M, Zilberter MY, Hao Y, Yoon SY, Oskotsky T, Pineda S, Chen B, Jones EAA, Choudhary K, Grone B, Balestra ME, Chaudhry F, Paranjpe I, De Freitas J, Koutsodendris N, Chen N, Wang C, Chang W, An A, Glicksberg BS, Sirota M, Huang Y. Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease. Nat Aging 2021; 1:932-947. [PMID: 36172600 PMCID: PMC9514594 DOI: 10.1038/s43587-021-00122-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
The evident genetic, pathological, and clinical heterogeneity of Alzheimer's disease (AD) poses challenges for traditional drug development. We conducted a computational drug repurposing screen for drugs to treat apolipoprotein (apo) E4-related AD. We first established apoE-genotype-dependent transcriptomic signatures of AD by analyzing publicly-available human brain database. We then queried these signatures against the Connectivity Map database containing transcriptomic perturbations of >1300 drugs to identify those that best reverse apoE-genotype-specific AD signatures. Bumetanide was identified as a top drug for apoE4 AD. Bumetanide treatment of apoE4 mice without or with Aβ accumulation rescued electrophysiological, pathological, or cognitive deficits. Single-nucleus RNA-sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed in apoE4-iPSC-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 in two electronic health record databases, suggesting effectiveness of bumetanide in preventing AD.
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Affiliation(s)
- Alice Taubes
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA 94143, USA
| | - Phil Nova
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA 94143, USA
| | - Kelly A. Zalocusky
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94143, USA
| | - Idit Kosti
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA
- Department of Pediatrics, University of California, San Francisco, CA 94158, USA, USA
| | - Mesude Bicak
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Misha Y. Zilberter
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Yanxia Hao
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Seo Yeon Yoon
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Tomiko Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA
- Department of Pediatrics, University of California, San Francisco, CA 94158, USA, USA
| | - Silvia Pineda
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA
- Department of Surgery, University of California, San Francisco, CA 94143, USA
| | - Bin Chen
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA
| | - Emily A. Aery Jones
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA 94143, USA
| | - Krishna Choudhary
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Brian Grone
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94143, USA
| | - Maureen E. Balestra
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Fayzan Chaudhry
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Ishan Paranjpe
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Jessica De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Nicole Koutsodendris
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Development and Stem Cell Biology Graduate Program, University of California, San Francisco, CA 94143, USA
| | - Nuo Chen
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Celine Wang
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - William Chang
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Alice An
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94158, USA
- Department of Pediatrics, University of California, San Francisco, CA 94158, USA, USA
- Correspondence: Yadong Huang () or Marina Sirota ()
| | - Yadong Huang
- Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA 94158, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, CA 94143, USA
- Gladstone Center for Translational Advancement, Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Neurology, University of California, San Francisco, CA 94143, USA
- Department of Pathology, University of California, San Francisco, CA 94143, USA
- Correspondence: Yadong Huang () or Marina Sirota ()
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49
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De Freitas JK, Johnson KW, Golden E, Nadkarni GN, Dudley JT, Bottinger EP, Glicksberg BS, Miotto R. Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records. Patterns (N Y) 2021; 2:100337. [PMID: 34553174 PMCID: PMC8441576 DOI: 10.1016/j.patter.2021.100337] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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] [Received: 03/29/2021] [Revised: 06/30/2021] [Accepted: 08/05/2021] [Indexed: 11/23/2022]
Abstract
Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.
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Affiliation(s)
- Jessica K. De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Kipp W. Johnson
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Eddye Golden
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Joel T. Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Erwin P. Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Digital Health Center at Hasso Plattner Institute, University of Potsdam, Professor-Dr.-Helmert-Str 2–3, 14482 Potsdam, Germany
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
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50
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Wanyan T, Honarvar H, Azad A, Ding Y, Glicksberg BS. Deep Learning with Heterogeneous Graph Embeddings for Mortality
Prediction from Electronic Health Records. Data Intelligence 2021. [DOI: 10.1162/dint_a_00097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model (HGM) on electronic health record (EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.
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Affiliation(s)
- Tingyi Wanyan
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York 10065, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
| | - Hossein Honarvar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York 10065, USA
| | - Ariful Azad
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
| | - Ying Ding
- Dell Medical School, University of Texas at Austin, Austin, Texas 78701-1996, USA
- School of Informatics, University of Texas at Austin, Austin, Texas 78712-1139, USA
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York 10065, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10065, USA
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