1
|
Vakulenko-Lagun B, Magdamo C, Charpignon ML, Zheng B, Albers MW, Das S. causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data. Comput Methods Programs Biomed 2023; 242:107819. [PMID: 37774426 PMCID: PMC10841064 DOI: 10.1016/j.cmpb.2023.107819] [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] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/06/2023] [Accepted: 09/15/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND AND OBJECTIVE Competing risks data arise in both observational and experimental clinical studies with time-to-event outcomes, when each patient might follow one of the multiple mutually exclusive competing paths. Ignoring competing risks in the analysis can result in biased conclusions. In addition, possible confounding bias of the treatment-outcome relationship has to be addressed, when estimating treatment effects from observational data. In order to provide tools for estimation of average treatment effects on time-to-event outcomes in the presence of competing risks, we developed the R package causalCmprsk. We illustrate the package functionality in the estimation of effects of a right heart catheterization procedure on discharge and in-hospital death from observational data. METHODS The causalCmprsk package implements an inverse probability weighting estimation approach, aiming to emulate baseline randomization and alleviate possible treatment selection bias. The package allows for different types of weights, representing different target populations. causalCmprsk builds on existing methods from survival analysis and adapts them to the causal analysis in non-parametric and semi-parametric frameworks. RESULTS The causalCmprsk package has two main functions: fit.cox assumes a semiparametric structural Cox proportional hazards model for the counterfactual cause-specific hazards, while fit.nonpar does not impose any structural assumptions. In both frameworks, causalCmprsk implements estimators of (i) absolute risks for each treatment arm, e.g., cumulative hazards or cumulative incidence functions, and (ii) relative treatment effects, e.g., hazard ratios, or restricted mean time differences. The latter treatment effect measure translates the treatment effect from probability into more intuitive time domain and allows the user to quantify, for example, by how many days or months the treatment accelerates the recovery or postpones illness or death. CONCLUSIONS The causalCmprsk package provides a convenient and useful tool for causal analysis of competing risks data. It allows the user to distinguish between different causes of the end of follow-up and provides several time-varying measures of treatment effects. The package is accompanied by a vignette that contains more details, examples and code, making the package accessible even for non-expert users.
Collapse
Affiliation(s)
| | - Colin Magdamo
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Marie-Laure Charpignon
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bang Zheng
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Mark W Albers
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
2
|
Wilcox DR, Rudmann EA, Ye E, Noori A, Magdamo C, Jain A, Alabsi H, Foy B, Triant VA, Robbins GK, Westover MB, Das S, Mukerji SS. Cognitive concerns are a risk factor for mortality in people with HIV and coronavirus disease 2019. AIDS 2023; 37:1565-1571. [PMID: 37195278 PMCID: PMC10355333 DOI: 10.1097/qad.0000000000003595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 05/03/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Data supporting dementia as a risk factor for coronavirus disease 2019 (COVID-19) mortality relied on ICD-10 codes, yet nearly 40% of individuals with probable dementia lack a formal diagnosis. Dementia coding is not well established for people with HIV (PWH), and its reliance may affect risk assessment. METHODS This retrospective cohort analysis of PWH with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR positivity includes comparisons to people without HIV (PWoH), matched by age, sex, race, and zipcode. Primary exposures were dementia diagnosis, by International Classification of Diseases (ICD)-10 codes, and cognitive concerns, defined as possible cognitive impairment up to 12 months before COVID-19 diagnosis after clinical review of notes from the electronic health record. Logistic regression models assessed the effect of dementia and cognitive concerns on odds of death [odds ratio (OR); 95% CI (95% confidence interval)]; models adjusted for VACS Index 2.0. RESULTS Sixty-four PWH were identified out of 14 129 patients with SARS-CoV-2 infection and matched to 463 PWoH. Compared with PWoH, PWH had a higher prevalence of dementia (15.6% vs. 6%, P = 0.01) and cognitive concerns (21.9% vs. 15.8%, P = 0.04). Death was more frequent in PWH ( P < 0.01). Adjusted for VACS Index 2.0, dementia [2.4 (1.0-5.8), P = 0.05] and cognitive concerns [2.4 (1.1-5.3), P = 0.03] were associated with increased odds of death. In PWH, the association between cognitive concern and death trended towards statistical significance [3.92 (0.81-20.19), P = 0.09]; there was no association with dementia. CONCLUSION Cognitive status assessments are important for care in COVID-19, especially among PWH. Larger studies should validate findings and determine long-term COVID-19 consequences in PWH with preexisting cognitive deficits.
Collapse
Affiliation(s)
- Douglas R. Wilcox
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Brigham and Women's Hospital
- Department of Neurology, Harvard Medical School
| | - Emily A. Rudmann
- Neuroimmunology and Neuro-Infectious Diseases Division, Department of Neurology, Massachusetts General Hospital, Boston
- Division of Infectious Diseases, Vaccine and Immunotherapy Center, Massachusetts General Hospital, Charlestown
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital
| | - Ayush Noori
- Department of Neurology, Massachusetts General Hospital
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital
| | - Haitham Alabsi
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Harvard Medical School
| | - Brody Foy
- Center for Systems Biology, Massachusetts General Hospital, and Department of Systems Biology, Harvard Medical School
| | - Virginia A. Triant
- Division of Infectious Diseases
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Harvard Medical School
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital
- Department of Neurology, Harvard Medical School
| | - Shibani S. Mukerji
- Neuroimmunology and Neuro-Infectious Diseases Division, Department of Neurology, Massachusetts General Hospital, Boston
- Division of Infectious Diseases, Vaccine and Immunotherapy Center, Massachusetts General Hospital, Charlestown
| |
Collapse
|
3
|
Weinberg MS, Zafar A, Magdamo C, Chung SY, Chou WH, Nayan M, Deodhar M, Frendl DM, Feldman AS, Faustman DL, Arnold SE, Vakulenko-Lagun B, Das S. Association of BCG Vaccine Treatment With Death and Dementia in Patients With Non-Muscle-Invasive Bladder Cancer. JAMA Netw Open 2023; 6:e2314336. [PMID: 37204792 DOI: 10.1001/jamanetworkopen.2023.14336] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2023] Open
Abstract
Importance The BCG vaccine-used worldwide to prevent tuberculosis-confers multiple nonspecific beneficial effects, and intravesical BCG vaccine is currently the recommended treatment for non-muscle-invasive bladder cancer (NMIBC). Moreover, BCG vaccine has been hypothesized to reduce the risk of Alzheimer disease and related dementias (ADRD), but previous studies have been limited by sample size, study design, or analyses. Objective To evaluate whether intravesical BCG vaccine exposure is associated with a decreased incidence of ADRD in a cohort of patients with NMIBC while accounting for death as a competing event. Design, Setting, and Participants This cohort study was performed in patients aged 50 years or older initially diagnosed with NMIBC between May 28, 1987, and May 6, 2021, treated within the Mass General Brigham health care system. The study included a 15-year follow-up of individuals (BCG vaccine treated or controls) whose condition did not clinically progress to muscle-invasive cancer within 8 weeks and did not have an ADRD diagnosis within the first year after the NMIBC diagnosis. Data analysis was conducted from April 18, 2021, to March 28, 2023. Main Outcomes and Measures The main outcome was time to ADRD onset identified using diagnosis codes and medications. Cause-specific hazard ratios (HRs) were estimated using Cox proportional hazards regression after adjusting for confounders (age, sex, and Charlson Comorbidity Index) using inverse probability scores weighting. Results In this cohort study including 6467 individuals initially diagnosed with NMIBC between 1987 and 2021, 3388 patients underwent BCG vaccine treatment (mean [SD] age, 69.89 [9.28] years; 2605 [76.9%] men) and 3079 served as controls (mean [SD] age, 70.73 [10.00] years; 2176 [70.7%] men). Treatment with BCG vaccine was associated with a lower rate of ADRD (HR, 0.80; 95% CI, 0.69-0.99), with an even lower rate of ADRD in patients aged 70 years or older at the time of BCG vaccine treatment (HR, 0.74; 95% CI, 0.60-0.91). In competing risks analysis, BCG vaccine was associated with a lower risk of ADRD (5-year risk difference, -0.011; 95% CI, -0.019 to -0.003) and a decreased risk of death in patients without an earlier diagnosis of ADRD (5-year risk difference, -0.056; 95% CI, -0.075 to -0.037). Conclusions and Relevance In this study, BCG vaccine was associated with a significantly lower rate and risk of ADRD in a cohort of patients with bladder cancer when accounting for death as a competing event. However, the risk differences varied with time.
Collapse
Affiliation(s)
- Marc S Weinberg
- Department of Psychiatry, Massachusetts General Hospital, Boston
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Affan Zafar
- Harvard Medical School, Boston, Massachusetts
- Department of Urology, Massachusetts General Hospital, Boston
- Division of Urology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston
| | | | - Wesley H Chou
- Harvard Medical School, Boston, Massachusetts
- Department of Urology, Oregon Health and Science University, Portland
| | - Madhur Nayan
- Harvard Medical School, Boston, Massachusetts
- Department of Urology, Massachusetts General Hospital, Boston
- Division of Urology, Brigham and Women's Hospital, Boston, Massachusetts
- Department of Urology, New York University, New York
| | | | - Daniel M Frendl
- Harvard Medical School, Boston, Massachusetts
- Department of Urology, Massachusetts General Hospital, Boston
- Department of Urology, Mayo Clinic, Phoenix, Arizona
| | - Adam S Feldman
- Harvard Medical School, Boston, Massachusetts
- Department of Urology, Massachusetts General Hospital, Boston
| | - Denise L Faustman
- Harvard Medical School, Boston, Massachusetts
- Immunobiology Laboratories, Massachusetts General Hospital, Boston
| | - Steven E Arnold
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | | | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
4
|
Sheu YH, Magdamo C, Miller M, Das S, Blacker D, Smoller JW. AI-assisted prediction of differential response to antidepressant classes using electronic health records. NPJ Digit Med 2023; 6:73. [PMID: 37100858 PMCID: PMC10133261 DOI: 10.1038/s41746-023-00817-8] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 04/04/2023] [Indexed: 04/28/2023] Open
Abstract
Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.
Collapse
Affiliation(s)
- Yi-Han Sheu
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Matthew Miller
- Harvard Injury Control Research Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
5
|
Sheu YH, Magdamo C, Miller M, Smoller JW, Blacker D. Initial antidepressant choice by non-psychiatrists: Learning from large-scale electronic health records. Gen Hosp Psychiatry 2023; 81:22-31. [PMID: 36724694 DOI: 10.1016/j.genhosppsych.2022.12.004] [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: 09/29/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Pharmacological treatment of depression mostly occurs in non-psychiatric settings, but the determinants of initial choice of antidepressant treatment in these settings are unclear. We investigate how non-psychiatrists choose among four antidepressant classes at first prescription (selective serotonin reuptake inhibitors [SSRI], bupropion, mirtazapine, or serotonin-norepinephrine reuptake inhibitors [SNRI]). METHOD Using electronic health records (EHRs), we included adult patients at the time of first antidepressant prescription with a co-occurring diagnosis code for a depressive disorder. We selected 64 variables based on a literature search and expert consultation, constructed the variables from either structured codes or through applying natural language processing (NLP), and modeled antidepressant choice using multinomial logistic regression, using SSRI as the reference class. RESULTS With 47,528 patients, we observed significant associations for 36 of 64 variables. Many of these associations suggested antidepressants' known pharmacological properties/actions guided choice. For example, there was a decreased likelihood of bupropion prescription among patients with epilepsy (adjusted OR 0.49, 95%CI: 0.41-0.57, p < 0.001), and an increased likelihood of mirtazapine prescription among patients with insomnia (adjusted OR 1.59, 95%CI: 1.40-1.80, p < 0.001). CONCLUSIONS Broadly speaking, non-psychiatrists' selection of antidepressant class appears to be at least in part guided by clinically relevant pharmacological considerations.
Collapse
Affiliation(s)
- Yi-Han Sheu
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, 2(nd) floor, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge Street, 6(th) floor, Boston, MA 02114, USA; Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA.
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital / Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA
| | - Matthew Miller
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA; Harvard Injury Control Research Centre, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA; Bouvé College of Health Sciences, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Massachusetts General Hospital, 185 Cambridge Street, 2(nd) floor, Boston, MA 02114, USA; Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge Street, 6(th) floor, Boston, MA 02114, USA; Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| |
Collapse
|
6
|
Iglesias JE, Billot B, Balbastre Y, Magdamo C, Arnold SE, Das S, Edlow BL, Alexander DC, Golland P, Fischl B. SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry. Sci Adv 2023; 9:eadd3607. [PMID: 36724222 PMCID: PMC9891693 DOI: 10.1126/sciadv.add3607] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 01/04/2023] [Indexed: 05/10/2023]
Abstract
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.
Collapse
Affiliation(s)
- Juan E. Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin Billot
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Yaël Balbastre
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven E. Arnold
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Brian L. Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
7
|
Mukerji SS, Wilcox DR, Rudmann EA, Ye E, Noori A, Magdamo C, Alabsi H, Triant VA, Robbins G, Brandon Westover M, Das S. 2358. Dementia and Cognitive Concerns are Risk Factors for Mortality in People with Human Immunodeficiency Virus and COVID-19. Open Forum Infect Dis 2022. [DOI: 10.1093/ofid/ofac492.165] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Abstract
Background
Despite higher prevalence of cognitive disorders in people with human immunodeficiency virus (PWH) and dementia being a risk factor for COVID-19 mortality, the association between dementia and adverse outcomes in PWH with COVID-19 has not been well established.
Methods
This was a matched case-control study (1:10) of patients with and without HIV at an academic institution with documented SARS-CoV-2 polymerase chain reaction (PCR) positivity from March 2020-March 2021. Data were extracted from the electronic health record data registry. PWH were matched to people without HIV (PWoH) by age, sex, race, and zip code. The primary exposures were dementia (identified using International Classification of Diseases, Tenth Revision codes) and cognitive concerns, defined as documentation of possible cognitive impairment up to 12 months prior to COVID-19 diagnosis and ascertained using a semi-automated natural language processing annotation tool. VACS 2.0 Index (including age, sex, body mass index, CD4+ T-cell count and HIV-1 RNA) was calculated. Logistic regression models assessed the effect of dementia and cognitive concerns on the odds of death (OR [95% confidence interval]), adjusted for VACS 2.0 Index.
Results
Sixty-four (0.45%) PWH were identified among 14129 patients with COVID-19 and were matched to 463 PWoH. Among PWH, 59% were virally suppressed, and 14% had CD4< 200 cells/μL. Compared to 463 matched PWoH, PWH had higher prevalence of dementia (16% vs. 6%, p=0.01) and cognitive concerns (22% vs. 16%, p=0.04). Death was more frequent in PWH (17% vs. 6%, p< 0.01) and at younger ages (58 vs. 66 years, p=0.03). Cognitive concerns (2.5 [1.1–5.9], p=0.03) and dementia (3.4 [1.3–8.1], p=0.01) were significantly associated with increased adjusted odds of death in the overall group. Among PWH, cognitive concerns (7.2 [1.1–48], p=0.04) and dementia (6.0 [0.8–43.8], p=0.08) remained associated with mortality.
Conclusion
Dementia and cognitive concerns were associated with mortality among PWH with COVID-19. The magnitude of the effect of cognitive impairment on COVID-19 outcomes may be greater in HIV, and additional studies with larger cohorts will help to assess this association further. Assessment of cognitive status is an important component to care for aging PWH in the COVID-19 era.
Disclosures
All Authors: No reported disclosures.
Collapse
Affiliation(s)
| | - Douglas R Wilcox
- Massachusetts General Hospital/Brigham and Women's Hospital , Boston, Massachusetts
| | | | - Elissa Ye
- Massachusetts General Hospital , Boston, Massachusetts
| | - Ayush Noori
- Massachusetts General Hospital , Boston, Massachusetts
| | - Colin Magdamo
- Massachusetts General Hospital , Boston, Massachusetts
| | | | | | | | | | - Sudeshna Das
- Massachusetts General Hospital , Boston, Massachusetts
| |
Collapse
|
8
|
Charpignon ML, Vakulenko-Lagun B, Zheng B, Magdamo C, Su B, Evans K, Rodriguez S, Sokolov A, Boswell S, Sheu YH, Somai M, Middleton L, Hyman BT, Betensky RA, Finkelstein SN, Welsch RE, Tzoulaki I, Blacker D, Das S, Albers MW. Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia. Nat Commun 2022; 13:7652. [PMID: 36496454 PMCID: PMC9741618 DOI: 10.1038/s41467-022-35157-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [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: 06/13/2021] [Accepted: 11/21/2022] [Indexed: 12/13/2022] Open
Abstract
Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain.
Collapse
Affiliation(s)
- Marie-Laure Charpignon
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Bang Zheng
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Bowen Su
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Kyle Evans
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Steve Rodriguez
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Sarah Boswell
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Yi-Han Sheu
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Melek Somai
- Inception Labs, Collaborative for Health Delivery Sciences, Medical College of Wisconsin, Wauwatosa, WI, USA
| | - Lefkos Middleton
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
- Public Health Directorate, Imperial College London NHS Healthcare Trust, London, UK
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Rebecca A Betensky
- Department of Biostatistics, School of Global Public Health, New York University, New York, NY, USA
| | - Stan N Finkelstein
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Roy E Welsch
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- Dementia Research Institute, Imperial College London, London, UK.
- Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece.
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
| | - Mark W Albers
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
9
|
Noori A, Magdamo C, Liu X, Tyagi T, Li Z, Kondepudi A, Alabsi H, Rudmann E, Wilcox D, Brenner L, Robbins GK, Moura L, Zafar S, Benson NM, Hsu J, R Dickson J, Serrano-Pozo A, Hyman BT, Blacker D, Westover MB, Mukerji SS, Das S. Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study. J Med Internet Res 2022; 24:e40384. [PMID: 36040790 PMCID: PMC9472045 DOI: 10.2196/40384] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 06/17/2022] [Revised: 07/29/2022] [Accepted: 07/31/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. OBJECTIVE The aim of this study was to evaluate whether natural language processing (NLP)-powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. METHODS In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. RESULTS NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. CONCLUSIONS NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.
Collapse
Affiliation(s)
- Ayush Noori
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Xiao Liu
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Tanish Tyagi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Zhaozhi Li
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Akhil Kondepudi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Haitham Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Emily Rudmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Vaccine and Immunotherapy Center, Division of Infectious Disease, Boston, MA, United States
| | - Douglas Wilcox
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Laura Brenner
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Gregory K Robbins
- Harvard Medical School, Boston, MA, United States
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
| | - Lidia Moura
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Sahar Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Nicole M Benson
- Harvard Medical School, Boston, MA, United States
- Mongan Institute, Massachusetts General Hospital, Boston, MA, United States
- McLean Hospital, Belmont, MA, United States
| | - John Hsu
- Harvard Medical School, Boston, MA, United States
- Mongan Institute, Massachusetts General Hospital, Boston, MA, United States
| | - John R Dickson
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Alberto Serrano-Pozo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Deborah Blacker
- Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| |
Collapse
|
10
|
Kivisäkk P, Magdamo C, Trombetta BA, Noori A, Kuo YKE, Chibnik LB, Carlyle BC, Serrano-Pozo A, Scherzer CR, Hyman BT, Das S, Arnold SE. Plasma biomarkers for prognosis of cognitive decline in patients with mild cognitive impairment. Brain Commun 2022; 4:fcac155. [PMID: 35800899 PMCID: PMC9257670 DOI: 10.1093/braincomms/fcac155] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/11/2022] [Accepted: 06/11/2022] [Indexed: 11/23/2022] Open
Abstract
Plasma-based biomarkers present a promising approach in the research and clinical practice of Alzheimer's disease as they are inexpensive, accessible and minimally invasive. In particular, prognostic biomarkers of cognitive decline may aid in planning and management of clinical care. Although recent studies have demonstrated the prognostic utility of plasma biomarkers of Alzheimer pathology or neurodegeneration, such as pTau-181 and NF-L, whether other plasma biomarkers can further improve prediction of cognitive decline is undetermined. We conducted an observational cohort study to determine the prognostic utility of plasma biomarkers in predicting progression to dementia for individuals presenting with mild cognitive impairment due to probable Alzheimer's disease. We used the Olink™ Proximity Extension Assay technology to measure the level of 460 circulating proteins in banked plasma samples of all participants. We used a discovery data set comprised 60 individuals with mild cognitive impairment (30 progressors and 30 stable) and a validation data set consisting of 21 stable and 21 progressors. We developed a machine learning model to distinguish progressors from stable and used 44 proteins with significantly different plasma levels in progressors versus stable along with age, sex, education and baseline cognition as candidate features. A model with age, education, APOE genotype, baseline cognition, plasma pTau-181 and 12 plasma Olink protein biomarker levels was able to distinguish progressors from stable with 86.7% accuracy (mean area under the curve = 0.88). In the validation data set, the model accuracy was 78.6%. The Olink proteins selected by the model included those associated with vascular injury and neuroinflammation (e.g. IL-8, IL-17A, TIMP-4, MMP7). In addition, to compare these prognostic biomarkers to those that are altered in Alzheimer's disease or other types of dementia relative to controls, we analyzed samples from 20 individuals with Alzheimer, 30 with non-Alzheimer dementias and 34 with normal cognition. The proteins NF-L and PTP-1B were significantly higher in both Alzheimer and non-Alzheimer dementias compared with cognitively normal individuals. Interestingly, the prognostic markers of decline at the mild cognitive impairment stage did not overlap with those that differed between dementia and control cases. In summary, our findings suggest that plasma biomarkers of inflammation and vascular injury are associated with cognitive decline. Developing a plasma biomarker profile could aid in prognostic deliberations and identify individuals at higher risk of dementia in clinical practice.
Collapse
Affiliation(s)
- Pia Kivisäkk
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Colin Magdamo
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Bianca A Trombetta
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Ayush Noori
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Yi kai E Kuo
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Lori B Chibnik
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Becky C Carlyle
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Alberto Serrano-Pozo
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Clemens R Scherzer
- Center for Advanced Parkinson Research and Precision Neurology Program, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Bradley T Hyman
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Sudeshna Das
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Steven E Arnold
- Alzheimer’s Clinical & Translational Research Unit and Massachusetts Alzheimer’s Disease Research Center, Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| |
Collapse
|
11
|
Ge W, Alabsi H, Jain A, Ye E, Sun H, Fernandes M, Magdamo C, Tesh RA, Collens SI, Newhouse A, Mvr Moura L, Zafar S, Hsu J, Akeju O, Robbins GK, Mukerji SS, Das S, Westover MB. Identifying patients with delirium based on unstructured clinical notes. (Preprint). JMIR Form Res 2021; 6:e33834. [PMID: 35749214 PMCID: PMC9270709 DOI: 10.2196/33834] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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] [Received: 09/27/2021] [Revised: 01/22/2022] [Accepted: 02/10/2022] [Indexed: 11/23/2022] Open
Abstract
Background Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate. Objective We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes. Methods We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators. Results The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs –0.028). Conclusions Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.
Collapse
Affiliation(s)
- Wendong Ge
- Massachusetts General Hospital, Boston, MA, United States
| | - Haitham Alabsi
- Massachusetts General Hospital, Boston, MA, United States
| | - Aayushee Jain
- Massachusetts General Hospital, Boston, MA, United States
| | - Elissa Ye
- Massachusetts General Hospital, Boston, MA, United States
| | - Haoqi Sun
- Massachusetts General Hospital, Boston, MA, United States
| | | | - Colin Magdamo
- Massachusetts General Hospital, Boston, MA, United States
| | - Ryan A Tesh
- Massachusetts General Hospital, Boston, MA, United States
| | | | - Amy Newhouse
- Massachusetts General Hospital, Boston, MA, United States
| | | | - Sahar Zafar
- Massachusetts General Hospital, Boston, MA, United States
| | - John Hsu
- Massachusetts General Hospital, Boston, MA, United States
| | | | | | | | - Sudeshna Das
- Massachusetts General Hospital, Boston, MA, United States
| | | |
Collapse
|
12
|
Mukerji SS, Das S, Alabsi H, Brenner LN, Jain A, Magdamo C, Collens SI, Ye E, Keller K, Boutros CL, Leone MJ, Newhouse A, Foy B, Li MD, Lang M, Anahtar MN, Shao YP, Ge W, Sun H, Triant VA, Kalpathy-Cramer J, Higgins J, Rosand J, Robbins GK, Westover MB. Prolonged Intubation in Patients With Prior Cerebrovascular Disease and COVID-19. Front Neurol 2021; 12:642912. [PMID: 33897598 PMCID: PMC8062773 DOI: 10.3389/fneur.2021.642912] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/05/2021] [Indexed: 01/08/2023] Open
Abstract
Objectives: Patients with comorbidities are at increased risk for poor outcomes in COVID-19, yet data on patients with prior neurological disease remains limited. Our objective was to determine the odds of critical illness and duration of mechanical ventilation in patients with prior cerebrovascular disease and COVID-19. Methods: A observational study of 1,128 consecutive adult patients admitted to an academic center in Boston, Massachusetts, and diagnosed with laboratory-confirmed COVID-19. We tested the association between prior cerebrovascular disease and critical illness, defined as mechanical ventilation (MV) or death by day 28, using logistic regression with inverse probability weighting of the propensity score. Among intubated patients, we estimated the cumulative incidence of successful extubation without death over 45 days using competing risk analysis. Results: Of the 1,128 adults with COVID-19, 350 (36%) were critically ill by day 28. The median age of patients was 59 years (SD: 18 years) and 640 (57%) were men. As of June 2nd, 2020, 127 (11%) patients had died. A total of 177 patients (16%) had a prior cerebrovascular disease. Prior cerebrovascular disease was significantly associated with critical illness (OR = 1.54, 95% CI = 1.14-2.07), lower rate of successful extubation (cause-specific HR = 0.57, 95% CI = 0.33-0.98), and increased duration of intubation (restricted mean time difference = 4.02 days, 95% CI = 0.34-10.92) compared to patients without cerebrovascular disease. Interpretation: Prior cerebrovascular disease adversely affects COVID-19 outcomes in hospitalized patients. Further study is required to determine if this subpopulation requires closer monitoring for disease progression during COVID-19.
Collapse
Affiliation(s)
- Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Haitham Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Laura N Brenner
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Elissa Ye
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Kiana Keller
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Christine L Boutros
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Michael J Leone
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Amy Newhouse
- Harvard Medical School, Boston, MA, United States
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Brody Foy
- Department of Pathology, Massachusetts General Hospital, Boston, MA, United States
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Matthew D Li
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Min Lang
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Melis N Anahtar
- Department of Pathology, Massachusetts General Hospital, Boston, MA, United States
| | - Yu-Ping Shao
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Wendong Ge
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Virginia A Triant
- Harvard Medical School, Boston, MA, United States
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
- The Mongan Institute, Massachusetts General Hospital, Boston, MA, United States
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - John Higgins
- Department of Pathology, Massachusetts General Hospital, Boston, MA, United States
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Gregory K Robbins
- Harvard Medical School, Boston, MA, United States
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, United States
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
- Clinical Data A.I. Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| |
Collapse
|
13
|
Sun H, Jain A, Leone MJ, Alabsi HS, Brenner LN, Ye E, Ge W, Shao YP, Boutros CL, Wang R, Tesh RA, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu JT, Dougan ML, Stratton LW, Rosand J, Fischl B, Das S, Mukerji SS, Robbins GK, Westover MB. CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis. J Infect Dis 2021; 223:38-46. [PMID: 33098643 PMCID: PMC7665643 DOI: 10.1093/infdis/jiaa663] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [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: 06/26/2020] [Accepted: 10/19/2020] [Indexed: 01/08/2023] Open
Abstract
Background We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. Methods We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March–2 May) and prospective (n = 2205, 3–14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). Results In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. Conclusions CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection.
Collapse
Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Haitham S Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Laura N Brenner
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Christine L Boutros
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ruopeng Wang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ingrid V Bassett
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - James B Meigs
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Matthew D Li
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Jacqueline T Chu
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.,MGH Chelsea HealthCare Center, Chelsea, Massachusetts, USA
| | - Michael L Dougan
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lawrence W Stratton
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce Fischl
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA.,Massachusetts Institute of Technology Health Sciences & Technology Program/Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Gregory K Robbins
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| |
Collapse
|
14
|
Sun H, Jain A, Leone MJ, Alabsi HS, Brenner LN, Ye E, Ge W, Shao YP, Boutros CL, Wang R, Tesh RA, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu JT, Dougan ML, Stratton LW, Rosand J, Fischl B, Das S, Mukerji SS, Robbins GK, Westover MB. CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis. J Infect Dis 2021. [PMID: 33098643 DOI: 10.1093/infdis/jiaa663/5938525] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. METHODS We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March-2 May) and prospective (n = 2205, 3-14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). RESULTS In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection.
Collapse
Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Haitham S Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Laura N Brenner
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Christine L Boutros
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ruopeng Wang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Ingrid V Bassett
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - James B Meigs
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Matthew D Li
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Jacqueline T Chu
- Harvard Medical School, Boston, Massachusetts, USA.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.,MGH Chelsea HealthCare Center, Chelsea, Massachusetts, USA
| | - Michael L Dougan
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Lawrence W Stratton
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce Fischl
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA.,Massachusetts Institute of Technology Health Sciences & Technology Program/Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Shibani S Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Gregory K Robbins
- Harvard Medical School, Boston, Massachusetts, USA.,Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| |
Collapse
|
15
|
Trombetta BA, Kivisäkk P, Carlyle BC, Magdamo C, Merrill E, Chibnik LB, Serrano‐Pozo A, Hyman BT, Das S, Arnold SE. Plasma biomarkers of neuroinflammation and vascular injury predict cognitive decline in patients with mild cognitive impairment. Alzheimers Dement 2020. [DOI: 10.1002/alz.046134] [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: 11/09/2022]
Affiliation(s)
- Bianca A. Trombetta
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Pia Kivisäkk
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Becky C. Carlyle
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Colin Magdamo
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Emily Merrill
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Lori B. Chibnik
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
- Harvard T.H. Chan School of Public Health Boston MA USA
| | - Alberto Serrano‐Pozo
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Bradley T. Hyman
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Sudeshna Das
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Steven E. Arnold
- MassGeneral Institute for Neurodegenerative Diseases Charlestown MA USA
- Massachusetts General Hospital Harvard Medical School Boston MA USA
| |
Collapse
|
16
|
Conklin J, Frosch MP, Mukerji S, Rapalino O, Maher M, Schaefer PW, Lev MH, Gonzalez RG, Das S, Champion SN, Magdamo C, Sen P, Harrold GK, Alabsi H, Normandin E, Shaw B, Lemieux J, Sabeti P, Branda JA, Brown EN, Westover MB, Huang SY, Edlow BL. Cerebral Microvascular Injury in Severe COVID-19. medRxiv 2020. [PMID: 32743599 DOI: 10.1101/2020.07.21.20159376] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
IMPORTANCE Microvascular lesions are common in patients with severe COVID-19. Radiologic-pathologic correlation in one case suggests a combination of microvascular hemorrhagic and ischemic lesions that may reflect an underlying hypoxic mechanism of injury, which requires validation in larger studies. OBJECTIVE To determine the incidence, distribution, and clinical and histopathologic correlates of microvascular lesions in patients with severe COVID-19. DESIGN Observational, retrospective cohort study: March to May 2020. SETTING Single academic medical center. PARTICIPANTS Consecutive patients (16) admitted to the intensive care unit with severe COVID-19, undergoing brain MRI for evaluation of coma or focal neurologic deficits. EXPOSURES Not applicable. MAIN OUTCOME AND MEASURES Hypointense microvascular lesions identified by a prototype ultrafast high-resolution susceptibility-weighted imaging (SWI) MRI sequence, counted by two neuroradiologists and categorized by neuroanatomic location. Clinical and laboratory data (most recent measurements before brain MRI). Brain autopsy and cerebrospinal fluid PCR for SARS-CoV 2 in one patient who died from severe COVID-19. RESULTS Eleven of 16 patients (69%) had punctate and linear SWI lesions in the subcortical and deep white matter, and eight patients (50%) had >10 SWI lesions. In 4/16 patients (25%), lesions involved the corpus callosum. Brain autopsy in one patient revealed that SWI lesions corresponded to widespread microvascular injury, characterized by perivascular and parenchymal petechial hemorrhages and microscopic ischemic lesions. CONCLUSIONS AND RELEVANCE SWI lesions are common in patients with neurological manifestations of severe COVID-19 (coma and focal neurologic deficits). The distribution of lesions is similar to that seen in patients with hypoxic respiratory failure, sepsis, and disseminated intravascular coagulation. Collectively, these radiologic and histopathologic findings suggest that patients with severe COVID-19 are at risk for multifocal microvascular hemorrhagic and ischemic lesions in the subcortical and deep white matter.
Collapse
|
17
|
Sun H, Jain A, Leone MJ, Alabsi HS, Brenner L, Ye E, Ge W, Shao YP, Boutros C, Wang R, Tesh R, Magdamo C, Collens SI, Ganglberger W, Bassett IV, Meigs JB, Kalpathy-Cramer J, Li MD, Chu J, Dougan ML, Stratton L, Rosand J, Fischl B, Das S, Mukerji S, Robbins GK, Westover MB. COVID-19 Outpatient Screening: a Prediction Score for Adverse Events. medRxiv 2020:2020.06.17.20134262. [PMID: 32607523 PMCID: PMC7325189 DOI: 10.1101/2020.06.17.20134262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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] [Indexed: 01/08/2023]
Abstract
BACKGROUND We sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak. METHODS Single-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed event ratio (E/O). Discrimination was assessed by C-statistics (AUC). RESULTS In the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS CoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.
Collapse
Affiliation(s)
- Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Haitham S Alabsi
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Laura Brenner
- Harvard Medical School, Boston, MA
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | | | - Ruopeng Wang
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
| | - Ryan Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Sarah I Collens
- Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| | - Ingrid V Bassett
- Harvard Medical School, Boston, MA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA
| | - James B Meigs
- Harvard Medical School, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Jayashree Kalpathy-Cramer
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
| | - Matthew D Li
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
| | - Jacqueline Chu
- Harvard Medical School, Boston, MA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
- MGH Chelsea HealthCare Center, Chelsea, MA
| | - Michael L Dougan
- Harvard Medical School, Boston, MA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA
| | - Lawrence Stratton
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Bruce Fischl
- Harvard Medical School, Boston, MA
- Department of Radiology, Massachusetts General Hospital, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA
- MIT HST/CSAIL, Cambridge, MA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Shibani Mukerji
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Gregory K Robbins
- Harvard Medical School, Boston, MA
- Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA
| |
Collapse
|
18
|
Rodriguez S, Cao L, Rickenbacher GT, Benz EG, Magdamo C, Gomez LR, Holbrook EH, Albers AD, Gallagher R, Westover MB, Evans KE, Tatar DJ, Mukerji S, Zafonte R, Boyer EW, Yu CR, Albers MW. Innate immune signaling in the olfactory epithelium reduces odorant receptor levels: modeling transient smell loss in COVID-19 patients. medRxiv 2020:2020.06.14.20131128. [PMID: 32587994 PMCID: PMC7310652 DOI: 10.1101/2020.06.14.20131128] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [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: 02/06/2023]
Abstract
Post-infectious anosmias typically follow death of olfactory sensory neurons (OSNs) with a months-long recovery phase associated with parosmias. While profound anosmia is the leading symptom associated with COVID-19 infection, many patients regain olfactory function within days to weeks without distortions. Here, we demonstrate that sterile induction of anti-viral type I interferon signaling in the mouse olfactory epithelium is associated with diminished odor discrimination and reduced odor-evoked local field potentials. RNA levels of all class I, class II, and TAAR odorant receptors are markedly reduced in OSNs in a non-cell autonomous manner. We find that people infected with COVID-19 rate odors with lower intensities and have odor discrimination deficits relative to people that tested negative for COVID-19. Taken together, we propose that inflammatory-mediated loss of odorant receptor expression with preserved circuit integrity accounts for the profound anosmia and rapid recovery of olfactory function without parosmias caused by COVID-19.
Collapse
Affiliation(s)
- Steven Rodriguez
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| | - Luxiang Cao
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| | | | - Eric G. Benz
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| | - Colin Magdamo
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| | | | - Eric H. Holbrook
- Dept. of Otolaryngology—Head and Neck Surgery, Mass. Eye and Ear, Boston, MA 02114
| | - Alefiya D. Albers
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
- Dept. of Psychology, Endicott College, Beverly, MA 01915
| | - Rose Gallagher
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| | | | - Kyle E. Evans
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| | | | - Shibani Mukerji
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| | - Ross Zafonte
- Dept. of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA 02129
| | - Edward W Boyer
- Dept. of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA 02115
| | - C. Ron Yu
- Stowers Institute of Medical Research, Kansas City, MO 64110
| | - Mark W. Albers
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA 02129
| |
Collapse
|
19
|
Mezlini AM, Magdamo C, Merrill E, Chibnik LB, Blacker DL, Hyman BT, Das S. Characterizing Clinical and Neuropathological Traits of APOE Haplotypes in African Americans and Europeans. J Alzheimers Dis 2020; 78:467-477. [PMID: 33016904 PMCID: PMC7774865 DOI: 10.3233/jad-200228] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND The APOEɛ4 allele is the largest genetic risk factor for late-onset Alzheimer's disease (AD). Recent literature suggested that the contribution of APOEɛ4 to AD risk could be population-specific, with ɛ4 conferring a lower risk to Blacks or African Americans. OBJECTIVE To investigate the effect of APOE haplotypes on AD risk in individuals with European ancestry (EU) and Blacks or African Americans (AA). METHODS We selected data from 1) the National Alzheimer's Coordinating Center: a total of 3,486 AD cases and 4,511 controls (N = 7,997, 60% female) with genotypes from the Alzheimer's Disease Genetics Consortium (ADGC), and 2) the Rush University Religious Orders Study and Memory and Aging Project (ROSMAP) cohort with 578 AD and 670 controls (N = 1,248, 60% female). Using ɛ3 homozygotes as the reference, we compared the association of various APOE haplotypes with the clinical and neuropathological correlates of dementia in AA and EU. RESULTS In both cohorts, we find no difference in the odds or age of onset of AD among the ɛ4-linked haplotypes defined by rs769449 within either AA or EU. Additionally, while APOEɛ4 was associated with a faster rate of decline, no differences were found in rate of decline, clinical or neuropathological features among the ɛ4-linked haplotypes. Further analysis with other variants near the APOE locus failed to identify any effect modification. CONCLUSION Our study finds similar effects of the ɛ4-linked haplotypes defined by rs769449 on AD as compared to ɛ3 in both AA and EU. Future studies are required to understand the heterogeneity of APOE conferred risk of AD among various genotypes and populations.
Collapse
Affiliation(s)
- Aziz M. Mezlini
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Colin Magdamo
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA
| | - Emily Merrill
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA
| | - Lori B. Chibnik
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deborah L. Blacker
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bradley T. Hyman
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| |
Collapse
|