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How well can sociodemographic features, laboratory values, and comorbidities of individuals hospitalized with coronavirus disease 2019 (COVID-19) in Eastern Massachusetts through June 5, 2020, predict a severe illness course?
In this cohort study of 2511 hospitalized individuals positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by polymerase chain reaction who were admitted to 1 of 6 hospitals, 215 (8.6%) were admitted to the instensive care unit, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. In a risk prediction model, 212 deaths (78%) occurred in the top mortality-risk quintile.
Simple prediction models may assist in risk stratification among hospitalized patients with COVID-19.
The coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse hospital outcomes are needed.
To quantify admission laboratory and comorbidity features associated with critical illness and mortality risk across 6 Eastern Massachusetts hospitals.
Design, Setting, and Participants
Retrospective cohort study of all individuals admitted to the hospital who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by polymerase chain reaction across these 6 hospitals through June 5, 2020, using hospital course, prior diagnoses, and laboratory values in emergency department and inpatient settings from 2 academic medical centers and 4 community hospitals. The data were extracted on June 11, 2020, and the analysis was conducted from June to July 2020.
Main Outcomes and Measures
Severe illness defined by admission to intensive care unit, mechanical ventilation, or death.
Of 2511 hospitalized individuals who tested positive for SARS-CoV-2 (of whom 50.9% were male, 53.9% White, and 27.0% Hispanic, with a mean [SD ]age of 62.6 [19.0] years), 215 (8.6%) were admitted to the intensive care unit, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded an area under the receiver operating characteristic curve of 0.807 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 212 of 292 deaths (72.6%) occurred in the highest-risk mortality quintile.
Conclusions and Relevance
In this cohort, specific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitated risk stratification among individuals hospitalized for COVID-19.
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Accepted for Publication: September 2, 2020.
Published: October 30, 2020. doi:10.1001/jamanetworkopen.2020.23934
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Castro VM et al. JAMA Network Open.
Corresponding Author: Roy H. Perlis, MD, MSc, Center for Quantitative Health, Division of Clinical Research, Massachusetts General Hospital, 185 Cambridge St, 6th Floor, Boston, MA 02114 (firstname.lastname@example.org).
Author Contributions: Dr Perlis had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: All authors.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Castro, McCoy.
Administrative, technical, or material support: All authors.
Conflict of Interest Disclosures: Dr Perlis has received consulting fees from Burrage Capital, Genomind, RID Ventures, and Takeda. He holds equity in Outermost Therapeutics and Psy Therapeutics. Dr McCoy has received research funding from the Stanley Center at the Broad Institute, the Brain and Behavior Research Foundation, National Institute of Mental Health, National Human Genome Research Institute Home, and Telefonica Alfa. No other disclosures were reported.
Disclaimer: Dr Perlis is an Associate Editor at JAMA Network Open; however, he had no role in the editorial review or decision to accept the manuscript for publication.
Additional Contributions: The authors would like to thank the Partners Research Patient Data Registry and Analytics Enclave team members for their support in making up-to-date electronic health record data available for this research. No financial compensation was provided specifically for this work.
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