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Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME
Key Points

Question  In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity?

Findings  In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020.

Meaning  These findings suggest that machine learning models can be used to predict COVID-19 clinical severity with the use of an available large-scale US COVID-19 data resource.

Abstract

Importance  The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.

Objectives  To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity.

Design, Setting, and Participants  In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation).

Main Outcomes and Measures  Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression.

Results  The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity.

Conclusions and Relevance  This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

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Article Information

Accepted for Publication: May 3, 2021.

Published: July 13, 2021. doi:10.1001/jamanetworkopen.2021.16901

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Bennett TD et al. JAMA Network Open.

Corresponding Authors: Tellen D. Bennett, MD, Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, 13199 E Montview Blvd, Ste 300, Aurora, CO 80045 (tell.bennett@cuanschutz.edu); Christopher G. Chute, MD, Johns Hopkins University, 2024 E Monument St, Baltimore, MD 21287 (chute@jhu.edu).

Author Contributions: Drs Bennett and Chute had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Bennett, Moffitt, Hajagos, Garibaldi, Girvin, Hill, Hong, Lehmann, Mallipattu, Neumann, Qureshi, Russell, Spratt, Williams, Zhang, Austin, Saltz, Haendel, Chute.

Acquisition, analysis, or interpretation of data: Bennett, Moffitt, Hajagos, Amor, Anand, Bissell, Bradwell, Bremer, Byrd, Denham, DeWitt, Gabriel, Garibaldi, Girvin, Guinney, Hill, Hong, Jimenez, Kavuluru, Kostka, Lehmann, Levitt, Mallipattu, Manna, McMurry, Morris, Muschelli, Palchuk, Pfaff, Qian, Qureshi, Russell, Walden, Wooldridge, Yoo, Zhang, Zhu, Saltz, Gersing, Haendel, Chute.

Drafting of the manuscript: Bennett, Moffitt, Hajagos, Anand, Bissell, Bremer, Byrd, Denham, DeWitt, Garibaldi, Guinney, Jimenez, Lehmann, Levitt, Mallipattu, Manna, McMurry, Neumann, Pfaff, Qian, Qureshi, Russell, Walden, Yoo, Zhu, Saltz, Haendel, Chute.

Critical revision of the manuscript for important intellectual content: Bennett, Moffitt, Amor, Bradwell, Byrd, Denham, Gabriel, Garibaldi, Girvin, Guinney, Hill, Hong, Kavuluru, Kostka, Levitt, Mallipattu, Morris, Muschelli, Neumann, Palchuk, Pfaff, Qureshi, Russell, Spratt, Walden, Williams, Wooldridge, Zhang, Austin, Saltz, Gersing, Haendel, Chute.

Statistical analysis: Bennett, Moffitt, Hajagos, Anand, Denham, DeWitt, Garibaldi, Girvin, Jimenez, Kavuluru, Levitt, Manna, Qian, Qureshi, Russell, Wooldridge, Yoo, Zhang.

Obtained funding: McMurry, Walden, Gersing, Haendel, Chute.

Administrative, technical, or material support: Hajagos, Amor, Bissell, Bradwell, Byrd, Denham, Gabriel, Girvin, Guinney, Hong, Kostka, Lehmann, Manna, McMurry, Morris, Neumann, Palchuk, Pfaff, Qureshi, Russell, Spratt, Walden, Williams, Zhang, Zhu, Austin, Saltz, Gersing, Haendel, Chute.

Supervision: Bennett, Moffitt, Hill, Qureshi, Austin, Saltz, Gersing, Haendel, Chute.

Conflict of Interest Disclosures: Dr Bennett reported receiving grants from the National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) during the conduct of the study and grants from the NIH/Eunice Kennedy Shriver National Institute of Child Health and Human Development and NIH/National Institute of Allergy and Infectious Diseases outside the submitted work. Dr Moffitt reported receiving grants from the NIH during the conduct of the study. Dr Hajagos reported receiving grants from the NIH/NCATS during the conduct of the study. Dr Amor reported receiving commercial payment from the NCATS during the conduct of the study. Mr Bissell reported being employed by Palantir Technologies during the conduct of the study. Dr Bradwell reported being employed by Palantir Technologies during the conduct of the study and outside the submitted work. Dr Byrd reported receiving grants from the NIH/National Heart, Lung, and Blood Institute during the conduct of the study. Ms Gabriel reported receiving grants from the NIH/NCATS during the conduct of the study. Dr Garibaldi reported receiving personal fees from Janssen Development LLC and from the US Food and Drug Administration Pulmonary-Asthma Drug Advisory Committee outside the submitted work. Dr Girvin reported being an employee of Palantir Technologies. Dr Kavuluru reported receiving grants from the NIH/NCATS during the conduct of the study. Ms Kostka reported receiving an N3C subaward from Johns Hopkins University during the conduct of the study and is an employee of IQVIA. Dr Lehmann reported receiving grants from the NIH during the conduct of the study. Mr Manna reported receiving personal fees from Palantir Technologies Inc during the conduct of the study. Ms McMurry reported being a cofounder of Pryzm Health outside the submitted work. Dr Pfaff reported receiving grants from NIH/NCATS during the conduct of the study. Mr Qureshi reported being an employee of Palantir Technologies during the conduct of the study. Dr Haendel reported receiving grants from the NIH during the conduct of the study. Dr Chute reported receiving grants from the NIH/NCATS during the conduct of the study. No other disclosures were reported.

Funding/Support: The primary study sponsors are multiple institutes of the National Institutes of Health. The NCATS is the primary steward of the N3C data, created the underlying architecture of the N3C Data Enclave, manages the Data Transfer Agreements and Data Use Agreements, houses the Data Access Committee, and supports contracts to vendors (see Conflicts of Interest Disclosures) to help build various aspects of the N3C Data Enclave. The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave and supported by NCATS U24 TR002306 (Drs Haendel, Guinney, Chute, Saltz, and Williams; also supporting Dr Pfaff, Ms Walden, Ms McMurry, Mr Neumann, Ms Gabriel, and Dr Lehmann).This study was also supported by the following (institutions with release data): grants U24TR002306 (Stony Brook University), U54GM104938 (Oklahoma Clinical and Translational Science Institute, University of Oklahoma Health Sciences Center), U54GM104942 (West Virginia Clinical and Translational Science Institute, West Virginia University), U54GM115428 (Mississippi Center for Clinical and Translational Research, University of Mississippi Medical Center), U54GM115458 (Great Plains IDeA-Clinical & Translational Research, University of Nebraska Medical Center), U54GM115516 (Northern New England Clinical & Translational Research) Network, Maine Medical Center), UL1TR001420 (Wake Forest Clinical and Translational Science Institute, Wake Forest University Health Sciences), UL1TR001422 (Northwestern University Clinical and Translational Science Institute, Northwestern University)), UL1TR001425 (Center for Clinical and Translational Science and Training, University of Cincinnati), UL1TR001439 (Institute for Translational Sciences, University of Texas Medical Branch at Galveston), UL1TR001450 (South Carolina Clinical & Translational Research Institute, Medical University of South Carolina), UL1TR001453 (UMass Center for Clinical and Translational Science, University of Massachusetts Medical School Worcester), UL1TR001855 (Southern California Clinical and Translational Science Institute, University of Southern California), UL1TR001873 (Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center), UL1TR001876 (Clinical and Translational Science Institute at Children's National, George Washington Children's Research Institute), UL1TR001998 (Appalachian Translational Research Network, University of Kentucky), (University of Rochester Clinical & Translational Science Institute), UL1TR002003 (University of Illinois at Chicago Center for Clinical and Translational Science), UL1TR002014 (Penn State Clinical and Translational Science Institute), UL1TR002240 (Michigan Institute for Clinical and Health Research, University of Michigan at Ann Arbor), UL1TR002243 (Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center), UL1TR002319 (Institute of Translational Health Sciences, University of Washington), UL1TR002345 (Institute of Clinical and Translational Sciences, Washington University in St. Louis), UL1TR002369 (Oregon Clinical and Translational Research Institute, Oregon Health & Science University), UL1TR002373 (Wisconsin Network For Health Research, University of Wisconsin–Madison), UL1TR002389 (Institute for Translational Medicine, Rush University Medical Center), UL1TR002389 (Institute for Translational Medicine, University of Chicago), UL1TR002489 (North Carolina Translational and Clinical Science Institute, University of North Carolina at Chapel Hill), UL1TR002494 (Clinical and Translational Science Institute, University of Minnesota), UL1TR002535 (Colorado Clinical and Translational Sciences Institute and Children's Hospital Colorado), UL1TR002537 and UL1TR002535 03S2 (Institute for Clinical and Translational Science, University of Iowa), UL1TR002538 (Uhealth Center for Clinical and Translational Science, University of Utah), UL1TR002544 (Tufts Clinical and Translational Science Institute, Tufts Medical Center), UL1TR002553 (Duke Clinical and Translational Science Institute, Duke University), UL1TR002649 (C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University), UL1TR002733 (Center for Clinical and Translational Science, Ohio State University) UL1TR002736 (University of Miami Clinical and Translational Science Institute), UL1TR003015 (iTHRIVL Integrated Translational health Research Institute of Virginia, University of Virginia), UL1TR003015 (iTHRIVL Integrated Translational health Research Institute of Virginia, Carilion Clinic), UL1TR003096 (Center for Clinical and Translational Science, University of Alabama at Birmingham), UL1TR003098 (Johns Hopkins Institute for Clinical and Translational Research, Johns Hopkins University), UL1TR003107 (Consortium of Rural States, University of Arkansas for Medical Sciences), U54GM104941 (Delaware CTR ACCEL Program, Nemours), UL1TR002535 (Colorado Clinical and Translational Sciences Institute, University of Colorado, Denver, Anschutz Medical Campus), UL1TR002377 (Mayo Clinic Center for Clinical and Translational Science, Mayo Clinic Rochester), UL1TR002389 (Institute for Translational Medicine, Loyola University Medical Center), and UL1TR002389 (Institute for Translational Medicine, Advocate Health Care Network). Additional support (data release pending) as provided as follows: UL1TR001866 (Center for Clinical and Translational Science, Rockefeller University), UL1TR002550 (Scripps Research Translational Institute, The Scripps Research Institute), UL1TR002645 (Institute for Integration of Medicine and Science, University of Texas Health Science Center at San Antonio), UL1TR003167 (Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston), UL1TR002389 (Institute for Translational Medicine, NorthShore University HealthSystem), UL1TR001863 (Yale Center for Clinical Investigation, Yale New Haven Hospital), UL1TR002378 (Georgia Clinical and Translational Science Alliance, Emory University), UL1TR002384 (Weill Cornell Medicine Clinical and Translational Science Center, Weill Medical College of Cornell University), UL1TR002556 (Institute for Clinical and Translational Research at Einstein and Montefiore, Montefiore Medical Center), UL1TR001436 (Clinical and Translational Science Institute of Southeast Wisconsin, Medical College of Wisconsin), UL1TR001449 (University of New Mexico Clinical and Translational Science Center, University of New Mexico Health Sciences Center), UL1TR001876 (Clinical and Translational Science Institute at Children's National, George Washington University), UL1TR003142 (Spectrum: The Stanford Center for Clinical and Translational Research and Education, Stanford University), UL1TR002529 (Indiana Clinical and Translational Science Institute, Regenstrief Institute), UL1TR001425 (Center for Clinical and Translational Science and Training, Cincinnati Children's Hospital Medical Center), UL1TR001430 (Boston University Clinical and Translational Science Institute, Boston University Medical Campus), U54GM104940 (Louisiana Clinical and Translational Science Center, University Medical Center New Orleans), UL1TR001412 (Clinical and Translational Science Institute, The State University of New York at Buffalo), UL1TR002373 (Wisconsin Network For Health Research, Aurora Health Care), U54GM115677 (Advance Clinical Translational Research, Brown University), UL1TR003017 (New Jersey Alliance for Clinical and Translational Science, Rutgers, The State University of New Jersey), UL1TR002389 (Institute for Translational Medicine, Loyola University Chicago), UL1TR001445 (Langone Health's Clinical and Translational Science Institute, New York University Grossman School of Medicine), UL1TR001878 (Institute for Translational Medicine and Therapeutics, Children's Hospital of Philadelphia), UL1TR002366 (Frontiers: University of Kansas Clinical and Translational Science Institute, University of Kansas Medical Center), UL1TR002541 (Harvard Catalyst, Massachusetts General Brigham), INV-018455 (Bill and Melinda Gates Foundation grant to Sage Bionetworks, OCHIN), UL1TR001433 (ConduITS Institute for Translational Sciences, Icahn School of Medicine at Mount Sinai), U54GM104940 (Louisiana Clinical and Translational Science Center, Ochsner Medical Center), UL1TR001414 (University of California, Irvine Institute for Clinical and Translational Science), UL1TR001442 (Altman Clinical and Translational Research Institute, University of California, San Diego), UL1TR001860 (UCDavis Health Clinical and Translational Science Center, University of California, Davis), UL1TR001872 (University of California, San Francisco Clinical and Translational Science Institute), and UL1TR001881 (UCLA Clinical Translational Science Institute).

Role of the Funder/Sponsor: Employees of the NIH and of the contracting companies contributed to the design and conduct of the study; collection, management, analysis, and interpretation of the data; review and approval of the manuscript; and decision to submit the manuscript for publication. They are included as authors of the manuscript. Please see the author contribution section for details.

Group Information: National COVID Cohort Collaborative (N3C) Consortium members are listed in Supplement 2.

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