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

Tellen D. Bennett, Richard A. Moffitt, Janos G. Hajagos, Benjamin Amor, Adit Anand, Mark M. Bissell, Katie Rebecca Bradwell, Carolyn Bremer, James Brian Byrd, Alina Denham, Peter E. DeWitt, Davera Gabriel, Brian T. Garibaldi, Andrew T. Girvin, Justin Guinney, Elaine L. Hill, Stephanie S. Hong, Hunter Jimenez, Ramakanth Kavuluru, Kristin KostkaHarold P. Lehmann, Eli Levitt, Sandeep K. Mallipattu, Amin Manna, Julie A. McMurry, Michele Morris, John Muschelli, Andrew J. Neumann, Matvey B. Palchuk, Emily R. Pfaff, Zhenglong Qian, Nabeel Qureshi, Seth Russell, Heidi Spratt, Anita Walden, Andrew E. Williams, Jacob T. Wooldridge, Yun Jae Yoo, Xiaohan Tanner Zhang, Richard L. Zhu, Christopher P. Austin, Joel H. Saltz, Ken R. Gersing, Melissa A. Haendel, Christopher G. Chute

Research output: Contribution to journalArticlepeer-review

152 Scopus citations


IMPORTANCE The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity 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, patientswere stratified using aWorld 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 theWorld 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.

Original languageEnglish (US)
Pages (from-to)E2116901
JournalJAMA network open
Issue number7
StatePublished - Jul 13 2021
Externally publishedYes

ASJC Scopus subject areas

  • General Medicine


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