Aggregating Electronic Health Record Data for COVID-19 Research—Caveat Emptor

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.
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
This cohort study evaluates COVID-19 severity and factors associated with severity over time and assesses the use of machine learning to predict clinical severity.