Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19

Parums, Dinah V.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pneumonia. However, COVID-19 is now recognized to involve multiple organ systems with varying severity and duration. In July 2021, the findings from a retrospective population study from the National COVID Cohort Collaborative (N3C) Consortium were published that included analysis by machine learning methods of 174,568 adults with SARS-CoV-2 infection from 34 medical centers in the US. The study stratified patients for COVID-19 according to the World Health Organization (WHO) Clinical Progression Scale (CPS). Severe clinical outcomes were identified as the requirement for invasive ventilatory support, or extracorporeal membrane oxygenation (ECMO), and patient mortality. Machine learning analysis showed that the factor most strongly associated with severity of clinical course in patients with COVID-19 was pH. A separate multivariable logistic regression model showed that independent factors associated with more severe clinical outcomes included age, dementia, male gender, liver disease, and obesity. This Editorial aims to present the rationale and findings of the largest population cohort of adult patients with COVID-19 to date and highlights the importance of using large population studies with sophisticated analytical methods, including machine learning.