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Abstract
Risk stratification for hospitalized adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments. This retrospective study analyzed data from 6906 hospitalized adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.81 (95% CI 0.71–0.91) and models for age ≥ 50 years reached AUROC 0.82 (95% CI 0.77–0.86). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients including age, BMI, vital signs, and laboratory results. In addition, for hospitalized patients, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results.
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Details
1 Institute for Systems Biology, Seattle, USA (GRID:grid.64212.33) (ISNI:0000 0004 0463 2320)
2 Institute for Systems Biology, Seattle, USA (GRID:grid.64212.33) (ISNI:0000 0004 0463 2320); Washington University School of Medicine, St. Louis, USA (GRID:grid.4367.6) (ISNI:0000 0001 2355 7002)
3 Swedish Center for Research and Innovation, Seattle, USA (GRID:grid.64212.33); Providence St. Joseph Health, Renton, USA (GRID:grid.64212.33); University of Washington, Division of Allergy & Infectious Diseases, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
4 Mayo Clinic Jacksonville, Department of Neuroscience, Department of Neurology, Jacksonville, USA (GRID:grid.417467.7) (ISNI:0000 0004 0443 9942)
5 Institute for Systems Biology, Seattle, USA (GRID:grid.64212.33) (ISNI:0000 0004 0463 2320); Onegevity, a Division of Thorne HealthTech, New York, USA (GRID:grid.64212.33)