TY - JOUR
T1 - Risk factors for severe COVID-19 differ by age for hospitalized adults
AU - Molani, Sevda
AU - Hernandez, Patricia V.
AU - Roper, Ryan T.
AU - Duvvuri, Venkata R.
AU - Baumgartner, Andrew M.
AU - Goldman, Jason D.
AU - Ertekin-Taner, Nilüfer
AU - Funk, Cory C.
AU - Price, Nathan D.
AU - Rappaport, Noa
AU - Hadlock, Jennifer J.
N1 - Funding Information:
JJH and SM have received grant funding from Pfizer for research unrelated to this work. JDG declared contracted research with Gilead, Lilly, and Regeneron, and fees from Gilead and Lilly for speaking and advisory board. JJH declared grant funding from Pfizer for COVID-19 research unrelated to this study. None of the other authors declare a competing interest with this study.
Funding Information:
We are grateful to Providence St. Joseph Health for sharing their data engineering expertise and computational resources. We would also like to acknowledge SNOMED International for developing and maintaining SNOMED-CT.
Funding Information:
This work was funded by NIH NIA Grant 2U01AG046139-06 (to NDP, NET). JJH and VRD have been funded in part with Federal funds from the Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority, under Contract No. HHSO100201600031C, administered by Merck, Inc., on work unrelated to this study. NET is also funded by NIH NIA R01 AG061796. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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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U2 - 10.1038/s41598-022-10344-3
DO - 10.1038/s41598-022-10344-3
M3 - Article
C2 - 35484176
AN - SCOPUS:85128940471
SN - 2045-2322
VL - 12
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 6568
ER -