Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study

This study identifies clinical data and laboratory biomarkers of COVID-19 severity and mortality using machine learning models. The study identifies SpO<sub>2</sub>/FiO<sub>2</sub> ratio to be the most important predictor for both severity and mortality. Several biomarkers were identified to modestly improve the predictions. The results also provide a baseline of SARS-CoV-2 infection during the early stages of the coronavirus emergence and can serve as a baseline for future studies that inform how the genetic evolution of the coronavirus affects the host response to new variants.

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Publication Details

Journal
Frontiers in Medicine
Published
2025-07-04
DOI
https://doi.org/10.3389/fmed.2025.1604388
Primary Topic
COVID-19 Clinical Research Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study

Grace A. McComsey, Walter L. Eckalbar, Joann Diray‐Arce, Hanno Steen et al.
Frontiers in Medicine
COVID-19 Clinical Research Studies
article

Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study

Grace A. McComsey, Walter L. Eckalbar, Joann Diray‐Arce, Hanno Steen, Charles Langelier, David A. Hafler, Leying Guan, Lauren I. R. Ehrlich, David J. Erle, Chris Bime, Ruth R. Montgomery, Annmarie Hoch, William B. Messer, Viviana Simon, Esther Melamed, Mary Ann Comunale, Albert C. Shaw, Michael R. Wilson, Mark M. Davis, Jintong Hou, Kari Christine Nadeau, Jordan P. Metcalf, Bali Pulendran, Rafick‐Pierre Sékaly, Charles B. Cairns, Nadine Rouphael, Bjoern Peters, Steven E. Bosinger, Farrah Kheradmand, Catherine L. Hough, Ana Fernandez Sesma, Elaine F. Reed, Lindsey B. Rosen, Mark A. Atkinson, Joanna Schaenman, Carolyn S. Calfee, Steven H. Kleinstein, Ofer Levy, Holden T. Maecker, Benjamin Haslund-Gourley, Patrice M. Becker, David B. Corry, Nelson Iván Agudelo Higuita, Elias K. Haddad, Al Ozonoff, Scott C. Brakenridge, Alison D. Augustine, Lindsey Robert Baden, Impacc Network, Monica Kraft, Seung Hee Kim, Florian Krammer, Matt Altman, Lucy Robinson
article en

Abstract

This study identifies clinical data and laboratory biomarkers of COVID-19 severity and mortality using machine learning models. The study identifies SpO<sub>2</sub>/FiO<sub>2</sub> ratio to be the most important predictor for both severity and mortality. Several biomarkers were identified to modestly improve the predictions. The results also provide a baseline of SARS-CoV-2 infection during the early stages of the coronavirus emergence and can serve as a baseline for future studies that inform how the genetic evolution of the coronavirus affects the host response to new variants.

Frontiers in MedicineVol. 12
Brigham and Women's Hospital (US), Boston Children's Hospital (US), La Jolla Institute for Immunology (US), Harvard University (US), University of Arizona (US), Emory University (US), University of California, Los Angeles (US), Oregon Health & Science University (US), Palo Alto University (US), University of California, San Francisco (US), Baylor College of Medicine (US), University of Washington (US), University Hospitals of Cleveland (US), Yale University (US), University of Florida (US), Yale New Haven Health System (US), National Institute of Allergy and Infectious Diseases (US), OU Health, University of Oklahoma Health Sciences Center (US), Drexel University (US), The University of Texas at Austin (US), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
Yale University, National Institutes of Health, National Institute of Allergy and Infectious Diseases, David Geffen School of Medicine, University of California, Los Angeles
Openalex Percentile: Top 80%
COVID-19 Clinical Research Studies
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