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

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

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1604388

DOI=10.3389/fmed.2025.1604388

ISSN=2296-858X

ABSTRACT=IntroductionThe coronavirus disease 2019 (COVID-19) pandemic threatened public health and placed a significant burden on medical resources. The Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study collected clinical, demographic, blood cytometry, serum receptor-binding domain (RBD) antibody titers, metabolomics, targeted proteomics, nasal metagenomics, Olink, nasal viral load, autoantibody, SARS-CoV-2 antibody titers, and nasal and peripheral blood mononuclear cell (PBMC) transcriptomics data from patients hospitalized with COVID-19. The aim of this study is to select baseline biomarkers and build predictive models for 28-day in-hospital COVID-19 severity and mortality with most predictive variables while prioritizing routinely collected variables.MethodsWe analyzed 1102 hospitalized COVID-19 participants. We used the lasso and forward selection to select top predictors for severity and mortality, and built predictive models based on balanced training data. We then validated the models on testing data.ResultsSeverity was best predicted by the baseline SpO2/FiO2 ratio obtained from COVID-19 patients (test AUC: 0.874). Adding patient age, BMI, FGF23, IL-6, and LTA to the disease severity prediction model improves the test AUC by an additional 3%. The clinical mortality prediction model using SpO2/FiO2 ratio, age, and BMI resulted in a test AUC of 0.83. Adding laboratory results such as TNFRSF11B and plasma ribitol count increased the prediction model by 3.5%. The severity and mortality prediction models developed outperform the Sequential Organ Failure Assessment (SOFA) score among inpatients and perform similarly to the SOFA score among ICU patients.ConclusionThis study identifies clinical data and laboratory biomarkers of COVID-19 severity and mortality using machine learning models. The study identifies SpO2/FiO2 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.