Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC study.
The 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.
We 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.
Severity 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.
This 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.
We 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.
Severity 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.
This 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.
Authors
Hou Hou, Haslund-Gourley Haslund-Gourley, Diray-Arce Diray-Arce, Hoch Hoch, Rouphael Rouphael, Becker Becker, Augustine Augustine, Ozonoff Ozonoff, Guan Guan, Kleinstein Kleinstein, Peters Peters, Reed Reed, Altman Altman, Langelier Langelier, Maecker Maecker, Kim Kim, Montgomery Montgomery, Krammer Krammer, Wilson Wilson, Eckalbar Eckalbar, Bosinger Bosinger, Levy Levy, Steen Steen, Rosen Rosen, Baden Baden, Melamed Melamed, Ehrlich Ehrlich, McComsey McComsey, Sekaly Sekaly, Schaenman Schaenman, Shaw Shaw, Hafler Hafler, Corry Corry, Kheradmand Kheradmand, Atkinson Atkinson, Brakenridge Brakenridge, Agudelo Higuita Agudelo Higuita, Metcalf Metcalf, Hough Hough, Messer Messer, Pulendran Pulendran, Nadeau Nadeau, Davis Davis, Fernandez Sesma Fernandez Sesma, Simon Simon, Kraft Kraft, Bime Bime, Calfee Calfee, Erle Erle, Impacc Network Impacc Network, Robinson Robinson, Cairns Cairns, Haddad Haddad, Comunale Comunale
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