Related Experiment Video
Updated: Sep 14, 2025
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Baseline predictors for 28-day COVID-19 severity and mortality among hospitalized patients: results from the IMPACC
Jintong Hou1, Benjamin Haslund-Gourley1, Joann Diray-Arce2
1Department of Microbiology and Immunology/Department of Medicine/Department of Epidemiology & Biostatistics, Drexel University, Philadelphia, PA, United States.
Insights
The SpO2/FiO2 ratio is the key predictor of COVID-19 severity and mortality. Machine learning models identified clinical and laboratory biomarkers to improve these predictions, outperforming existing scores.
Area of Science:
- Infectious Diseases
- Biomarkers
- Machine Learning
Background:
- The COVID-19 pandemic overwhelmed healthcare systems globally.
- Predictive models for COVID-19 severity and mortality are crucial for resource allocation and patient management.
Purpose of the Study:
- To identify baseline clinical and laboratory biomarkers for predicting COVID-19 severity and mortality.
- To develop and validate machine learning models for these predictions.
Main Methods:
- Analysis of data from 1102 hospitalized COVID-19 patients.
- Utilized lasso and forward selection for predictor identification.
- Developed and validated predictive models using balanced training and testing data.
Main Results:
- The SpO2/FiO2 ratio was the strongest predictor of severity (AUC: 0.874) and mortality (AUC: 0.83).
- Adding biomarkers like age, BMI, FGF23, IL-6, LTA, TNFRSF11B, and ribitol improved prediction accuracy.
- Developed models outperformed the Sequential Organ Failure Assessment (SOFA) score in certain patient groups.
Conclusions:
- Machine learning models effectively identify key predictors of COVID-19 outcomes.
- The SpO2/FiO2 ratio is a critical, easily accessible biomarker for COVID-19 prognosis.
- Identified biomarkers offer potential for refining risk stratification in hospitalized COVID-19 patients.
Introduction:
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.
Methods:
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.
Results:
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.
Conclusion:
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.
More Related Videos
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
04:32Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia
Published on: June 28, 2018
