1. Development of a prediction model for 30-day COVID-19 hospitalization and death in a national cohort of Veterans Health Administration patients-March 2022-April 2023.
- Author
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Bui DP, Bajema KL, Huang Y, Yan L, Li Y, Rajeevan N, Berry K, Rowneki M, Argraves S, Hynes DM, Huang G, Aslan M, and Ioannou GN
- Subjects
- Humans, Male, Female, Middle Aged, Aged, United States epidemiology, Cohort Studies, Machine Learning, Veterans Health, United States Department of Veterans Affairs, ROC Curve, Aged, 80 and over, COVID-19 Vaccines administration & dosage, Adult, COVID-19 mortality, COVID-19 epidemiology, Hospitalization statistics & numerical data, SARS-CoV-2 isolation & purification
- Abstract
Objective: The epidemiology of COVID-19 has substantially changed since its emergence given the availability of effective vaccines, circulation of different viral variants, and re-infections. We aimed to develop models to predict 30-day COVID-19 hospitalization and death in the Omicron era for contemporary clinical and research applications., Methods: We used comprehensive electronic health records from a national cohort of patients in the Veterans Health Administration (VHA) who tested positive for SARS-CoV-2 between March 1, 2022, and March 31, 2023. Full models incorporated 84 predictors, including demographics, comorbidities, and receipt of COVID-19 vaccinations and anti-SARS-CoV-2 treatments. Parsimonious models included 19 predictors. We created models for 30-day hospitalization or death, 30-day hospitalization, and 30-day all-cause mortality. We used the Super Learner ensemble machine learning algorithm to fit prediction models. Model performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration intercepts and slopes in a 20% holdout dataset., Results: Models were trained and tested on 198,174 patients, of whom 8% were hospitalized or died within 30 days of testing positive. AUCs for the full models ranged from 0.80 (hospitalization) to 0.91 (death). Brier scores were close to 0, with the lowest error in the mortality model (Brier score: 0.01). All three models were well calibrated with calibration intercepts <0.23 and slopes <1.05. Parsimonious models performed comparably to full models., Conclusions: We developed prediction models that accurately estimate COVID-19 hospitalization and mortality risk following emergence of the Omicron variant and in the setting of COVID-19 vaccinations and antiviral treatments. These models may be used for risk stratification to inform COVID-19 treatment and to identify high-risk patients for inclusion in clinical trials., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)
- Published
- 2024
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