Predicting COVID-19 prognosis in hospitalized patients based on early status.

ABSTRACT: Predicting which patients are at greatest risk of severe disease from COVID-19 has the potential to improve patient outcomes and improve resource allocation. We developed machine learning models for predicting COVID-19 prognosis from a retrospective chart review of 969 hospitalized COVID-19 patients at Robert Wood Johnson University Hospital during the first pandemic wave in the United States, focusing on 77 variables from patients' first day of hospital admission. Our best 77-variable model was better able to predict mortality (receiver operating characteristic area under the curve [ROC AUC] = 0.808) than CURB-65, a commonly used clinical prediction rule for pneumonia severity (ROC AUC = 0.722). After identifying highly predictive variables in our full models using Shapley a

SUBMITTER: Natanov D 

PROVIDER: S-EPMC10653946 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature