A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients.

ABSTRACT: As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve patient outcomes. Existing prognostic models mostly consider the impact of biomarkers at presentation on the risk of a single patient outcome at a single follow up time. We collected data for 553 Polymerase Chain Reaction (PCR)-positive COVID-19 patients admitted to hospital whose eventual outcomes were known. The data collected for the patients included demographics, comorbidities and laboratory values taken at admission and throughout the course of hospitalization. We trained multivariate Markov prognostic models to identify high-risk patients at admission along with a dynamic measure of risk incorporating time-dependent changes in patients' laborato

SUBMITTER: Momeni-Boroujeni A 

PROVIDER: S-EPMC8006025 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature