Personalized Prediction of Hospital Mortality in COVID-19-Positive Patients.

ABSTRACT:

Objective

To develop predictive models for in-hospital mortality and length of stay (LOS) for coronavirus disease 2019 (COVID-19)-positive patients.

Patients and methods

We performed a multicenter retrospective cohort study of hospitalized COVID-19-positive patients. A total of 764 patients admitted to 14 different hospitals within the Cleveland Clinic from March 9, 2020, to May 20, 2020, who had reverse transcriptase-polymerase chain reaction-proven coronavirus infection were included. We used LightGBM, a machine learning algorithm, to predict in-hospital mortality at different time points (after 7, 14, and 30 days of hospitalization) and in-hospital LOS. Our final cohort was composed of 764 patients admitted to 14 different hospitals within our system.

Results

The m

SUBMITTER: Rozenbaum D 

PROVIDER: S-EPMC8114764 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature