Development and validation of a prediction model for mortality in critically ill COVID-19 patients.

ABSTRACT:

Background

Early prediction of prognosis may help early treatment measures to reduce mortality in critically ill coronavirus disease (COVID-19) patients. The study aimed to develop a mortality prediction model for critically ill COVID-19 patients.

Methods

This retrospective study analyzed the clinical data of critically ill COVID-19 patients in an intensive care unit between April and June 2022. Propensity matching scores were used to reduce the effect of confounding factors. A predictive model was built using logistic regression analysis and visualized using a nomogram. Calibration and receiver operating characteristic (ROC) curves were used to estimate the accuracy and predictive value of the model. Decision curve analysis (DCA) was used to examine the value of the model f

SUBMITTER: Sun X 

PROVIDER: S-EPMC11228157 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature