A comparison of modeling approaches for static and dynamic prediction of central-line bloodstream infections using electronic health records (part 1): regression models.

ABSTRACT:

Background

Hospitals register information in the electronic health records (EHRs) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We aimed to compare different static and dynamic regression modeling approaches to predict central line-associated bloodstream infections (CLABSIs) in EHR while accounting for competing events precluding CLABSI.

Methods

We analyzed data from 30,862 catheter episodes at University Hospitals Leuven from 2012 and 2013 to predict 7-day risk of CLABSI. Competing events are discharge and death. Static models using information at catheter onset included logistic, multinomial logistic, Cox, cause-specific hazard, and Fine-Gray regression. Dynamic models updated predictions daily up to 30 days after catheter

SUBMITTER: Gao S 

PROVIDER: S-EPMC12278581 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature