A comparison of modeling approaches for static and dynamic prediction of central line-associated bloodstream infections using electronic health records (part 2): random forest models.

ABSTRACT:

Objective

Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of static and dynamic random forest (RF) models to predict the risk of central line-associated bloodstream infections (CLABSI) using different outcome operationalizations.

Methods

We included data from 27,478 admissions to the University Hospitals Leuven, covering 30,862 catheter episodes (970 CLABSI, 1466 deaths and 28,426 discharges) to build static and dynamic RF models for binary (CLABSI vs no CLABSI), multinomial (CLABSI, discharge, death or no event), survival (time to CLABSI) and competing risks (time to CLABSI, discharge or death) out

SUBMITTER: Albu E 

PROVIDER: S-EPMC12278561 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature