Hospital-wide, dynamic, individualized prediction of central line-associated bloodstream infections-development and temporal evaluation of six prediction models.

ABSTRACT:

Background

Central line-associated bloodstream infections (CLABSI) are preventable hospital-acquired infections. Predicting CLABSI helps improve early intervention strategies and enhance patient safety.

Aim

To develop and temporally evaluate dynamic prediction models for continuous CLABSI risk monitoring.

Methods

Data from hospitalized patients with central catheter(s) admitted to University Hospitals Leuven between 2014 and 2017 were used to develop five dynamic models (a landmark cause-specific model, two random forest models, and two XGBoost models) to predict 7-day CLABSI risk, accounting for competing events (death, discharge, and catheter removal). The models' predictions were then combined using a superlearner model. All models were temporally evaluated on data

SUBMITTER: Albu E 

PROVIDER: S-EPMC12023667 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature