Personalized machine learning-based prognostic model for ICU-acquired bloodstream infections.

ABSTRACT:

Background

Intensive care unit-acquired bloodstream infections (ICU-BSIs) are among the most prevalent healthcare-associated infections and a major cause of mortality among ICU patients. We developed a machine learning (ML)-based model to predict the prognosis of ICU-BSIs.

Methods

Adult patients with blood cultures drawn ≥48 hours after ICU admission were included: the Second Affiliated Hospital of Anhui Medical University (AMU, China) and the Medical Information Mart for Intensive Care IV (MIMIC-IV, USA). The AMU dataset was used for model training and internal validation, and the MIMIC-IV dataset served as the external validation set. The model incorporated routinely collected, easily obtainable clinical variables, including several representing the average rate of change

SUBMITTER: Zhou S 

PROVIDER: S-EPMC12605174 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature