Early detection of ICU-acquired infections using high-frequency electronic health record data.

ABSTRACT:

Background

Nosocomial infections are a major cause of morbidity and mortality in the ICU. Earlier identification of these complications may facilitate better clinical management and improve outcomes. We developed a dynamic prediction model that leveraged high-frequency longitudinal data to estimate infection risk 48 h ahead of clinically overt deterioration.

Methods

We used electronic health record data from consecutive adults who had been treated for > 48 h in a mixed tertiary ICU in the Netherlands enrolled in the Molecular Diagnosis and Risk Stratification of Sepsis (MARS) cohort from 2011 to 2018. All infectious episodes were prospectively adjudicated. ICU-acquired infection (ICU-AI) risk was estimated using a Cox landmark model with high-resolution vital sign data proce

SUBMITTER: Varkila MRJ 

PROVIDER: S-EPMC12278606 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature