Development and validation of interpretable machine learning models to predict intensive care unit outcomes in patients on hemodialysis: a multicenter study.

ABSTRACT:

Background

Hemodialysis patients are at high risk for ICU admission due to elevated mortality, cardiovascular disease, and infection rates. Traditional ICU scoring systems (e.g., APACHE-II, SOFA) demonstrate limited accuracy in this population. This study aimed to identify key risk factors and develop interpretable machine learning (ML) models for predicting ICU outcomes to enable early intervention.

Methods

This multicenter study analyzed data from three cohorts: The First Affiliated Hospital of Sun Yat-sen University (n = 248), MIMIC-IV (n = 769), and eICU-CRD (n = 1,878). Primary outcome was all-cause ICU mortality; secondary outcomes were cardiovascular and infection-related mortality. Thirteen ML algorithms and ensemble models were applied to 113 clinical variables coll

SUBMITTER: Chen M 

PROVIDER: S-EPMC12784603 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature