Prediction of hospital mortality in sepsis-associated acute kidney injury using a machine-learning approach: a multicenter study using SHAP interpretability analysis.

ABSTRACT:

Background

Sepsis-associated acute kidney injury (S-AKI) represents a critical complication with high mortality rates in intensive care units. Current risk stratification tools lack precision and interpretability for clinical decision-making. This study aimed to develop and validate interpretable machine learning models for predicting hospital mortality in S-AKI patients.

Methods

This retrospective cohort study utilized five international critical care databases: Medical Information Mart for Intensive Care (MIMIC)-IV (n = 12 966), MIMIC-III-CareVue (n = 2209), eICU (n = 8210), Northwestern University Intensive Care Unit (NWICU) (n = 2207) and Salzburg Intensive Care database (SICdb) (n = 1893). Adult patients with S-AKI meeting sepsis-3.0 a

SUBMITTER: Luo S 

PROVIDER: S-EPMC12805249 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature