Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.

ABSTRACT:

Background

Sepsis-associated acute kidney injury (SA-AKI) is a frequent and life-threatening complication in patients in the intensive care unit (ICU), significantly increasing both mortality rates and the risk of chronic kidney dysfunction. However, existing prediction models have often focused on overall risk and lack severity-based stratification, which limits their clinical applicability.

Objective

This study aimed to identify critical time points in SA-AKI progression development and validate dynamic, stratified machine learning prediction models for moderate-to-severe (Kidney Disease: Improving Global Outcomes guideline stages 2-3) SA-AKI through multicenter, multiregional external validation, ultimately deploying them as publicly accessible, interpretable clinical dec

SUBMITTER: Ge X 

PROVIDER: S-EPMC12521856 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature