Predicting outcomes in pediatric patients with acute kidney injury: a retrospective single-center cohort study using machine learning models.

ABSTRACT:

Objective

To develop and evaluate machine learning models combined with survival analysis for predicting 7-, 14-, and 28-day mortality in critically ill children with acute kidney injury (AKI), identifying key predictors to guide risk stratification and early intervention.

Methods

Using the Pediatric Intensive Care (PIC) database, we analyzed data from 3,624 children with AKI admitted between 2010 and 2018. Nine machine learning algorithms, including CatBoost, were trained to predict mortality, with feature importance assessed via SHapley Additive exPlanations (SHAP). Time-to-event analyses, including Kaplan-Meier and restricted cubic spline methods, examined the temporal impact of predictors on 28-day mortality, stratified by age and AKI stage.

Results

CatBoost achie

SUBMITTER: Shen F 

PROVIDER: S-EPMC12512778 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature