ArticleBMC medical informatics and decision making2025
Predicting outcomes in pediatric patients with acute kidney injury: a retrospective single-center cohort study using machine learning models.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine Learning on the Pediatric Intensive Care (PIC) Database: PIC-Powered Prediction.Bioengineering (Basel, Switzerland) · 2026Review
- Serum lactate and capillary refill time as predictors of acute kidney injury after pediatric cardiovascular surgery: a prospective cohort study.Pediatric nephrology (Berlin, Germany) · 2026Article
- Accurate prediction of mortality in children with sepsis: development and validation of an explainable model based on real-world data.Italian journal of pediatrics · 2026Article
- Development and external validation of an interpretable early prediction model for acute kidney injury using TabPFN and routine admission data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
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7 authors.
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Abstract
objectiveTo 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.
methodsUsing 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.
resultsCatBoost achieved the highest area under the curve (AUC) values: 0.871 (95% CI: 0.824-0.918) for 7-day, 0.871 (95% CI: 0.829-0.913) for 14-day, and 0.867 (95% CI: 0.829-0.905) for 28-day mortality. Lactate was the top predictor across all models. Time-to-event analyses revealed a linear association between elevated lactate (cut-off: 1.5 mmol/L) and 28-day mortality (p-overall < 0.001), with stronger effects in infants (0-3 years) and AKI stage 1 patients (HR > 1).
conclusionsMachine learning, particularly CatBoost, combined with survival analysis, accurately predicts AKI-related mortality in critically ill children, with lactate as a pivotal marker. These findings support precision risk stratification and early lactate-targeted interventions, though multicenter validation is needed for clinical adoption.
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