ArticleJournal of inflammation research2026
Predicting In-Hospital Mortality in Pediatric Sepsis: Machine Learning Development and Multicenter Validation.
Article in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Early identification of pediatric sepsis patients at high risk of in-hospital mortality is crucial. However, the clinical utility of existing machine learning (ML) models is limited by temporal data leakage and a lack of robust external validation. Objective: To develop, interpret, and externally validate an interpretable ML model for predicting in-hospital mortality in pediatric sepsis, utilizing only predictors available within the first 24 hours of ICU admission. Methods: Based on a retrospective development cohort from the Children's Hospital of Chongqing Medical University, eight ML algorithms were evaluated. Predictor variables were strictly limited to the first 24 ICU hours to prevent temporal bias. The best-performing model was interpreted via SHAP, calibrated with spline-based regression, and externally validated on the Pediatric Intensive Care (PIC) database (pediatric cohort) and the MIMIC-IV database (adult cohort, to test biological generalizability). A web-based calculator was developed. Results: XGBoost achieved the best performance (AUC: 0.821, 95% CI: 0.751-0.892). In external validation, the model demonstrated strong generalizability, yielding AUCs of 0.735 (95% CI: 0.700-0.770) in the pediatric PIC cohort and 0.778 (95% CI: 0.763-0.793) in the adult MIMIC-IV cohort. The calibrated model showed excellent agreement (pediatric Brier score: 0.060, calibration slope: 1.000, intercept: 0.000). Decision curve analysis confirmed clinical net benefit between 3% and 27% thresholds. SHAP analysis identified pH, fibrinogen, lactate, platelet count, urea, and chloride as the key predictors. Conclusion: We developed a validated, interpretable XGBoost model for early risk stratification of pediatric sepsis. By restricting predictors to the initial 24 hours, the model avoids temporal bias while maintaining stable performance across pediatric and adult cohorts. This web-based tool is ready to support clinical bedside decision-making.
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