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

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 ( Results: Among 27 485 S-AKI patients, hospital mortality was 27.5%. Boruta identified 21 consensus features including severity scores [Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA), OASIS], vital signs and laboratory parameters. Gradient Boosting Machine emerged as optimal with area under the curve (AUC) values of 0.770 (training), 0.731 (internal validation) and 0.732-0.778 across four external validation cohorts. The model demonstrated excellent calibration and minimal overfitting (3.9% AUC difference). Decision curve analysis revealed superior clinical utility across probability thresholds of 4%-82%. SHAP analysis identified SAPS II as the most important predictor, with scores >60 and SOFA >15 associated with substantially increased mortality risk. Complete case analysis confirmed model robustness (AUC 0.766-0.847). Conclusions: The interpretable machine learning model demonstrated excellent performance and robust generalizability for S-AKI mortality prediction across five international databases. SHAP analysis provided clinically meaningful insights supporting personalized risk stratification and evidence-based clinical decision-making.