摘要Background:Rhabdomyolysis(RM)is a complex set of clinical syndromes.RM-induced acute kidney injury(AKI)is a common illness in war and military operations.This study aimed to develop an interpretable and generalizable model for early AKI prediction in patients with RM.Methods:Retrospective analyses were performed on 2 electronic medical record databases:the eICU Collaborative Research Data-base and the Medical Information Mart for Intensive Care Ⅲ database.Data were extracted from the first 24 hours after patient admission.Data from the two datasets were merged for further analysis.The extreme gradient boosting(XGBoost)model with the Shapley additive explanation method(SHAP)was used to conduct early and interpretable predictions of AKI.Results:The analysis included 938 eligible patients with RM.The XGBoost model exhibited superior performance(area under the re-ceiver operating characteristic curve[AUC]=0.767)compared to the other models(logistic regression,AUC=0.711;support vector ma-chine,AUC=0.693;random forest,AUC=0.728;and naive Bayesian,AUC=0.700).Conclusion:Although the XGBoost model performance could be improved from an absolute perspective,it provides better predictive performance than other models for estimating the AKI in patients with RM based on patient characteristics in the first 24 hours after ad-mission to an intensive care unit.Furthermore,including SHAP to elucidate AK-related factors enables individualized patient treatment,potentially leading to improved prognoses for patients with RM.