Multicenter validation of an explainable machine learning model for early prediction of acute kidney injury in critically ill patients with digestive system tumors.

Objective: Critically ill patients with digestive system tumors are at high risk of acute kidney injury (AKI), a complication strongly associated with increased mortality and adverse outcomes. However, early AKI risk identification in the intensive care unit (ICU) remains challenging. This study aimed to develop and externally validate an interpretable model for early AKI risk prediction in critically ill patients with digestive system tumors. Methods: We retrospectively analyzed 3,821 patients with digestive system tumors from the MIMIC-IV 3.0 database. Routine clinical variables from the first 24 hours after ICU admission were extracted. Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were used for feature selection, followed by the development and comparison of six machine learning models. Model performance was evaluated using the AUC, calibration analysis, and decision curve analysis. The optimal model was interpreted using Shapley Additive Explanations (SHAP), and an online interactive prediction tool was developed to facilitate clinical translation. External validation was conducted in three independent cohorts from the United States and China, including the eICU Collaborative Research Database (eICU), the Tangshan tumor-related AKI cohort (TS-TAKI), and the Beijing Acute Kidney Injury cohort (BAKIT). Result: The incidence of AKI was 75.8%. Among all models, extreme gradient boosting (XGBoost) showed the best overall performance, with an AUC of 0.765 (95% Conclusion: This interpretable XGBoost-based model, based on routine ICU data, enables early AKI risk stratification in critically ill patients with digestive system tumors. The model achieves a balance between predictive performance, transparency, and clinical feasibility. It exhibited stable discriminative performance across multicenter and cross-population cohorts, while calibration was clearly affected by population heterogeneity, highlighting the need for local validation and recalibration in real-world application. An online prediction tool further supports its potential for clinical translation.