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% CI 0.745-0.786) in the training set and 0.742 (95% CI 0.710-0.773) in the validation set, low Brier score, good calibration, and meaningful clinical net benefit. SHAP analysis identified the SOFA score, mechanical ventilation, WBC count, age, serum potassium level, sepsis, and vasoactive drug use as key predictors, with consistent interpretability at both the population and individual levels. In external validation, the model demonstrated stable discrimination across multicenter populations (AUCs of 0.719 in eICU, 0.769 in TS-TAKI, and 0.616 in BAKIT); however, calibration performance was notably affected by population heterogeneity, suggesting the need for recalibration using local data in real-world application.

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.