Comparative machine learning to predict acute kidney injury in traumatic brain injury: a MIMIC-IV cohort with SHAP interpretation.

Background: AKI is a frequent and severe complication among TBI patients. Accurate early prediction is critical but remains challenging in ICU practice. Methods: We retrospectively analyzed the MIMIC-IV database. After screening 85,242 first ICU admissions and applying exclusions, 2,986 TBI patients were included. AKI was defined by KDIGO criteria. Demographic, physiological, laboratory, and intervention variables were extracted, preprocessed, and imputed. Predictors were selected using LASSO, Boruta, and logistic regression with bootstrap validation. Seven ML models (LR, DT, RF, XGBoost, LightGBM, SVM, ANN) were trained on 70% of the cohort and validated on 30%, with hyperparameters optimized by grid search and 5-fold CV. Performance was assessed by AUC, calibration, DCA, accuracy, sensitivity, specificity, PPV, NPV, and Results: Of the 2,986 TBI patients, 2,045 (68.5%) developed AKI. AKI patients were older, heavier, and had higher glucose, sodium, SBP, and temperature, with lower urine output and more frequent ventilation. Feature selection consistently retained urine output, ventilation, weight, age, glucose, sodium, SBP, and temperature as core predictors. In validation, XGBoost showed the best performance (AUC 0.775, 95% CI 0.747-0.802; accuracy 74.4%; sensitivity 88.3%; Conclusion: Ensemble ML models, particularly XGBoost, demonstrated robust predictive power, outperforming LR and DT. The XGBoost model combined high discrimination, calibration, and interpretability, offering a clinically applicable tool for early AKI risk stratification in TBI.