Interpretable machine learning for early prediction of acute kidney injury in critically ill patients with acute pancreatitis.

Background: Acute pancreatitis (AP) is a global health issue that can lead to acute kidney injury (AKI), especially in critically ill patients. Timely identification of AP-AKI is vital for early intervention. This study aimed to develop and validate a machine learning (ML) model to predict AP-AKI in ICU patients. Methods: Data were collected from Shandong Provincial Hospital (training/internal validation) and the MIMIC-IV database (external validation). Thirty-three clinical variables within 24 hours of ICU admission were selected using LASSO regression. Six ML algorithms were tested, with model performance evaluated through discrimination, calibration, and clinical utility. SHAP analysis was used for interpretability, and a web-based interface was developed for clinical use. Results: Among 317 patients in the internal cohort, 219 (69.1%) developed AKI. A 7-variable Random Forest model showed the best performance with AUCs of 0.970 (training), 0.811 (internal validation), and 0.820 (external validation). Key predictors included neutrophil percentage, platelet-to-neutrophil ratio, creatinine, APTT, lactate, glucose, and vasopressor use. A friendly user interface has been developed for clinician use. Conclusion: The ML-based model demonstrated strong predictive performance for AP-AKI and good generalizability. SHAP analysis enhanced model transparency, supporting clinical decision-making and early intervention.