An Interpretable Machine Learning Model for Predicting in-Hospital Progression in Initially Mild Hypertriglyceridemia-Induced Acute Pancreatitis Using Clinical and Non-Contrast CT Features

Introduction: Early identification of patients with initially mild hypertriglyceridemia-induced acute pancreatitis (HTG-AP) who are at risk of an unstable disease course remains challenging using conventional assessment alone. This study aimed to develop an interpretable machine learning model based on baseline clinical and non-contrast computed tomography (CT) features for early prediction of in-hospital progression. Methods: We retrospectively enrolled 164 patients with initially mild HTG-AP between October 2020 and October 2024. Baseline clinical variables, CT-based body composition parameters, and pancreatic radiomics features from non-contrast CT were collected at admission. Patients were classified into progression (n = 88) and non-progression (n = 76) groups based on clinically relevant worsening supported by clinical or CT evidence during hospitalization. Five-fold cross-validation was used for model development and internal validation. Four XGBoost-based models were constructed: clinical only, clinical + body composition, clinical + radiomics, and clinical + body composition + radiomics. Model performance was assessed using receiver operating characteristic analysis, calibration analysis, decision curve analysis, and Shapley additive explanations (SHAP). Results: The clinical + body composition + radiomics model achieved the best overall performance among the four models, with an AUC of 0.830 and an accuracy of 0.768. Calibration analysis showed relatively good agreement between predicted and observed risks, and decision curve analysis demonstrated a higher net benefit across most clinically relevant threshold probabilities. SHAP analysis identified triglycerides (TG) and the visceral fat area-to-abdominal cavity area ratio (VFA/ACA) as the dominant contributors to model prediction. Conclusion: The proposed interpretable multimodal model may improve early risk stratification for in-hospital progression in initially mild HTG-AP and help identify patients who require closer monitoring, although further external validation is needed to confirm its generalizability and clinical applicability.