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Research Article: Explainable machine learning model to predict refeeding syndrome in patients with severe acute pancreatitis

Date Published: 2026-01-23

Abstract:
To construct and validate a risk prediction model for refeeding syndrome (RFS) in patients with severe acute pancreatitis (SAP), identify high-risk individuals before overt electrolyte abnormalities occur, and provide decision support for the timing of enteral nutrition initiation and early personalized intervention. A retrospective cohort study was conducted on SAP patients admitted to Xuzhou Medical University Affiliated Hospital (XYFY) between September 2018 and September 2025. Patients were divided into RFS and Non-RFS groups based on the development of RFS after initiating enteral nutrition. Clinical data differences between groups were compared, least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and six machine learning (ML) algorithms were applied to build prediction models. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. SHapley Additive exPlanations (SHAP) analysis was performed to interpret the contribution of key features. Seven predictive features were identified for model construction. The gradient boosting machine (GBM) model exhibited good generalization ability, with area under the curve (AUC) values of 0.851 (95% CI: 0.809–0.894) in the training set and 0.762 (95% CI: 0.672–0.852) in the testing set. Calibration curves confirmed consistency between predicted probabilities and actual outcomes, while decision curves demonstrated favorable net benefits across different clinical decision thresholds. SHAP analysis ranked feature importance as follows: serum potassium (K), serum sodium (Na), serum calcium (Ca), gastrointestinal decompression, blood urea nitrogen (BUN), diabetes mellitus (DM) history, and diuretic use. The GBM model effectively predicts RFS risk in SAP patients after initiating enteral nutrition.

Introduction:
Refeeding syndrome (RFS) is a severe metabolic complication characterized by profound electrolyte disturbances, fluid imbalance, and organ dysfunction. It occurs when nutritional support is restarted in patients with prolonged fasting or severe malnutrition, driven by core pathophysiological changes that result from rapid insulin secretion recovery and subsequent intracellular electrolyte shifts ( 1 ). In critically ill patients, RFS is closely associated with increased mortality and prolonged hospital stays ( 2…

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