Work

Nutritional indicator-based prediction model for esophagogastric variceal bleeding in cirrhosis using XGBoost and SHAP

Year: 2026

Type: article

Abstract: Purpose: Malnutrition is common in cirrhotic patients and associated with poor prognosis, yet current models have suboptimal predictive performance for esophagogastric variceal bleeding (EGVB). This study aimed to construct a noninvasive prediction model integrating nutritional indicators. Methods: We retrospectively analyzed 416 cirrhotic patients (135 with EGVB, 281 without EGVB) at the First Hospital of Shanxi Medical University between March 2023 and March 2025. Eight nutritional screening tools were compared by univariate logistic regression and receiver operating characteristic (ROC) analysis to identify the optimal tool. An XGBoost prediction model was then constructed with permutation-importance-based feature selection. Model performance was assessed by AUC, accuracy, sensitivity, specificity, and F1-score, with SHAP for interpretation. Results: ), monocyte count and mid-arm muscle circumference (MAMC). The model yielded an AUC of 0.860, accuracy of 84.3%, recall of 66.7%, precision of 81.8%, F1-score of 80.9%, and Brier score of 0.122. SHAP analysis demonstrated that three nutritional indicators, namely MUST score, adjusted BMI and mid-arm muscle circumference, were important predictors of EGVB. A higher MUST score, higher adjusted BMI, and lower mid-arm muscle circumference were associated with an increased risk of EGVB. Conclusion: The XGBoost-SHAP model integrating nutritional indicators accurately predicts EGVB risk in cirrhosis, outperforming single traditional nutritional screening tools. This explainable, noninvasive machine learning approach provides a reliable tool for risk stratification and individualized nutritional intervention, potentially improving outcomes in this high-risk population.

Source: Frontiers in Nutrition

DOI: 10.3389/fnut.2026.1911832