Objective To develop and validate a machine learning model incorporating liver function, variceal characteristics, and hemodynamic parameters for individualized prediction of prognosis in cirrhotic patients undergoing endoscopic variceal ligation (EVL), thereby providing a reference for clinical outcome assessment and treatment decision-making. Methods Cirrhotic patients who underwent EVL between January 2022 and December 2024 were retrospectively enrolled and randomly divided into a training set and a validation set at a 7:3 ratio. In the training set, univariate analysis, Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to identify independent predictors. Key predictive variables were used to construct machine learning models (random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN)). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis, with the optimal model selected. SHapley Additive exPlanations (SHAP) method was employed to assess model interpretability, analyzing the direction and magnitude of variable contributions to predictions. Results A total of 342 patients were retrospectively enrolled and randomly divided into a training set (n = 239) and a validation set (n = 103). Baseline clinical characteristics showed no significant differences between the training and validation sets (all P > 0.05). In the training set, multivariate logistic regression showed that Model for End-Stage Liver Disease (MELD) score, total bilirubin, maximum variceal diameter, and hepatic venous pressure gradient (HVPG) were independent risk factors for poor prognosis (all P < 0.05), whereas serum albumin, platelet count, and hemoglobin at admission were protective factors (all P < 0.05). Among machine learning models, the SVM model demonstrated superior predictive performance, with an AUC of 0.859 in the training set and 0.797 in the validation set, outperforming RF and KNN models. SHAP interpretability analysis confirmed that MELD score, HVPG, and total bilirubin contributed most strongly to increased risk, while serum albumin, platelet count, and hemoglobin at admission exerted protective effects. Conclusion This study successfully developed and validated a predictive model incorporating MELD score, variceal diameter, and HVPG. The model accurately predicts survival in cirrhotic patients after EVL, serving as a practical tool for individualized prognostic assessment.

Development and validation of a prognostic model for cirrhotic patients after endoscopic variceal ligation

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2026年3月3日

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Dongxue Yao,
Jianmei Pan

摘要

Objective

To develop and validate a machine learning model incorporating liver function, variceal characteristics, and hemodynamic parameters for individualized prediction of prognosis in cirrhotic patients undergoing endoscopic variceal ligation (EVL), thereby providing a reference for clinical outcome assessment and treatment decision-making.

Methods

Cirrhotic patients who underwent EVL between January 2022 and December 2024 were retrospectively enrolled and randomly divided into a training set and a validation set at a 7:3 ratio. In the training set, univariate analysis, Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression were used to identify independent predictors. Key predictive variables were used to construct machine learning models (random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN)). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis, with the optimal model selected. SHapley Additive exPlanations (SHAP) method was employed to assess model interpretability, analyzing the direction and magnitude of variable contributions to predictions.

Results

A total of 342 patients were retrospectively enrolled and randomly divided into a training set (n = 239) and a validation set (n = 103). Baseline clinical characteristics showed no significant differences between the training and validation sets (all P > 0.05). In the training set, multivariate logistic regression showed that Model for End-Stage Liver Disease (MELD) score, total bilirubin, maximum variceal diameter, and hepatic venous pressure gradient (HVPG) were independent risk factors for poor prognosis (all P < 0.05), whereas serum albumin, platelet count, and hemoglobin at admission were protective factors (all P < 0.05). Among machine learning models, the SVM model demonstrated superior predictive performance, with an AUC of 0.859 in the training set and 0.797 in the validation set, outperforming RF and KNN models. SHAP interpretability analysis confirmed that MELD score, HVPG, and total bilirubin contributed most strongly to increased risk, while serum albumin, platelet count, and hemoglobin at admission exerted protective effects.

Conclusion

This study successfully developed and validated a predictive model incorporating MELD score, variceal diameter, and HVPG. The model accurately predicts survival in cirrhotic patients after EVL, serving as a practical tool for individualized prognostic assessment.