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<p>Rebleeding is a severe complication following recovery from esophageal variceal bleeding (EVB), yet robust predictive tools for assessing post-treatment risk after endoscopic variceal ligation (EVL) therapy remain scarce. This study developed and independently validated a machine learning (ML) model using multidimensional clinical data to predict 1-year rebleeding risk. Two independent cohorts were included: a retrospective cohort (<i>n</i> = 373) for model development and a prospective cohort (<i>n</i> = 119) for validation, with a one-year rebleeding endpoint. Predictors were identified using Recursive Feature Elimination (RFE), and eight ML algorithms were evaluated. Each algorithm was optimized via 5-fold cross-validation. The model with optimal performance was chosen to develop an online computational platform. RFE identified eight key predictors. The XGBoost model demonstrated superior predictive performance in both the training and validation cohorts, achieving AUCs of 0.883 and 0.887, respectively. This model was subsequently implemented in an online clinical platform for individualized 1-year rebleeding risk assessment. Our findings establish XGBoost as an effective tool for predicting EVB rebleeding risk, providing an evidence-based decision aid for post-EVL management.</p>

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Machine learning model for predicting rebleeding risk after endoscopic variceal ligation in esophageal variceal bleeding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Junyi Zhan,
  • Keqiang Lu,
  • Jinzhong Yu,
  • Jie Chen,
  • Feifei Xing,
  • Dexin Wang,
  • Shihao Zhang,
  • Mingyan Yang,
  • Ping Liu,
  • Jian Wang,
  • Yongping Mu

摘要

Rebleeding is a severe complication following recovery from esophageal variceal bleeding (EVB), yet robust predictive tools for assessing post-treatment risk after endoscopic variceal ligation (EVL) therapy remain scarce. This study developed and independently validated a machine learning (ML) model using multidimensional clinical data to predict 1-year rebleeding risk. Two independent cohorts were included: a retrospective cohort (n = 373) for model development and a prospective cohort (n = 119) for validation, with a one-year rebleeding endpoint. Predictors were identified using Recursive Feature Elimination (RFE), and eight ML algorithms were evaluated. Each algorithm was optimized via 5-fold cross-validation. The model with optimal performance was chosen to develop an online computational platform. RFE identified eight key predictors. The XGBoost model demonstrated superior predictive performance in both the training and validation cohorts, achieving AUCs of 0.883 and 0.887, respectively. This model was subsequently implemented in an online clinical platform for individualized 1-year rebleeding risk assessment. Our findings establish XGBoost as an effective tool for predicting EVB rebleeding risk, providing an evidence-based decision aid for post-EVL management.