Machine learning-driven risk stratification to guide variceal embolization in TIPS-treated cirrhotic patients with acute variceal bleeding

Transjugular intrahepatic portosystemic shunt (TIPS) combined with variceal embolization (VE) is a therapeutic strategy for cirrhotic patients with acute variceal bleeding (AVB). However, the efficacy of this combination remains controversial. This study aimed to develop and validate prediction models to guide treatment decisions for TIPS combined with VE in AVB patients, focusing on predicting rebleeding risk. This retrospective study included 1,336 cirrhotic patients with AVB undergoing TIPS with (941 cases) or without VE (395 cases) between January 2010 and June 2020. Patients were divided into training (n = 338), internal validation (n = 146), and external validation (n = 852) cohorts. Data were collected on baseline characteristics, clinical variables, and procedural details. Several machine-learning algorithms were evaluated alongside a multivariable logistic regression (LR) model, with LR demonstrating the best predictive performance. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). The SHapley Additive exPlanations (SHAP) method was used to identify key predictors of rebleeding risk. Rebleeding occurred in 17.74% of patients, with no significant difference between the TIPS alone and TIPS + VE groups (p = 0.946). Shunt dysfunction was more frequent in the TIPS + VE group (p < 0.001). The multivariable logistic regression model had the highest AUC for predicting rebleeding in both internal (AUC = 0.762) and external validation cohorts (AUC = 0.762). It showed a balanced sensitivity and specificity across validation cohorts. SHAP analysis identified key predictors, including creatinine, Child-Pugh class, and international normalized ratio. High-risk patients identified by the LR model benefited from TIPS + VE (p = 0.018), while low-risk patients saw no additional benefit. The LR model effectively stratified TIPS patients into high- and low-risk groups for post-procedural rebleeding, providing a useful tool to guide personalized treatment decisions for cirrhotic patients undergoing TIPS with or without VE. The LR-based model enables individualized treatment decisions by identifying AVB patients most likely to benefit from TIPS combined with variceal embolization, thereby optimizing therapeutic outcomes and avoiding unnecessary interventions.

Authors

Institutions

Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2025-12-04
DOI
https://doi.org/10.1186/s12911-025-03304-0
Primary Topic
Liver Disease and Transplantation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine learning-driven risk stratification to guide variceal embolization in TIPS-treated cirrhotic patients with acute variceal bleeding

Hung‐Chun Fu, Beijia Yu, Yipeng Song, Yi Xie et al.
BMC Medical Informatics and Decision Making
Liver Disease and Transplantation
article

Machine learning-driven risk stratification to guide variceal embolization in TIPS-treated cirrhotic patients with acute variceal bleeding

Hung‐Chun Fu, Beijia Yu, Yipeng Song, Yi Xie, Cixiang Chen, Xiangcai Wang, Gangfeng Zhu, Siying Chen, Qiang Yi
article en

Abstract

Transjugular intrahepatic portosystemic shunt (TIPS) combined with variceal embolization (VE) is a therapeutic strategy for cirrhotic patients with acute variceal bleeding (AVB). However, the efficacy of this combination remains controversial. This study aimed to develop and validate prediction models to guide treatment decisions for TIPS combined with VE in AVB patients, focusing on predicting rebleeding risk. This retrospective study included 1,336 cirrhotic patients with AVB undergoing TIPS with (941 cases) or without VE (395 cases) between January 2010 and June 2020. Patients were divided into training (n = 338), internal validation (n = 146), and external validation (n = 852) cohorts. Data were collected on baseline characteristics, clinical variables, and procedural details. Several machine-learning algorithms were evaluated alongside a multivariable logistic regression (LR) model, with LR demonstrating the best predictive performance. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). The SHapley Additive exPlanations (SHAP) method was used to identify key predictors of rebleeding risk. Rebleeding occurred in 17.74% of patients, with no significant difference between the TIPS alone and TIPS + VE groups (p = 0.946). Shunt dysfunction was more frequent in the TIPS + VE group (p < 0.001). The multivariable logistic regression model had the highest AUC for predicting rebleeding in both internal (AUC = 0.762) and external validation cohorts (AUC = 0.762). It showed a balanced sensitivity and specificity across validation cohorts. SHAP analysis identified key predictors, including creatinine, Child-Pugh class, and international normalized ratio. High-risk patients identified by the LR model benefited from TIPS + VE (p = 0.018), while low-risk patients saw no additional benefit. The LR model effectively stratified TIPS patients into high- and low-risk groups for post-procedural rebleeding, providing a useful tool to guide personalized treatment decisions for cirrhotic patients undergoing TIPS with or without VE. The LR-based model enables individualized treatment decisions by identifying AVB patients most likely to benefit from TIPS combined with variceal embolization, thereby optimizing therapeutic outcomes and avoiding unnecessary interventions.

BMC Medical Informatics and Decision MakingVol. 26(1)
Gannan Medical University (CN), First Affiliated Hospital of Gannan Medical University (CN)
Openalex Percentile: Top 50%
Liver Disease and Transplantation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.