Evidence map›Paper›PMID 41345583›Full record

ArticleBMC medical informatics and decision making2025

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

Gangfeng Zhu, Yipeng Song, Beijia Yu, Cixiang Chen, Siying Chen, Yi Xie, Qiang Yi, Haozhe Fu, Xiangcai Wang, Li Huang

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Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Gangfeng Zhu *The First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Yipeng Song *The First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Beijia Yu *The First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Cixiang Chen *The First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Siying ChenThe First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Yi XieThe First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Qiang YiThe First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Haozhe FuThe First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Xiangcai WangDepartment of Oncology, The First Affiliated Hospital, Jiangxi Clinical Research Center for Cancer, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China. wangxiangcai@csco.ac.cn.
Li HuangDepartment of Oncology, The First Affiliated Hospital, Jiangxi Clinical Research Center for Cancer, Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China. hlellen@gmu.edu.cn.

Funding

Bureau of Science and Technology of Ganzhou Municipality GZ2023ZSF098
6 · The paper itself

Abstract

backgroundTransjugular 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.

methodsThis 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.

resultsRebleeding 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.

conclusionThe 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. PRACTICE IMPLICATIONS: 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.

Indexed as

Embolization, TherapeuticEsophageal and Gastric VaricesGastrointestinal HemorrhageLiver CirrhosisMachine LearningPortasystemic Shunt, Transjugular IntrahepaticAgedFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRetrospective StudiesRisk AssessmentAcute variceal bleedingCirrhosisMachine learningRebleedingTransjugular intrahepatic portosystemic shuntVariceal embolization

Identifiers

PMID41345583
PMCPMC12781592

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.