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This study develops and validates prediction models to guide TIPS plus VE treatment decisions by focusing on rebleeding risk. A retrospective cohort of 1,336 patients was analyzed across training and internal/external validation, comparing machine-learning algorithms with multivariable logistic regression as best performer, using AUC, sensitivity, specificity and decision curve analysis with SHAP-derived predictors.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-driven-risk-stratification-to-guide-variceal-embolization-in-tips-treated-cirrhotic-patients-with-acute-variceal-bleeding/457666/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-driven-risk-stratification-to-guide-variceal-embolization-in-tips-treated-cirrhotic-patients-with-acute-variceal-bleeding/457666.png","ImageObject",300,407,{"name":92,"@type":93},"SANS","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-04","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What clinical problem does the study address?","Question",{"text":112,"@type":113},"The study addresses how to predict rebleeding risk in cirrhotic patients with acute variceal bleeding treated with TIPS, with or without variceal embolization.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the prediction modeling performed?",{"text":117,"@type":113},"A retrospective cohort of 1,336 patients was split into training, internal validation, and external validation sets. 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High-risk patients identified by LR benefited from TIPS plus VE, whereas low-risk patients showed no additional benefit.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},457666,1790811281,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090760505,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Zhu et al. BMC Medical Informatics and Decision Making (2026) 26:3  \n[https://doi.org/10.1186/s12911-025-03304-0](https://doi.org/10.1186/s12911-025-03304-0)  \nBMC Medical Informatics and Decision Making  \nRESEARCH Open Access  \nMachine learning-driven risk stratification to guide variceal embolization in TIPS-treated cirrhotic patients with acute variceal bleeding  \nGangfeng Zhu 1†, Yipeng Song 1†, Beijia Yu 1†, Cixiang Chen 1†, Siying Chen 1, Yi Xie 1, Qiang Yi 1, Haozhe Fu 1, Xiangcai Wang2* and Li Huang2*  \nAbstract  \nBackground Transjugular intrahepatic portosystemic shunt (TIPS) combined with variceal embolization (VE)  \nis 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.  \nMethods 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.  \nResults 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 \u003C 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.  \n†Gangfeng Zhu, Yipeng Song, Beijia Yu and Cixiang Chen contributed equally to this work and share first authorship.  \n*Correspondence: Xiangcai Wang [wangxiangcai@csco.ac.cn](wangxiangcai@csco.ac.cn)[ ](wangxiangcai@csco.ac.cn)Li Huang [hlellen@gmu.edu.cn](hlellen@gmu.edu.cn)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/.  \nZhu et al. BMC Medical Informatics and Decision Making (2026) 26:3 Page 2 of 14  \nConclusion The LR mo","cbCaimS3RbX7QNrQ","https://ap.wps.com/l/cbCaimS3RbX7QNrQ","pdf",2642559,14,"English","# Abstract\n# Methods\n# Results\n# Conclusion\n# Practice implications\n# Keywords\n# Introduction","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study addresses how to predict rebleeding risk in cirrhotic patients with acute variceal bleeding treated with TIPS, with or without variceal embolization.\"},{\"question\":\"How was the prediction modeling performed?\",\"answer\":\"A retrospective cohort of 1,336 patients was split into training, internal validation, and external validation sets. Multiple machine-learning algorithms were compared with multivariable logistic regression (LR), and model performance was evaluated with AUC, sensitivity, specificity, and decision curve analysis; SHAP was used to identify key predictors.\"},{\"question\":\"What did the results show about TIPS plus VE benefit?\",\"answer\":\"The LR model achieved the highest predictive performance in both validation cohorts. High-risk patients identified by LR benefited from TIPS plus VE, whereas low-risk patients showed no additional benefit.\"}]","Machine learning-driven risk stratification to guide variceal embolization in TIPS-treated cirrhotic patients with acute variceal bleeding | PDF",1790750099,35]