ArticleWorld journal of gastroenterology2025
Machine learning prediction of hepatic encephalopathy for long-term survival after transjugular intrahepatic portosystemic shunt in acute variceal bleeding.
Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk factors for hepatic encephalopathy in patients following transjugular intrahepatic portosystemic shunt: a meta-analysis and systematic review.BMC surgery · 2026Pooled it
- Risk prediction models for hepatic encephalopathy following TIPS: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Artificial Intelligence for Personalized Prediction of Post-TIPS Outcomes: Integrating Clinical, Biochemical, and Radiomics Data-A Narrative Review.Journal of clinical medicine · 2026Review
- [Novel advances following the integration of Baveno VII consensus definitions for the non-invasive testing of portal hypertension].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- Review
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Machine learning techniques in hepatic encephalopathy: a scoping review.BMC medical informatics and decision making · 2025Article
- Application of artificial intelligence in portal hypertension and esophagogastric varices.World journal of gastroenterology · 2025Review
- Interpretable machine learning model for predicting covert hepatic encephalopathy in patients with cirrhosis: a multicenter study.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransjugular intrahepatic portosystemic shunt (TIPS) is an effective intervention for managing complications of portal hypertension, particularly acute variceal bleeding (AVB). While effective in reducing portal pressure and preventing rebleeding, TIPS is associated with a considerable risk of overt hepatic encephalopathy (OHE), a complication that significantly elevates mortality rates.
aimTo develop a machine learning (ML) model to predict OHE occurrence post-TIPS in patients with AVB using a 5-year dataset.
methodsThis retrospective single-center study included 218 patients with AVB who underwent TIPS. The dataset was divided into training (70%) and testing (30%) sets. Critical features were identified using embedded methods and recursive feature elimination. Three ML algorithms-random forest, extreme gradient boosting, and logistic regression-were validated
resultsThe median OS of the study cohort was 47.83 ± 22.95 months. Among the models evaluated, logistic regression demonstrated the highest performance with an area under the curve (AUC) of 0.825. Key predictors identified were Child-Pugh score, age, and portal vein thrombosis. Kaplan-Meier analysis revealed that patients without OHE had a significantly longer OS (
conclusionThe ML model accurately predicts post-TIPS OHE and outperforms traditional models, supporting its use in improving outcomes in patients with AVB.
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