ArticleDiagnostic pathology2023
Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients.
Article in Diagnostic pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Machine Learning in Predicting the Risk of Esophagogastric Variceal Bleeding Among Patients With Liver Cirrhosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The Development of a Scoring System for Acute Variceal Bleeding Risk in Cirrhotic Patients in Emergency Departments.Life (Basel, Switzerland) · 2026Article
- Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding.World journal of hepatology · 2026Article
- Etiology-Specific Nomograms for Predicting First Variceal Bleeding in Decompensated HBV- and HCV-Related Cirrhosis.International journal of general medicine · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Machine Learning-Based Prediction of Decompensation in Hepatitis B Virus-Related Cirrhosis.Diagnostics (Basel, Switzerland) · 2025Article
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.BMC medical informatics and decision making · 2025Article
- Establishment and validation of a nomogram for predicting esophagogastric variceal bleeding in patients with liver cirrhosis.World journal of gastroenterology · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Esophageal varices detection and bleeding risk assessment with artificial intelligence: a systematic review.iGIE : innovation, investigation and insights · 2024Article
- Applications of Artificial Intelligence-Based Systems in the Management of Esophageal Varices.Journal of personalized medicine · 2024Review
- Machine Learning Models for Predicting Mortality in Patients with Cirrhosis and Acute Upper Gastrointestinal Bleeding at an Emergency Department: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2024Article
- Future directions of noninvasive prediction of esophageal variceal bleeding: No worry about the present computed tomography inefficiency.World journal of gastrointestinal endoscopy · 2024Article
- Article
- Assessing the Predictive Factors for Bleeding in Esophageal Variceal Disease: A Systematic Review.Cureus · 2023Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundLiver cirrhosis patients are at risk for esophagogastric variceal bleeding (EGVB). Herein, we aimed to estimate the EGVB risk in patients with liver cirrhosis using an artificial neural network (ANN).
methodsWe included 999 liver cirrhosis patients hospitalized at the Beijing Ditan Hospital, Capital Medical University in the training cohort and 101 patients from Shuguang Hospital in the validation cohort. The factors independently affecting EGVB occurrence were determined via univariate analysis and used to develop an ANN model.
resultsThe 1-year cumulative EGVB incidence rates were 11.9 and 11.9% in the training and validation groups, respectively. A total of 12 independent risk factors, including gender, drinking and smoking history, decompensation, ascites, location and size of varices, alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), hematocrit (HCT) and neutrophil-lymphocyte ratio (NLR) levels as well as red blood cell (RBC) count were evaluated and used to establish the ANN model, which estimated the 1-year EGVB risk. The ANN model had an area under the curve (AUC) of 0.959, which was significantly higher than the AUC for the North Italian Endoscopic Club (NIEC) (0.669) and revised North Italian Endoscopic Club (Rev-NIEC) indices (0.725) (all P < 0.001). Decision curve analyses revealed improved net benefits of the ANN compared to the NIEC and Rev-NIEC indices.
conclusionsThe ANN model accurately predicted the 1-year risk for EGVB in liver cirrhosis patients and might be used as a basis for risk-based EGVB surveillance strategies.
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Registered trials
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