ArticleNPJ digital medicine2025
Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04662918 (Validation of CAGIB Score for In-hospital Mortality of Cirrhotic Patients With Acute Gastrointestinal Bleeding), which is not on this map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Validation of CAGIB Score for In-hospital Mortality of Cirrhotic Patients With Acute Gastrointestinal Bleeding: A Prospective, International Multicenter, Observational Study
Who cites it
9 citing papers in PubMed.
- Comparative Accuracy of Clinical and Inflammatory Scores for Predicting Mortality and Rebleeding After Acute Gastroesophageal Variceal Bleeding.Journal of clinical medicine · 2026Article
- Comparative Evaluation of A4C, CHAMPS, and CAGIB Scores for Risk Stratification in Hemodialysis Patients with Acute Gastrointestinal Bleeding.Diagnostics (Basel, Switzerland) · 2026Article
- A Causal and interpretable machine learning framework for postcranioplasty risk prediction and surgical decision support.NPJ digital medicine · 2026Article
- Age as a key predictor of 6-week mortality in cirrhotic patients with acute gastrointestinal bleeding: a retrospective cohort study.Frontiers in medicine · 2026Article
- Identifying Low-Risk Patients with Cirrhosis and Acute Gastrointestinal Bleeding That May Not Require Urgent Endoscopy.Advances in therapy · 2026Article
- Development and Validation of an Explainable Machine Learning Model for Prediction of Massive Transfusion in Upper Gastrointestinal Bleeding.Risk management and healthcare policy · 2026Article
- Impact of Cirrhosis Etiology and Intensive Care Accessibility on Upper Gastrointestinal Bleeding: A Prospective Observational Study.Canadian journal of gastroenterology & hepatology · 2026Observational
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
Acute gastrointestinal bleeding (AGIB) is a potentially lethal complication in cirrhosis. In this prospective international multi-center study, the performance of CAGIB score for predicting the risk of in-hospital death in 2467 cirrhotic patients with AGIB was validated. Machine learning (ML) models were established based on CAGIB components, and their area under curves (AUCs) were calculated and compared. Gray zone approach was employed to further stratify the risk of death. In training cohort, the AUC of CAGIB score was 0.789. Among the ML models, the least square support vector machine regression (LS-SVMR) model had the best predictive performance (AUC = 0.986). Patients were further divided into low- (LS-SVMR score <0.084), moderate- (LS-SVMR score 0.084-0.160), and high-risk (LS-SVMR score >0.160) groups with in-hospital mortality of 0.38%, 2.22%, and 64.37%, respectively. Statistical results were retained in validation cohort. LS-SVMR model has an excellent predictive performance for in-hospital death in cirrhotic patients with AGIB (ClinicalTrials.gov; NCT04662918).
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Registered trials
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.