Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding.
prospective_cohort · Level II
Where this comes from
- Record sourced from PubMed, PMID 40745090.
- Also identified by DOI 10.1038/s41746-025-01883-w and PMC identifier 12313868.
- Licence recorded as CC BY-NC-ND.
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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).