New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding

Le résumé fourni par la source

Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk of in-hospital mortality in patients admitted due to overt GIB. We analyzed the prospective, multicenter Hungarian GIB Registry's data. The predictive performance of XGBoost and CatBoost machine-learning algorithms with the Glasgow-Blatchford (GBS), pre-endoscopic Rockall and ABC scores were compared. We evaluated our models using five-fold cross-validation, and performance was measured by area under receiver operating characteristic curve (AUC) analysis with 95% confidence intervals (CI). Overall, we included 1,021 patients in the analysis. In-hospital death occurred in 108 cases. The XGBoost and the CatBoost model identified patients who died with an AUC of 0.84 (CI:0.76-0.90; 0.77-0.90; respectively) in the internal validation set, whereas the GBS and pre-endoscopic Rockall clinical scoring system's performance was significantly lower, AUC values of 0.68 (CI:0.62-0.74) and 0.62 (CI:0.56-0.67), respectively. ABC score had an AUC of 0.77 (CI:0.71-0.83). The XGBoost model had a specificity of 0.96 (CI:0.92-0.98) at a sensitivity of 0.25 (CI:0.10-0.43) compared with the CatBoost model, which had a specificity of 0.74 (CI:0.66-0.83) at a sensitivity of 0.78 (CI:0.57-0.95). XGBoost and the CatBoost models evaluate the mortality risk of acute GI bleeding better, than the conventional risk assessment tools.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.