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

Abstract

Abstract 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.