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.
