Gastro Hep Advances · Published 2026-01-01 · DOI 10.1016/j.gastha.2026.100985
Background and Aims: Gastrointestinal bleeding (GIB) is a life-threatening emergency with considerable morbidity and mortality. Traditional risk scores like AIMS65 and Glasgow-Blatchford Score (GBS) are limited in capturing nonlinear clinical interactions. We developed and externally validated a machine learning model to predict 30-day mortality in GIB patients. Methods: We retrospectively analyzed 5453 emergency department patients with GIB from the Medical Information Mart for Intensive Care IV–Emergency Department database for model development, with external validation using 7166 patients from Jefferson Health. Sixteen clinical and laboratory variables were selected based on a literature review and clinical relevance. The development cohort was divided into training (80%) and internal validation (20%) sets. Survivors were partitioned into 24 clusters using K-means, with separate random forest models trained on each cluster, combined with all deceased cases. Performance was evaluated using the area under the receiver-operating characteristic curve, sensitivity, and specificity on the external validation set, then benchmarked against AIMS65 and the GBS. Results: The model achieved an area under the receiver-operating characteristic curve of 0.884 (95% confidence interval: 0.863–0.905) on internal validation and 0.882 (95% confidence interval: 0.863–0.900) on external validation, significantly outperforming AIMS65 (0.737) and GBS (0.768) (P < .001). At the optimal threshold, the model achieved 87.9% sensitivity and 74.3% specificity on the external validation cohort. At maximum sensitivity thresholds, the model maintained higher specificity (54.4%) than AIMS65 (29.7%) and GBS (17.0%) (P < .001). Clustering identified distinct phenotypes with mortality ranging from 0.6% to 15.3%. SHapley Additive exPlanations analysis identified age, albumin, hemodynamic parameters, and presenting hemoglobin and platelet count as key predictors. Conclusion: Our model provides superior risk stratification for 30-day mortality in GIB compared to conventional scores, with validated generalizability and potential for integration into electronic health record systems.
Abstract from DOAJ. Public domain (CC0 1.0).
Read the article at the publisher →