Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study.

ABSTRACT

BACKGROUND: Upper gastrointestinal bleeding (UGIB) is a significant cause of morbidity and mortality worldwide. This study investigates the use of residual variables and machine learning (ML) models for predicting major bleeding in patients with severe UGIB after their first intensive care unit (ICU) admission. METHODS: The Medical Information Mart for Intensive Care IV and eICU databases were used. Conventional ML and long short-term memory models were constructed using pre-ICU and ICU admission day data to predict the recurrence of major gastrointestinal bleeding. In the models, residual data were utilized by subtracting the normal range from the test result. The models included eight algorithms. Shapley additive explanations and saliency maps were used for feature interpretability. RESULTS: Twenty-five ML models were developed using data from 2604 patients. The light gradient-boosting machine algorithm model using pre-ICU admission residual data outperformed other models that used test results directly, with an AUC of 0.96. The key factors included aspartate aminotransferase, blood urea nitrogen, albumin, length of ICU admission, and respiratory rate. CONCLUSIONS: ML models using residuals improved the accuracy and interpretability in predicting major bleeding during ICU admission in patients with UGIB. These interpretable features may facilitate the early identification and management of high-risk patients, thereby improving hemodynamic stability and outcomes.