Key result
Prior coronary heart disease is linked to ~2.8-fold higher in-hospital GI bleeding risk in AMI.
Why the study?
Gastrointestinal bleeding is a severe, potentially life-threatening complication during hospitalization for AMI, and early identification of high-risk patients through predictive modeling is needed to reduce complications and guide clinical decision-making.
Does a machine learning-based random forest model accurately predict in-hospital gastrointestinal bleeding, and is coronary heart disease an independent risk factor for bleeding in patients hospitalized with acute myocardial infarction?
Cohort (n=3,656)
Yes
Does a machine learning-based random forest model accurately predict in-hospital gastrointestinal bleeding, and is coronary heart disease an independent risk factor for bleeding in patients hospitalized with acute myocardial infarction?
A random forest machine learning model using routine admission laboratory and clinical data—specifically highlighting anemia markers, cardiac injury biomarkers, and history of coronary heart disease—can moderately predict in-hospital gastrointestinal bleeding in patients with acute myocardial infarction (AUC 0.75-0.77).
Answers are grounded in this paper and cite their sources.
May inform bleeding risk assessment in AMI using routine data; leaves open prospective validation of the random forest model.
Background Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making. Objective This study aimed to develop and validate a machine learning (ML)–based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. Methods A multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables. Results The RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups. Conclusions The ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies.
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Kou et al. (2025) conducted a cohort in Acute myocardial infarction (AMI) (n=3,656). Coronary heart disease (CHD) and Random Forest machine learning risk prediction model vs. Patients without coronary heart disease / alternative machine learning models was evaluated on In-hospital gastrointestinal bleeding (GIB) (OR 2.79, 95% CI 2.09-3.74, p=<0.001). Coronary heart disease independently increased in-hospital gastrointestinal bleeding risk in AMI patients, and a random forest model predicted bleeding with an AUC of 0.75-0.77.
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