ArticleJournal of medical Internet research2025
Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Predictive Value of D-Dimer to Albumin Ratio for Outcome in Acute Type A Aortic Dissection Patients.Journal of clinical hypertension (Greenwich, Conn.) · 2026Article
- Interpreting the Black Box: Interpretable Machine Learning and Systems Pharmacology in Small-Molecule Therapeutics.Pharmaceutics · 2026Review
- Multi-Output Machine Learning for Prediction of Postoperative Outcomes After Cardiac Surgery Using Patient Blood Management Biomarkers.Journal of clinical medicine · 2026Article
- Early postnatal risk stratification for severe adverse outcomes in twin neonates admitted to the neonatal intensive care unit: development and temporal validation of an interpretable machine learning model.Translational pediatrics · 2026Article
- Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study.JMIR medical informatics · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models.Frontiers in endocrinology · 2025Article
- The impact of clopidogrel combined with aspirin or aspirin alone on prognosis and adverse events in patients undergoing coronary artery bypass grafting in the first month after surgery.Frontiers in cardiovascular medicine · 2025Article
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15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for personalized prevention.
objectiveThis study aims to develop and validate predictive models to assess the risk of gastrointestinal bleeding after coronary artery bypass grafting (GIBCG) and to guide personalized prevention.
methodsParticipants were recruited from 4 medical centers, including a prospective cohort and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. From an initial cohort of 18,938 patients, 16,440 were included in the final analysis after applying the exclusion criteria. Thirty combinations of machine learning algorithms were compared, and the optimal model was selected based on integrated performance metrics, including the area under the receiver operating characteristic curve (AUROC) and the Brier score. This model was then developed into a web-based risk prediction calculator. The Shapley Additive Explanations method was used to provide both global and local explanations for the predictions.
resultsThe model was developed using data from 3 centers and a prospective cohort (n=13,399) and validated on the Drum Tower cohort (n=2745) and the MIMIC cohort (n=296). The optimal model, based on 15 easily accessible admission features, demonstrated an AUROC of 0.8482 (95% CI 0.8328-0.8618) in the derivation cohort. In external validation, the AUROC was 0.8513 (95% CI 0.8221-0.8782) for the Drum Tower cohort and 0.7811 (95% CI 0.7275-0.8343) for the MIMIC cohort. The analysis indicated that high-risk patients identified by the model had a significantly increased mortality risk (odds ratio 2.98, 95% CI 1.784-4.978; P<.001). For these high-risk populations, preoperative use of proton pump inhibitors was an independent protective factor against the occurrence of GIBCG. By contrast, dual antiplatelet therapy and oral anticoagulants were identified as independent risk factors. However, in low-risk populations, the use of proton pump inhibitors (χ
conclusionsOur machine learning model accurately identified patients at high risk of GIBCG, who had a poor prognosis. This approach can aid in early risk stratification and personalized prevention.
trial registrationChinese Clinical Registry Center ChiCTR2400086050; http://www.chictr.org.cn/showproj.html?proj=226129.
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