%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e68509
%T Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study
%A Dong,Jiale
%A Jin,Zhechuan
%A Li,Chengxiang
%A Yang,Jian
%A Jiang,Yi
%A Li,Zeqian
%A Chen,Cheng
%A Zhang,Bo
%A Ye,Zhaofei
%A Hu,Yang
%A Ma,Jianguo
%A Li,Ping
%A Li,Yulin
%A Wang,Dongjin
%A Ji,Zhili
%+ Department of General Surgery, Beijing Chaoyang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlua, Chaoyang District, Beijing, 100020, China, 86 010 85231610, anzhenjzl@mail.ccmu.edu.cn
%K machine learning
%K personalized medicine
%K coronary artery bypass grafting
%K adverse outcome
%K gastrointestinal bleeding
%D 2025
%7 6.3.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for personalized prevention. Objective: This 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. Methods: Participants 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. Results: The 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 (χ21=0.13, P=.72), dual antiplatelet therapy (χ21=0.38, P=.54), and oral anticoagulants (χ21=0.15, P=.69) were not significantly associated with the occurrence of GIBCG. Conclusions: Our 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 Registration: Chinese Clinical Registry Center ChiCTR2400086050; http://www.chictr.org.cn/showproj.html?proj=226129 
%M 40053791
%R 10.2196/68509
%U https://www.jmir.org/2025/1/e68509
%U https://doi.org/10.2196/68509
%U http://www.ncbi.nlm.nih.gov/pubmed/40053791