Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 7, 2024
Open Peer Review Period: Nov 7, 2024 - Jan 2, 2025
Date Accepted: Feb 13, 2025
(closed for review but you can still tweet)
Machine Learning Models with Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: A Multicenter Cohort Study
ABSTRACT
Background:
Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for personalized prevention.
Objective:
In this study, we aimed to develop and validate predictive models to assess the risk of gastrointestinal bleeding after coronary artery bypass grafting (GIBCG) and guide personalized prevention.
Methods:
Participants were recruited from four medical centers, including a prospective cohort and the Medical Information Mart for Intensive Care IV(MIMIC IV). 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 area under the receiver operating characteristic curve (AUROC) and Brier score, and 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 three centers and a prospective cohort (n=13,399) and validated on the Drum Tower (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% confidence interval [CI], 0.8328–0.8618) in the derivation cohort. In external validation, the AUROC of the Drum Tower cohort was 0.8492 (95% CI, 0.8216–0.8749), and the AUROC of the MIMIC cohort was 0.7807 (95% CI, 0.7267–0.8294). The analysis indicated that high-risk patients identified by the model had a significantly increased mortality risk (odds ratio, 1.99; 95% CI, 1.58–2.52; P < .001). For these high-risk populations, preoperative use of proton pump inhibitors was an independent protective factor against the occurrence of GIBCG. Conversely, dual antiplatelet therapy and oral anticoagulants were identified as independent risk factors. However, for low-risk populations, the use of these medications was not significantly associated with the occurrence of GIBCG.
Conclusions:
Our machine learning model accurately identified high-risk GIBCG patients who had a poor prognosis. This can aid in early risk stratification and personalized prevention. Clinical Trial: Chinese Clinical Registry Center ChiCTR2400086050; http://www.chictr.org.cn/showproj.html?proj=226129
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