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Development and Prospective Validation of an Interpretable Machine Learning Model Integrating Cardiac MRI
Zhihao Zhao1, Zeqing Zhang2, Yu Yang3
1Department of Cardiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China (Z.Z., J.L.).
Rationale And Objectives:
Extracellular volume (ECV) quantified by cardiac magnetic resonance (CMR) is a key indicator of myocardial fibrosis and strongly linked to outcomes after ST-segment elevation myocardial infarction (STEMI). This research focused on the development and validation of an interpretable machine learning (ML) model that incorporates ECV to predict 1-year major adverse cardiac events (MACE) in patients with STEMI following percutaneous coronary intervention.
Materials And Methods:
MACE prediction models were developed using clinical variables combined with CMR-ECV parameters and validated in a prospective cohort. Feature selection was conducted through Boruta algorithms and least absolute shrinkage and selection operator algorithms, and eight ML algorithms were trained. Model performance was assessed by discrimination and calibration. The top performer was interpreted by Shapley Additive exPlanations (SHAP) analysis. An interactive online nomogram was deployed to facilitate personalized perioperative risk management.
Results:
Among 673 STEMI patients (derivation: n = 398; prospective validation: n = 275), five key predictors were identified: N-terminal pro-B-type natriuretic peptide (NT-proBNP), left ventricular ejection fraction (LVEF), myocardial infarction ECV (MI-ECV), age, and creatine kinase-MB. The random forest model achieved the highest discriminative performance (area under the receiver operating characteristic curve 0.825, 95% confidence interval: 0.763-0.876). SHAP analysis identified NT-proBNP as the most impactful feature, with LVEF class and MI-ECV ranking next.
Conclusion:
Integration of CMR-derived ECV into an interpretable machine learning framework enables accurate 1-year MACE risk prediction following STEMI. This approach combines robust discriminative performance with clinical transparency through SHAP-based explanations, offering a proof-of-concept tool for precision cardiovascular care.