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Updated: Jun 6, 2025
Surgical Swine Model of Chronic Cardiac Ischemia Treated by Off-Pump Coronary Artery Bypass Graft Surgery
Published on: March 27, 2018
Multimodal Data-Driven Prognostic Model for Predicting Long-Term Prognosis in Patients With Ischemic Cardiomyopathy
Jun Wang1, Yijun Wang2, Shoupeng Duan3
1Department of Cardiology The First Affiliated Hospital of Bengbu Medical University Bengbu Anhui China.
Insights
Machine learning identified key predictors for major adverse cardiovascular events in patients undergoing coronary artery bypass grafting for ischemic cardiomyopathy. This aids in risk stratification for heart failure with preserved ejection fraction.
Area of Science:
- Cardiovascular Medicine
- Biomedical Engineering
- Machine Learning in Healthcare
Background:
- Limited data exists on coronary artery bypass grafting (CABG) efficacy for ischemic cardiomyopathy with heart failure with preserved ejection fraction (HFpEF).
- Accurate risk stratification is crucial for optimizing patient selection and outcomes in this complex population.
Purpose of the Study:
- To develop a machine learning-based prognostic model for risk stratification.
- To integrate clinical, biomarker, and echocardiography data for enhanced prediction.
- To improve comprehension of factors influencing outcomes after CABG in ischemic cardiomyopathy with HFpEF.
Main Methods:
- A cohort of 294 patients with ischemic cardiomyopathy and HFpEF undergoing CABG was analyzed.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression identified key predictors.
- A prediction nomogram was constructed using diabetes, hypertension, systemic immune-inflammation index, and NT-proBNP.
Main Results:
- The nomogram demonstrated satisfactory predictive performance in both development (C-index: 0.768) and validation cohorts (C-index: 0.633).
- The model showed adequate calibration for predicting major adverse cardiovascular events.
- Decision curve analysis confirmed the clinical utility of the prognostic model.
Conclusions:
- A clinically feasible prognostic model was developed using preoperative multimodal data.
- This model enables effective risk stratification for patients with ischemic heart disease and HFpEF undergoing CABG.
- The findings support the use of machine learning for personalized risk assessment in cardiovascular surgery.
Background:
Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical features, biomarker data, and echocardiography data to enhance comprehension and risk stratification in patients diagnosed with ischemic cardiomyopathy and heart failure with preserved ejection fraction who have undergone coronary artery bypass grafting surgery.
Methods And Results:
For this study, 294 patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction who underwent coronary artery bypass grafting surgery were assigned to the development cohort (n=176) and the independent validation cohort (n=118). A total of 52 clinical variables were extracted for each patient. The principal clinical end point was the incidence of major adverse cardiovascular events, encompassing cardiac mortality, acute myocardial infarction, acute heart failure, and graft failure. From least absolute shrinkage and selection operator regression, 4 predictors were selected for the final prediction nomogram: diabetes, hypertension, the systemic immune-inflammation index, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). The prediction nomogram achieved satisfactory prediction performance in both the development cohort (C index, 0.768 [95% CI, 0.701-0.835]) and independent validation cohort (C index, 0.633 [95% CI, 0.521-0.745]). Adequate calibration was noted for the likelihood of major adverse cardiovascular events in both the development and independent validation cohorts. Decision curve analysis confirmed the clinical usefulness of the established prediction nomogram.
Conclusions:
A clinically feasible prognostic model, based on preoperative multimodal data, was developed for risk stratification of patients with ischemic heart and heart failure with preserved ejection fraction who receive coronary artery bypass grafting surgery.
Registration:
https://www.chictr.org.cn; Unique identifier: ChiCTR2300074439.
