ArticleFrontiers in cardiovascular medicine2025
Prediction of major adverse cardiovascular events following ST-segment elevation myocardial infarction using cardiac obesity marker-epicardial adipose tissue mass index: a prospective cohort study.
Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Automated Deep Learning Segmentation and Quantification of Epicardial Adipose Tissue from Coronary Computed Tomography Angiography: Validation and Clinical Implications.Journal of imaging informatics in medicine · 2026Article
- An association between epicardial adipose tissue and prognosis in acute myocardial infarction: a systematic review and meta-analysis.Journal of thoracic disease · 2026Article
- Deep learning-derived pericardial adipose tissue by electrocardiogram-gated cardiac computed tomography predicts cardiovascular events beyond coronary calcium score.American journal of preventive cardiology · 2026Article
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6 authors.
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Abstract
Background: Although reperfusion therapy has led to improvements in the acute phase of ST-segment elevation myocardial infarction (STEMI), the incidence of major adverse cardiovascular events (MACE) following STEMI has not significantly decreased. The accumulation of epicardial adipose tissue (EAT) may be associated with poorer STEMI prognosis and could serve as a potential prognostic marker. However, research examining this relationship remains limited. Methods: This single-center prospective study enrolled 308 STEMI patients. Patients were randomly assigned to training set and validation set in a 7:3 ratio. The primary outcome was MACE one-year post-STEMI. Epicardial adipose tissue mass index (EAMI) was calculated as EAT volume divided by absolute value of the EAT attenuation index, measured using coronary computed tomography angiography (CTA). The relationship between EAMI and MACE was analyzed using Kaplan-Meier curves, Cox regression, and restricted cubic spline (RCS) plots. The predictive performance of EAMI was assessed through receiver operating characteristic (ROC) curves, C-index, net reclassification index (NRI), integrated discriminant improvement (IDI), coefficient of determination (R Results: A total of 308 patients were included in the analysis, with 212 in the training set and 96 in the validation set. In the training set, Kaplan-Meier survival analysis revealed that higher EAMI levels were associated with an increased cumulative risk of MACE. Cox multivariate regression analysis indicated that EAMI was independently associated with MACE (HR = 2.349, 95% CI 1.770-3.177, Conclusion: EAMI has high value in predicting MACE in patients 1-year after STEMI, helps identify high-risk patients with poor prognosis in early clinical practice.
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