AI-quantified epicardial adipose tissue and prediction of future myocardial infarction in patients with cardiometabolic disease: a post-hoc analysis from the SCOT-HEART trial.
Authors
Affiliations (11)
Affiliations (11)
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
- Department of Cardiology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium.
- 1st Department of Cardiology, Medical University of Warsaw, Warsaw, Poland.
- Department of Medicine, Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Poznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland.
- Department of Cardiology, Kusatsu Heart Center, Kusatsu, Shiga, Japan.
- Department of Cardiology, Friedrich-Alexander-University Erlangen-Nurnberg, Erlangen, Germany.
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA. [email protected].
Abstract
Epicardial adipose tissue is gaining increasing interest as a cardiometabolic imaging biomarker, but its exact role in coronary artery disease is not fully understood. This study aimed to investigate the relationship between epicardial adipose tissue, coronary plaque characteristics, and risk of myocardial infarction in patients with suspected coronary artery disease, and in those with concomitant cardiometabolic disease. In a post-hoc analysis of the SCOT-HEART trial, epicardial adipose tissue volume and attenuation were quantified automatically from computed tomography (CT) angiography using deep-learning. Quantitative and high-risk coronary plaque characteristics were also assessed. The primary endpoint was fatal or non-fatal myocardial infarction. The study population consisted of 1770 patients (58 ± 9 years, 56% males) of whom 313 (18%) with cardiometabolic disease. Epicardial adipose tissue volume was higher in patients withcardiometabolic disease (123 ± 44 versus 88 ± 36 mL, p < 0.001), and increased with the coronary calcium score (0: 82 ± 35 mL, 1-400: 97 ± 38 mL, > 400: 113 ± 44 mL; p < 0.001), and low-attenuation plaque burden (burden ≤ 4%: 85 ± 36mL, burden > 4%: 103 ± 41mL; p < 0.001), while there were no interactions between these features and epicardial adipose tissue attenuation (p > 0.05 for all). During a median follow-up of 8.6 years, 82 (4.6%) patients experienced myocardial infarction. In the total study cohort, epicardial adipose tissue volume predicted myocardial infarction both in univariable analysis, and after adjustment for established markers of cardiovascular risk. In patients with cardiometabolic disease, epicardial adipose tissue volume independently predicted myocardial infarction after adjustment for clinical risk factors and plaque features but this relationship was not found in those without cardiometabolic disease. CT-derived Epicardial adipose tissue volume correlates with quantitative and high-risk plaque features, and independently predicts risk of myocardial infarction in patients with cardiometabolic disease.