Acute myocardial infarction (AMI) remains a major cause of mortality and morbidity worldwide. Although percutaneous coronary intervention (PCI) combined with guideline-directed medical therapy has substantially improved survival, many patients continue to experience adverse cardiovascular events after revascularization, including recurrent ischemic events, heart failure, and malignant arrhythmias. Therefore, accurate risk stratification after PCI is essential for optimizing clinical management and improving long-term outcomes. However, currently available risk prediction tools are limited by suboptimal predictive performance or restricted clinical accessibility.
Cardiac imaging plays a central role in post-AMI evaluation. Echocardiography is routinely recommended after PCI to assess cardiac structure and function and to exclude early mechanical complications. Nevertheless, its ability to predict subsequent ventricular remodeling and long-term clinical outcomes is limited. Cardiac magnetic resonance imaging (CMR) is considered the reference standard for myocardial tissue characterization and provides important prognostic markers such as infarct size, microvascular obstruction, and intramyocardial hemorrhage. However, the routine use of CMR in patients with AMI is constrained by limited availability, high cost, and relatively long examination time. In addition, CMR does not directly evaluate coronary artery anatomy or plaque characteristics, which are important determinants of recurrent ischemic events.
Recent advances in cardiac computed tomography (CT) have enabled comprehensive assessment of coronary artery disease. Beyond evaluating coronary stenosis, coronary CT angiography can identify high-risk plaque features associated with plaque vulnerability. Furthermore, emerging CT-based techniques allow simultaneous assessment of coronary physiology, perivascular inflammation, myocardial structure and function, and myocardial tissue characteristics. CT-derived fractional flow reserve (CT-FFR) enables noninvasive functional assessment of coronary lesions, while the fat attenuation index (FAI) reflects pericoronary inflammatory activity. In addition, delayed iodine enhancement and dual-energy CT techniques allow evaluation of myocardial injury and fibrosis, and CT-derived extracellular volume fraction (ECV) provides quantitative assessment of myocardial fibrosis. These advances suggest that multimodal cardiac CT may provide an integrated imaging approach for comprehensive risk assessment after AMI.
The COMET-AMI study (Coronary and Myocardial Evaluation by Multimodal Cardiac CT in Acute Myocardial Infarction) is a prospective, multicenter cohort study designed to evaluate the prognostic value of multimodal cardiac CT in patients with AMI after PCI. A total of 1,000 patients with AMI undergoing successful PCI will be prospectively enrolled. All participants will undergo multimodal cardiac CT within 7 days after PCI to assess coronary plaque characteristics, coronary physiology, pericoronary adipose tissue inflammation, cardiac structure and function, and myocardial tissue features. A subset of participants will also undergo cardiac magnetic resonance imaging for validation of CT-derived myocardial tissue parameters.
Comprehensive clinical information, including demographic characteristics, cardiovascular risk factors, medication history, laboratory biomarkers, and procedural data, will be collected. Quantitative and qualitative analyses of coronary plaques will be performed using dedicated software to assess plaque burden, plaque composition, remodeling characteristics, and high-risk plaque features. Additional CT-derived parameters such as CT-FFR, myocardial strain, pericoronary fat attenuation index, and extracellular volume fraction will also be analyzed.
Participants will be followed longitudinally through outpatient visits, telephone interviews, and electronic medical record review for up to five years after PCI. The primary outcomes include composite thrombotic events (cardiac death, recurrent myocardial infarction, and urgent or clinically driven revascularization). Secondary outcomes include heart failure and major arrhythmic events such as new-onset heart failure, sustained ventricular arrhythmia, implantable cardioverter-defibrillator implantation, sudden cardiac death, or resuscitated cardiac arrest. All clinical events will be independently adjudicated by a blinded clinical events committee.
Using multimodal cardiac CT-derived imaging biomarkers in combination with clinical and laboratory data, machine learning-based predictive models will be developed to identify key determinants of adverse outcomes after PCI in patients with AMI. The results of this study may provide a novel imaging-based risk stratification strategy and facilitate personalized management for patients with AMI after PCI.
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