Coronary Artery Disease, Coronary Artery Stenosis
Conditions
Keywords
Invasive coronary angiography, Cardaic positron Emission Tomography, Artificial Intelligence, Quantitative Coronary Angiography
Brief summary
The aim of the study is to evaluate the clinical implications of artificial Intelligence (AI)-assisted quantitative coronary angiography (QCA) and positron emission tomography (PET)-derived myocardial blood flow in clinically indicated patients.
Detailed description
Percutaneous coronary angiography (CAG) is a standard method for evaluating coronary artery disease. Traditionally, a reduction in the luminal diameter of the coronary arteries by 50% or more during angiography has been considered a significant stenotic lesion. However, the assessment of coronary artery stenosis is usually based on visual estimation by the operator in daily routine clinical practice, which interferes with the objective evaluation. Quantitative coronary angiography (QCA) has been developed to overcome this limitation. This technique involves the software-based analysis of coronary images obtained through CAG. The previous study showed that there was low concordance between the QCA and visual estimation of coronary artery stenosis (Kappa=0.63) and a reclassification rate of approximately 20%. Furthermore, visual assessments tended to overestimate the degree of coronary artery stenosis, particularly in complex lesions such as bifurcation lesions. However, there are some limitations to adopting QCA in our daily routine practice. The QCA cannot analyze coronary images on-site and is not fully automated, requiring manual adjustments by humans. Recent advancements have led to the development of artificial intelligence (AI)-based QCA software, which achieves complete automation in the analysis process and provides real-time objective evaluations of coronary artery stenosis. This study aims to examine the clinical significance of AI-QCA by assessing the correlation between the degree of coronary stenosis detected by AI-QCA and myocardial blood flow abnormalities observed in 13NH3-Ammonia PET scans in patients with coronary artery disease.
Interventions
DEVICEPercutaneous coronary intervention (PCI)
Revascularization by percutaneous coronary intervention for vessels with decreased PET-derived flow indexes
Sponsors
Chonnam National University Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Subject must be ≥18 years 2. Patients suspected with CAD or ischemic heart disease 3. Patients undergoing CAG and cardiac PET for evaluation of severity of coronary artery disease
Exclusion criteria
1. Poor imaging quality of CAG and PET which were not available for core-lab analysis 2. Chronic total occlusion 3. Time interval was more than \>3 months between CAG and PET 4. History of coronary artery bypass grafting 5. History of acute myocardial infarction or recent myocardial infarction 6. Heart failure (left ventricular ejection fraction \<40%)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation between diameter stenosis by AI-QCA and PET-driven RFR | Immediate after AI-QCA and PET exams | Performance of AI-QCA predicting for PET-driven RFR |
| Correlation between diameter stenosis by AI-QCA and PET-driven stress MBF | Immediate after AI-QCA and PET exams | Performance of AI-QCA predicting for PET-driven stress MBF |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlation between diameter stenosis by AI-QCA and PET-driven semi-quantitative markers of ischemia | Immediate after AI-QCA and PET exams | Performance of AI-QCA predicting for PET-driven semi-quantitative markers of ischemia |
| All-cause death | 1 year after last patient enrollment | All-cause death |
| Cardiovascular death | 1 year after last patient enrollment | Cardiovascular death |
| Myocardial infarction | 1 year after last patient enrollment | Any myocardial infarction, defined by Forth Universal definition of myocardial infarction |
| Rate of target lesion revascularization | 1 year after last patient enrollment | Target lesion revascularization |
| Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow reserve (CFR) | Immediate after AI-QCA and PET exams | Performance of AI-QCA predicting for PET-driven CFR |
| Rate of any revascularization | 1 year after last patient enrollment | Any revascularization |
| Rate of stent thrombosis | 1 year after last patient enrollment | Definite or probable stent thrombosis, defined by ARC II definition |
| Rate of cerebrovascular accident | 1 year after last patient enrollment | Cerebrovascular accident |
| Major adverse cerebrocardiovascular event (MACCE) | 1 year after last patient enrollment | A composite of death, myocardial infarction, any revascularization, and cerebrovascular accident |
| Rate of target vessel revascularization | 1 year after last patient enrollment | Target vessel revascularization |
| Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow capacity (CFC) | Immediate after AI-QCA and PET exams | Performance of AI-QCA predicting for PET-driven CFC |
Countries
South Korea
Outcome results
None listed