Coronary Artery Disease Clinical Trial
— AI-CARPETOfficial title:
Relation Between Artificial Intelligence (AI)-Assisted Quantitative Coronary Angiography and Positron Emission Tomography-Derived Myocardial Blood Flow
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.
Status | Recruiting |
Enrollment | 450 |
Est. completion date | December 31, 2026 |
Est. primary completion date | July 31, 2024 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | 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%) |
Country | Name | City | State |
---|---|---|---|
Korea, Republic of | Chonnam National University Hospital | Gwangju |
Lead Sponsor | Collaborator |
---|---|
Chonnam National University Hospital |
Korea, Republic of,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Correlation between diameter stenosis by AI-QCA and PET-driven RFR | Performance of AI-QCA predicting for PET-driven RFR | Immediate after AI-QCA and PET exams | |
Primary | Correlation between diameter stenosis by AI-QCA and PET-driven stress MBF | Performance of AI-QCA predicting for PET-driven stress MBF | Immediate after AI-QCA and PET exams | |
Secondary | Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow reserve (CFR) | Performance of AI-QCA predicting for PET-driven CFR | Immediate after AI-QCA and PET exams | |
Secondary | Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow capacity (CFC) | Performance of AI-QCA predicting for PET-driven CFC | Immediate after AI-QCA and PET exams | |
Secondary | Correlation between diameter stenosis by AI-QCA and PET-driven semi-quantitative markers of ischemia | Performance of AI-QCA predicting for PET-driven semi-quantitative markers of ischemia | Immediate after AI-QCA and PET exams | |
Secondary | All-cause death | All-cause death | 1 year after last patient enrollment | |
Secondary | Cardiovascular death | Cardiovascular death | 1 year after last patient enrollment | |
Secondary | Myocardial infarction | Any myocardial infarction, defined by Forth Universal definition of myocardial infarction | 1 year after last patient enrollment | |
Secondary | Rate of target lesion revascularization | Target lesion revascularization | 1 year after last patient enrollment | |
Secondary | Rate of target vessel revascularization | Target vessel revascularization | 1 year after last patient enrollment | |
Secondary | Rate of any revascularization | Any revascularization | 1 year after last patient enrollment | |
Secondary | Rate of stent thrombosis | Definite or probable stent thrombosis, defined by ARC II definition | 1 year after last patient enrollment | |
Secondary | Rate of cerebrovascular accident | Cerebrovascular accident | 1 year after last patient enrollment | |
Secondary | Major adverse cerebrocardiovascular event (MACCE) | A composite of death, myocardial infarction, any revascularization, and cerebrovascular accident | 1 year after last patient enrollment |
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