An observational study in Coronary Artery Disease and Coronary Artery Stenosis, sponsored by Chonnam National University Hospital. Completed at 1 site in Korea, Republic of. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-02-24.
Sponsored by Chonnam National University Hospital · Observational
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.
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.
5,598 studies on the registry are indexed under Coronary Artery Disease; 957 are open to participants now.
This study's enrollment of 168 is below the median of 336 across 1,947 observational studies indexed under Coronary Artery Disease.
Browse Coronary Artery Disease studies →Chonnam National University Hospital is the lead sponsor of 70 studies on the registry; 14 are open to participants now.
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Patients with suspected coronary artery disease (CAD) undergoing invasive coronary angiography (CAG) and clinically indicated for cardiac PET assessment.
Exclusion criteria
Patients who had decreased stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET
Device: Percutaneous coronary intervention (PCI)
Patients who had preserved stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET
Revascularization by percutaneous coronary intervention for vessels with decreased PET-derived flow indexes
Correlation between diameter stenosis by AI-QCA and PET-driven RFR
Performance of AI-QCA predicting for PET-driven RFR
Time frame: Immediate after AI-QCA and PET exams
Correlation between diameter stenosis by AI-QCA and PET-driven stress MBF
Performance of AI-QCA predicting for PET-driven stress MBF
Time frame: Immediate after AI-QCA and PET exams
Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow reserve (CFR)
Performance of AI-QCA predicting for PET-driven CFR
Time frame: Immediate after AI-QCA and PET exams
Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow capacity (CFC)
Performance of AI-QCA predicting for PET-driven CFC
Time frame: Immediate after AI-QCA and PET exams
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
Time frame: Immediate after AI-QCA and PET exams
All-cause death
All-cause death
Time frame: 1 year after last patient enrollment
Cardiovascular death
Cardiovascular death
Time frame: 1 year after last patient enrollment
Myocardial infarction
Any myocardial infarction, defined by Forth Universal definition of myocardial infarction
Time frame: 1 year after last patient enrollment
Rate of target lesion revascularization
Target lesion revascularization
Time frame: 1 year after last patient enrollment
Rate of target vessel revascularization
Target vessel revascularization
Time frame: 1 year after last patient enrollment
Rate of any revascularization
Any revascularization
Time frame: 1 year after last patient enrollment
Rate of stent thrombosis
Definite or probable stent thrombosis, defined by ARC II definition
Time frame: 1 year after last patient enrollment
Rate of cerebrovascular accident
Cerebrovascular accident
Time frame: 1 year after last patient enrollment
Major adverse cerebrocardiovascular event (MACCE)
A composite of death, myocardial infarction, any revascularization, and cerebrovascular accident
Time frame: 1 year after last patient enrollment
Plan to share: Yes — After publication of main paper, de-identified data will be shared upon reasonable requests after discussion by Executive Committee.
Supporting information: Study protocol, Sap, Csr
No publications or documents are linked to this record.
This study is completed, as verified in Feb 2025. You cannot join it, but the record below documents what was studied.
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Chonnam National University Hospital