Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
168 participants
OBSERVATIONAL
2021-09-01
2024-12-31
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Positive for PET-derived indexes
Patients who had decreased stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET
Percutaneous coronary intervention (PCI)
Revascularization by percutaneous coronary intervention for vessels with decreased PET-derived flow indexes
Negative for PET-derived indexes
Patients who had preserved stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET
No interventions assigned to this group
Interventions
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Percutaneous coronary intervention (PCI)
Revascularization by percutaneous coronary intervention for vessels with decreased PET-derived flow indexes
Eligibility Criteria
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Inclusion Criteria
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
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%)
18 Years
ALL
No
Sponsors
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Chonnam National University Hospital
OTHER
Responsible Party
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Seung Hun Lee
Assistant Professor
Principal Investigators
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Sang-Geon Cho, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Chonnam National University Hospital
Seung Hun Lee, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Chonnam National University Hospital
Locations
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Chonnam National University Hospital
Gwangju, , South Korea
Countries
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Other Identifiers
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CNUH-AI-CARPET
Identifier Type: -
Identifier Source: org_study_id
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