Relation Between AI-QCA and Cardiac PET

Recruiting

• Conditions: Coronary Artery Disease; Coronary Artery Stenosis
• Interventions: Device: Percutaneous coronary intervention (PCI)

• Registration Number: NCT06397820
• Lead Sponsor: Chonnam National University Hospital

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.

Study Timeline

• Study Start: 2021-09-01
• Study First Submitted: 2024-04-30
• Study First Submitted QC: 2024-04-30
• Status Verified: 2024-05
• Last Update Submitted: 2024-05-02
• Study First Posted: 2024-05-03
• Last Update Posted: 2024-05-06
• Primary Completion: 2024-07-31
• Study Completion: 2026-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 450

Inclusion Criteria

Not provided

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Exclusion Criteria

Not provided

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Positive for PET-derived indexes	Percutaneous coronary intervention (PCI)	Patients who had decreased stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET

Primary Outcome Measures

Name	Time	Method
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
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

Secondary Outcome Measures

Name	Time	Method
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
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
Myocardial infarction	1 year after last patient enrollment	Any myocardial infarction, defined by Forth Universal definition of myocardial infarction
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
Cardiovascular death	1 year after last patient enrollment	Cardiovascular death
Rate of target vessel revascularization	1 year after last patient enrollment	Target vessel revascularization
All-cause death	1 year after last patient enrollment	All-cause death
Rate of cerebrovascular accident	1 year after last patient enrollment	Cerebrovascular accident
Rate of stent thrombosis	1 year after last patient enrollment	Definite or probable stent thrombosis, defined by ARC II definition
Rate of target lesion revascularization	1 year after last patient enrollment	Target lesion revascularization
Rate of any revascularization	1 year after last patient enrollment	Any revascularization
Major adverse cerebrocardiovascular event (MACCE)	1 year after last patient enrollment	A composite of death, myocardial infarction, any revascularization, and cerebrovascular accident

Trial Locations

Locations (1)

Chonnam National University Hospital; 🇰🇷 Gwangju, Korea, Republic of