Clinical trial NCT06397820: Relation Between AI-QCA and Cardiac PET
Official title: Relation Between Artificial Intelligence (AI)-Assisted Quantitative Coronary Angiography and Positron Emission Tomography-Derived Myocardial Blood Flow
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Coronary Artery Disease
Coronary Artery Stenosis
Keywords: Invasive coronary angiography
Cardaic positron Emission Tomography
Artificial Intelligence
Quantitative Coronary Angiography
Lead sponsor: Chonnam National University Hospital
Sponsor class: OTHER
First posted: 2024-05-03
Enrollment: 168
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with suspected coronary artery disease (CAD) undergoing invasive coronary angiography (CAG) and clinically indicated for cardiac PET assessment.

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.

Eligibility criteria: 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%)

Primary outcomes:
measure: Correlation between diameter stenosis by AI-QCA and PET-driven RFR
timeFrame: Immediate after AI-QCA and PET exams
measure: Correlation between diameter stenosis by AI-QCA and PET-driven stress MBF
timeFrame: Immediate after AI-QCA and PET exams
Secondary outcomes:
measure: Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow reserve (CFR)
timeFrame: Immediate after AI-QCA and PET exams
measure: Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow capacity (CFC)
timeFrame: Immediate after AI-QCA and PET exams
measure: Correlation between diameter stenosis by AI-QCA and PET-driven semi-quantitative markers of ischemia
timeFrame: Immediate after AI-QCA and PET exams
measure: All-cause death
timeFrame: 1 year after last patient enrollment
measure: Cardiovascular death
timeFrame: 1 year after last patient enrollment
measure: Myocardial infarction
timeFrame: 1 year after last patient enrollment
measure: Rate of target lesion revascularization
timeFrame: 1 year after last patient enrollment
measure: Rate of target vessel revascularization
timeFrame: 1 year after last patient enrollment
measure: Rate of any revascularization
timeFrame: 1 year after last patient enrollment
measure: Rate of stent thrombosis
timeFrame: 1 year after last patient enrollment
measure: Rate of cerebrovascular accident
timeFrame: 1 year after last patient enrollment
measure: Major adverse cerebrocardiovascular event (MACCE)
timeFrame: 1 year after last patient enrollment
Other outcomes:
Locations:
city: Gwangju
country: Korea, Republic of
facility: Chonnam National University Hospital
Study officials:
affiliation: Chonnam National University Hospital
name: Sang-Geon Cho, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Chonnam National University Hospital
name: Seung Hun Lee, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: