Relation Between AI-QCA and Cardiac PET

Coronary Artery Disease Clinical Trial

— AI-CARPET  

Official title:

Relation Between Artificial Intelligence (AI)-Assisted Quantitative Coronary Angiography and Positron Emission Tomography-Derived Myocardial Blood Flow

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06397820
• Other study ID #: CNUH-AI-CARPET
• Status: Recruiting
• Start date: September 1, 2021
• Est. completion date: December 31, 2026

Study information

• Verified date: May 2024
• Source: Chonnam National University Hospital
• Contact: Seung Hun Lee, MD, PhD
• Phone: 82-62-220-6246
• Email: lsh8602[@]naver.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• 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%)

Related Conditions & MeSH terms

• Coronary Artery Disease
• Coronary Artery Stenosis
• Coronary Stenosis

Intervention

Device:
• Percutaneous coronary intervention (PCI)
Revascularization by percutaneous coronary intervention for vessels with decreased PET-derived flow indexes

Locations

Country	Name	City	State
Korea, Republic of	Chonnam National University Hospital	Gwangju

Sponsors (1)

Lead Sponsor	Collaborator
Chonnam National University Hospital

Country where clinical trial is conducted

Korea, Republic of, 

Outcome

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