Relation Between AI-QCA and Cardiac PET (AI-CARPET)

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

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.

Study Overview

• Status: Completed
• Conditions: Coronary Artery Disease; Coronary Artery Stenosis
• Intervention / Treatment: Device: Percutaneous coronary intervention (PCI)

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 Type: Observational
• Enrollment (Actual): 168

Contacts and Locations

Study Locations

• Korea, Republic of
  • Gwangju, Korea, Republic of, 61469
    • Chonnam National University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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%)

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Positive for PET-derived indexes; Patients who had decreased stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET	Device: 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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 Outcome Measures

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

Collaborators and Investigators

• Sponsor: Chonnam National University Hospital
• Investigators: Principal Investigator: Seung Hun Lee, MD, PhD, Chonnam National University Hospital; Principal Investigator: Sang-Geon Cho, MD, PhD, Chonnam National University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2021
• Primary Completion (Actual): July 31, 2024
• Study Completion (Actual): December 31, 2024

Study Registration Dates

• First Submitted: April 30, 2024
• First Submitted That Met QC Criteria: April 30, 2024
• First Posted (Actual): May 3, 2024

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 20, 2025
• Last Verified: February 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Invasive coronary angiography; Quantitative Coronary Angiography; Cardaic positron Emission Tomography
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Heart Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Coronary Disease; Myocardial Ischemia; Coronary Artery Disease; Coronary Stenosis
• Other Study ID Numbers: CNUH-AI-CARPET

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: After publication of main paper, de-identified data will be shared upon reasonable requests after discussion by Executive Committee.
• IPD Sharing Time Frame: After publication of main paper.
• IPD Sharing Access Criteria: Executive Committee will discuss to share the de-identified data upon reasonable requests.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No