Ignite Creation Date:
2025-12-25 @ 4:13 AM
Ignite Modification Date:
2025-12-26 @ 3:13 AM
Study NCT ID:
NCT06397820
Status:
COMPLETED
Last Update Posted:
2025-02-24
First Post:
2024-04-30
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Relation Between AI-QCA and Cardiac PET
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003324', 'term': 'Coronary Artery Disease'}, {'id': 'D023921', 'term': 'Coronary Stenosis'}], 'ancestors': [{'id': 'D003327', 'term': 'Coronary Disease'}, {'id': 'D017202', 'term': 'Myocardial Ischemia'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D001161', 'term': 'Arteriosclerosis'}, {'id': 'D001157', 'term': 'Arterial Occlusive Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D062645', 'term': 'Percutaneous Coronary Intervention'}], 'ancestors': [{'id': 'D057510', 'term': 'Endovascular Procedures'}, {'id': 'D014656', 'term': 'Vascular Surgical Procedures'}, {'id': 'D013504', 'term': 'Cardiovascular Surgical Procedures'}, {'id': 'D013514', 'term': 'Surgical Procedures, Operative'}, {'id': 'D019060', 'term': 'Minimally Invasive Surgical Procedures'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 168}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2021-09-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-02', 'completionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-02-20', 'studyFirstSubmitDate': '2024-04-30', 'studyFirstSubmitQcDate': '2024-04-30', 'lastUpdatePostDateStruct': {'date': '2025-02-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-05-03', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-07-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Correlation between diameter stenosis by AI-QCA and PET-driven RFR', 'timeFrame': 'Immediate after AI-QCA and PET exams', 'description': 'Performance of AI-QCA predicting for PET-driven RFR'}, {'measure': 'Correlation between diameter stenosis by AI-QCA and PET-driven stress MBF', 'timeFrame': 'Immediate after AI-QCA and PET exams', 'description': 'Performance of AI-QCA predicting for PET-driven stress MBF'}], 'secondaryOutcomes': [{'measure': 'Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow reserve (CFR)', 'timeFrame': 'Immediate after AI-QCA and PET exams', 'description': 'Performance of AI-QCA predicting for PET-driven CFR'}, {'measure': 'Correlation between diameter stenosis by AI-QCA and PET-driven coronary flow capacity (CFC)', 'timeFrame': 'Immediate after AI-QCA and PET exams', 'description': 'Performance of AI-QCA predicting for PET-driven CFC'}, {'measure': 'Correlation between diameter stenosis by AI-QCA and PET-driven semi-quantitative markers of ischemia', 'timeFrame': 'Immediate after AI-QCA and PET exams', 'description': 'Performance of AI-QCA predicting for PET-driven semi-quantitative markers of ischemia'}, {'measure': 'All-cause death', 'timeFrame': '1 year after last patient enrollment', 'description': 'All-cause death'}, {'measure': 'Cardiovascular death', 'timeFrame': '1 year after last patient enrollment', 'description': 'Cardiovascular death'}, {'measure': 'Myocardial infarction', 'timeFrame': '1 year after last patient enrollment', 'description': 'Any myocardial infarction, defined by Forth Universal definition of myocardial infarction'}, {'measure': 'Rate of target lesion revascularization', 'timeFrame': '1 year after last patient enrollment', 'description': 'Target lesion revascularization'}, {'measure': 'Rate of target vessel revascularization', 'timeFrame': '1 year after last patient enrollment', 'description': 'Target vessel revascularization'}, {'measure': 'Rate of any revascularization', 'timeFrame': '1 year after last patient enrollment', 'description': 'Any revascularization'}, {'measure': 'Rate of stent thrombosis', 'timeFrame': '1 year after last patient enrollment', 'description': 'Definite or probable stent thrombosis, defined by ARC II definition'}, {'measure': 'Rate of cerebrovascular accident', 'timeFrame': '1 year after last patient enrollment', 'description': 'Cerebrovascular accident'}, {'measure': 'Major adverse cerebrocardiovascular event (MACCE)', 'timeFrame': '1 year after last patient enrollment', 'description': 'A composite of death, myocardial infarction, any revascularization, and cerebrovascular accident'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Invasive coronary angiography', 'Cardaic positron Emission Tomography', 'Artificial Intelligence', 'Quantitative Coronary Angiography'], 'conditions': ['Coronary Artery Disease', 'Coronary Artery Stenosis']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.\n\nQuantitative 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.\n\nHowever, 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.\n\nThis 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with suspected coronary artery disease (CAD) undergoing invasive coronary angiography (CAG) and clinically indicated for cardiac PET assessment.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion criteria\n\n1. Subject must be ≥18 years\n2. Patients suspected with CAD or ischemic heart disease\n3. Patients undergoing CAG and cardiac PET for evaluation of severity of coronary artery disease\n\nExclusion criteria\n\n1. Poor imaging quality of CAG and PET which were not available for core-lab analysis\n2. Chronic total occlusion\n3. Time interval was more than \\>3 months between CAG and PET\n4. History of coronary artery bypass grafting\n5. History of acute myocardial infarction or recent myocardial infarction\n6. Heart failure (left ventricular ejection fraction \\<40%)'}, 'identificationModule': {'nctId': 'NCT06397820', 'acronym': 'AI-CARPET', 'briefTitle': 'Relation Between AI-QCA and Cardiac PET', 'organization': {'class': 'OTHER', 'fullName': 'Chonnam National University Hospital'}, 'officialTitle': 'Relation Between Artificial Intelligence (AI)-Assisted Quantitative Coronary Angiography and Positron Emission Tomography-Derived Myocardial Blood Flow', 'orgStudyIdInfo': {'id': 'CNUH-AI-CARPET'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Positive for PET-derived indexes', 'description': 'Patients who had decreased stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET', 'interventionNames': ['Device: Percutaneous coronary intervention (PCI)']}, {'label': 'Negative for PET-derived indexes', 'description': 'Patients who had preserved stress myocardial blood flow (MBF) or relative flow ratio (RFR) on PET'}], 'interventions': [{'name': 'Percutaneous coronary intervention (PCI)', 'type': 'DEVICE', 'description': 'Revascularization by percutaneous coronary intervention for vessels with decreased PET-derived flow indexes', 'armGroupLabels': ['Positive for PET-derived indexes']}]}, 'contactsLocationsModule': {'locations': [{'zip': '61469', 'city': 'Gwangju', 'country': 'South Korea', 'facility': 'Chonnam National University Hospital', 'geoPoint': {'lat': 35.15472, 'lon': 126.91556}}], 'overallOfficials': [{'name': 'Sang-Geon Cho, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Chonnam National University Hospital'}, {'name': 'Seung Hun Lee, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Chonnam National University Hospital'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'CSR'], 'timeFrame': 'After publication of main paper.', 'ipdSharing': 'YES', 'description': 'After publication of main paper, de-identified data will be shared upon reasonable requests after discussion by Executive Committee.', 'accessCriteria': 'Executive Committee will discuss to share the de-identified data upon reasonable requests.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chonnam National University Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Assistant Professor', 'investigatorFullName': 'Seung Hun Lee', 'investigatorAffiliation': 'Chonnam National University Hospital'}}}}