Ignite Creation Date:
2025-12-24 @ 12:22 PM
Ignite Modification Date:
2026-02-23 @ 2:21 AM
Study NCT ID:
NCT06341361
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-04-02
First Post:
2024-03-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
OCT-based Machine Learning FFR for Predicting Post-PCI FFR
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 82}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2024-04-15', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-03', 'completionDateStruct': {'date': '2025-10-15', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-03-26', 'studyFirstSubmitDate': '2024-03-26', 'studyFirstSubmitQcDate': '2024-03-26', 'lastUpdatePostDateStruct': {'date': '2024-04-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-04-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-04-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Correlation of OCT-based machine learning FFR compared to wire-based FFR', 'timeFrame': '4 weeks', 'description': 'Determining the diagnostic accuracy of CT-FFR values obtained by the new method compared with invasive coronary angiography with fractional flow reserve'}], 'secondaryOutcomes': [{'measure': 'Diagnostic performance of OCT-based machine learning FFR compared to wire-based FFR', 'timeFrame': '4 weeks', 'description': 'Accuracy, sensitivity, specificity, positive predictive value, negative predictive value'}, {'measure': 'Diagnostic performance of OCT-based machine learning FFR according to the coronary artery (LAD, LCx or RCA) compared to wire-based FFR', 'timeFrame': '4 weeks', 'description': 'Accuracy, sensitivity, specificity, positive predictive value, negative predictive value'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Tomography, Optical Coherence', 'Fractional Flow Reserve, Myocardial']}, 'descriptionModule': {'briefSummary': 'This study aims to compare the diagnostic accuracy of the fractional flow reserve (FFR) model derived by machine learning based on optical coherence tomography (OCT) exam after coronary artery stent implantation with the wire-based FFR.', 'detailedDescription': "FFR and OCT exam are used for different purposes during percutaneous coronary intervention (PCI). The FFR is a decision-making tool to determine if additional procedures are necessary, while the OCT exam is used to optimize the stent procedure. The use of both tests provides additional information to help perform a excellent procedure, but it is more expensive and time-consuming.\n\nTherefore, an OCT-derived machine learning FFR test may be helpful. Previous studies have demonstrated that OCT-based machine learning FFR before the procedure has shown good diagnostic performance in predicting FFR, irrespective of the coronary territory.\n\nDespite the rapid development of technologies and tools for PCI, a significant number of patients experienced adverse events, such as recurrence of angina and silent ischemia despite angiographically successful PCI. Suboptimal PCI is a well-known independent prognostic factor for major cardiovascular accidents. Therefore, measuring post-PCI FFR immediately after stent implantation is crucial to optimize the procedure outcome and improve the patient's prognosis. Although the importance of measuring post-PCI FFR is gradually emerging, there is currently no model for OCT-based machine learning FFR that predicts FFR after stent insertion. In patients who underwent percutaneous coronary intervention using stents for ischemic heart disease, we will compare the diagnostic accuracy of the fractional flow reserve (FFR) model derived by machine learning based on optical coherence tomography (OCT) exam after coronary artery stent implantation with the wire-based FFR."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '19 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Among patients who underwent PCI for ischemic heart disease, those who underwent both OCT examination and pressure wire-based FFR after coronary artery stenting.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Patients who underwent stent implantation for ischemic heart disease\n2. Patients who underwent both OCT examination and FFR using a pressure wire after PCI\n\nExclusion Criteria:\n\n1. Poor OCT imaging quality\n2. Patients with severe left ventricular dysfunction (\\<30%)\n3. Patients with severe valvular heart disease\n4. Patients with a life expectancy of less than 1 year'}, 'identificationModule': {'nctId': 'NCT06341361', 'briefTitle': 'OCT-based Machine Learning FFR for Predicting Post-PCI FFR', 'organization': {'class': 'OTHER', 'fullName': 'Yonsei University'}, 'officialTitle': 'Optical Coherence Tomography-based Machine Learning for Predicting Fractional Flow Reserve After Coronary Artery Stenting', 'orgStudyIdInfo': {'id': 'OCT-FFR'}}, 'armsInterventionsModule': {'interventions': [{'name': 'OCT-based machine learning FFR', 'type': 'DIAGNOSTIC_TEST', 'description': 'OCT-based machine learning FFR and wire-based FFR'}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Oh-Hyun Lee, MD', 'role': 'CONTACT', 'email': 'Decenthyun@yuhs.ac', 'phone': '+82-31-5189-8786'}], 'overallOfficials': [{'name': 'Jung-Sun Kim, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Severance Cardiovascular Hospital, Yonsei University College of Medicine'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Yonsei University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Gangnam Severance Hospital', 'class': 'OTHER'}, {'name': 'Severance Hospital', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Jung-Sun Kim', 'investigatorAffiliation': 'Yonsei University'}}}}