OCT-based Machine Learning FFR for Predicting Post-PCI FFR

NCT ID: NCT06341361

Last Updated: 2024-04-02

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 82 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-04-15
• Study Completion Date: 2025-10-15

Brief Summary

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.

Detailed Description

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.

Therefore, 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.

Despite 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.

Conditions

Tomography, Optical Coherence; Fractional Flow Reserve, Myocardial

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

OCT-based machine learning FFR

OCT-based machine learning FFR and wire-based FFR

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

1. Patients who underwent stent implantation for ischemic heart disease

2. Patients who underwent both OCT examination and FFR using a pressure wire after PCI

Exclusion Criteria

1. Poor OCT imaging quality

2. Patients with severe left ventricular dysfunction (<30%)

3. Patients with severe valvular heart disease

4. Patients with a life expectancy of less than 1 year

• Minimum Eligible Age: 19 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Gangnam Severance Hospital

OTHER

Sponsor Role: collaborator

Severance Hospital

OTHER

Sponsor Role: collaborator

Yonsei University

OTHER

Sponsor Role: lead

Responsible Party

Jung-Sun Kim

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Jung-Sun Kim, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Severance Cardiovascular Hospital, Yonsei University College of Medicine

Central Contacts

Oh-Hyun Lee, MD

Role: CONTACT

Decenthyun@yuhs.ac

+82-31-5189-8786

Other Identifiers

OCT-FFR

Identifier Type: -

Identifier Source: org_study_id