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

Optical Coherence Tomography-based Machine Learning for Predicting Fractional Flow Reserve After Coronary Artery Stenting

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Tomography, Optical Coherence; Fractional Flow Reserve, Myocardial
• Intervention / Treatment: Diagnostic test: OCT-based machine learning 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.

• Study Type: Observational
• Enrollment (Estimated): 82

Contacts and Locations

• Study Contact: Name: Oh-Hyun Lee, MD; Phone Number: +82-31-5189-8786; Email: Decenthyun@yuhs.ac

Participation Criteria

Eligibility Criteria

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

• Sampling Method: Non-Probability Sample

Study Population

Among patients who underwent PCI for ischemic heart disease, those who underwent both OCT examination and pressure wire-based FFR after coronary artery stenting.

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation of OCT-based machine learning FFR compared to wire-based FFR	Determining the diagnostic accuracy of CT-FFR values obtained by the new method compared with invasive coronary angiography with fractional flow reserve	4 weeks

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic performance of OCT-based machine learning FFR compared to wire-based FFR	Accuracy, sensitivity, specificity, positive predictive value, negative predictive value	4 weeks
Diagnostic performance of OCT-based machine learning FFR according to the coronary artery (LAD, LCx or RCA) compared to wire-based FFR	Accuracy, sensitivity, specificity, positive predictive value, negative predictive value	4 weeks

Collaborators and Investigators

• Sponsor: Yonsei University
• Collaborators: Gangnam Severance Hospital; Severance Hospital
• Investigators: Principal Investigator: Jung-Sun Kim, MD, PhD, Severance Cardiovascular Hospital, Yonsei University College of Medicine

Study record dates

Study Major Dates

• Study Start (Estimated): April 15, 2024
• Primary Completion (Estimated): April 30, 2024
• Study Completion (Estimated): October 15, 2025

Study Registration Dates

• First Submitted: March 26, 2024
• First Submitted That Met QC Criteria: March 26, 2024
• First Posted (Actual): April 2, 2024

Study Record Updates

• Last Update Posted (Actual): April 2, 2024
• Last Update Submitted That Met QC Criteria: March 26, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: OCT-FFR

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

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