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

Yonsei University

Status: Not yet enrolling

Conditions: Fractional Flow Reserve, Myocardial; Tomography, Optical Coherence

Treatments: Diagnostic Test: OCT-based machine learning FFR

Study type: Observational

Funder types: Other

Identifiers

NCT06341361

OCT-FFR

Details and patient eligibility

About

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.

Full 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.

Enrollment: 82 estimated patients

Sex: All

Ages: 19+ years old

Volunteers: No Healthy Volunteers

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