Clinical trial NCT06341361: OCT-based Machine Learning FFR for Predicting Post-PCI FFR
Official title: Optical Coherence Tomography-based Machine Learning for Predicting Fractional Flow Reserve After Coronary Artery Stenting
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Tomography, Optical Coherence
Fractional Flow Reserve, Myocardial
Lead sponsor: Yonsei University
Sponsor class: OTHER
Collaborators: name: Gangnam Severance Hospital
name: Severance Hospital
First posted: 2024-04-02
Enrollment: 82
Sex: ALL
Minimum age (years): 19
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.

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

Primary outcomes:
measure: Correlation of OCT-based machine learning FFR compared to wire-based FFR
timeFrame: 4 weeks
Secondary outcomes:
measure: Diagnostic performance of OCT-based machine learning FFR compared to wire-based FFR
timeFrame: 4 weeks
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
Other outcomes:
Locations:
Study officials:
affiliation: Severance Cardiovascular Hospital, Yonsei University College of Medicine
name: Jung-Sun Kim, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: