Ignite Creation Date:
2024-05-06 @ 8:20 PM
Last Modification Date:
2024-10-26 @ 3:25 PM
Study NCT ID:
NCT06341361
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-04-02
First Post:
2024-03-26
Brief Title:
OCT-based Machine Learning FFR for Predicting Post-PCI FFR
Sponsor:
Yonsei University
Organization:
Yonsei University
Study Overview
Official Title:
Optical Coherence Tomography-based Machine Learning for Predicting Fractional Flow Reserve After Coronary Artery Stenting
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 patients 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
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 patients 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None