OCT-based Machine Learning FFR for Predicting Post-PCI FFR | NCT06341361

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

NCT06341361

Sponsor

Yonsei University

Status

Unknown status

Last Update Posted

Apr 2, 2024Actual

Enrollment

82Estimated

Phase

Not provided

Conditions

Tomography, Optical Coherence
Fractional Flow Reserve, Myocardial

Interventions

OCT-based machine learning FFR

Countries

Not provided

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Mar 2024

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Not yet recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Apr 15, 2024Estimated

Primary Completion Date

Apr 30, 2024Estimated

Completion Date

Oct 15, 2025Estimated

First Submitted Date

Mar 26, 2024

First Submission Date that Met QC Criteria

Mar 26, 2024

First Posted Date

Apr 2, 2024Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Mar 26, 2024

Last Update Posted Date

Apr 2, 2024Actual

Name	Class
Gangnam Severance Hospital	OTHER
Severance Hospital	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

82Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
OCT-based machine learning FFR	Diagnostic Test	OCT-based machine learning FFR and wire-based FFR

Outcomes Module

Primary Outcomes

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 Outcomes

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

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Patients who underwent stent implantation for ischemic heart disease
Patients who underwent both OCT examination and FFR using a pressure wire after PCI

Exclusion Criteria:

Poor OCT imaging quality
Patients with severe left ventricular dysfunction (<30%)
Patients with severe valvular heart disease
Patients with a life expectancy of less than 1 year

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

19 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

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.

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Oh-Hyun Lee, MD	Contact	+82-31-5189-8786		Decenthyun@yuhs.ac

Overall Officials

Name	Affiliation	Role
Jung-Sun Kim, MD, PhD	Severance Cardiovascular Hospital, Yonsei University College of Medicine	Principal Investigator

Locations

Not provided

IPD Sharing Statement Module

Plan to Share IPD

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided