Tomography, Optical CoherenceNCT06341361
OCT-based Machine Learning FFR for Predicting Post-PCI FFR
An observational study of Tomography, Optical Coherence and Fractional Flow Reserve, Myocardial, run by Yonsei University.
Trial 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. Read more about this study
- Who is it for?
- Ages 19 years and older
- Conditions the study is for
- Tomography, Optical Coherence, Fractional Flow Reserve, Myocardial
- What is being tested?
- OCT-based machine learning FFR
- How many people
- 82 participants
- Where
- No sites listed yet
- Study dates
- 15 Apr 2024to 15 Oct 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 years and older
- All sexes
- No healthy volunteers
- Adult, Older 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.
Who may be able to join
- Patients who underwent stent implantation for ischemic heart disease
- Patients who underwent both OCT examination and FFR using a pressure wire after PCI
Who cannot join
- 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
Trial locations and contacts
No sites are listed yet.
About this study
Official title: 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.
Read the 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.
- Being tested
- OCT-based machine learning FFR
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
OCT-based machine learning FFRDiagnostic test
OCT-based machine learning FFR and wire-based FFR
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 4 weeks
Secondary outcomes (2)
Diagnostic performance of OCT-based machine learning FFR compared to wire-based FFR
Accuracy, sensitivity, specificity, positive predictive value, negative predictive value
Measured: 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
Measured: 4 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Apr 2024
- Study start15 Apr 2024 (estimated)
- Primary completion30 Apr 2024 (estimated)
- Study completion15 Oct 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Tomography, Optical CoherenceStudies of the same condition, open and under way.
- More trials run by Yonsei UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 82 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yonsei University
- Sponsor type
- University, hospital or other
- Collaborators
- Gangnam Severance Hospital, Severance Hospital
- Responsible party
- Principal investigator, Jung-Sun Kim, Yonsei University
- Investigators
- Jung-Sun Kim, MD, PhD, Principal investigator, Severance Cardiovascular Hospital, Yonsei University College of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06341361
- Sponsor's protocol ID
- OCT-FFR
- Status last verified
- Mar 2024
- First submitted
- 26 Mar 2024
- Last updated
- 2 Apr 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Optical Coherence Tomography-based Machine Learning for Predicting Fractional Flow Reserve After Coronary Artery Stenting
- ClinicalTrials.gov ID
- NCT06341361
- Acronym / sponsor ID
- OCT-FFR
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 82 participants (estimated)
- Lead sponsor
- Yonsei UniversityUniversity, hospital or other
- Collaborators
- Gangnam Severance Hospital, Severance Hospital
- Study start
- 15 Apr 2024 (estimated)
- Primary completion
- 30 Apr 2024 (estimated)
- Study completion
- 15 Oct 2025 (estimated)
- First posted
- 2 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older 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.
Key inclusion criteria (2 listed)
- Patients who underwent stent implantation for ischemic heart disease
- Patients who underwent both OCT examination and FFR using a pressure wire after PCI
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| OCT-based machine learning FFROCT-based machine learning FFR and wire-based FFR | Diagnostic test | – |
Primary outcome
Correlation of OCT-based machine learning FFR compared to wire-based FFR
Time frame: 4 weeks
Determining the diagnostic accuracy of CT-FFR values obtained by the new method compared with invasive coronary angiography with fractional flow reserve
2 secondary outcomes
Diagnostic performance of OCT-based machine learning FFR compared to wire-based FFR
Time frame: 4 weeks
Diagnostic performance of OCT-based machine learning FFR according to the coronary artery (LAD, LCx or RCA) compared to wire-based FFR
Time frame: 4 weeks
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Apr 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jung-Sun Kim, MD, PhD principal investigator
Severance Cardiovascular Hospital, Yonsei University College of Medicine
Questions about this trial
What is this clinical trial studying?
An observational study of Tomography, Optical Coherence and Fractional Flow Reserve, Myocardial, run by Yonsei University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 2 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Oh-Hyun Lee, MD (+82-31-5189-8786, Decenthyun@yuhs.ac), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Correlation of OCT-based machine learning FFR compared to wire-based FFR (time frame: 4 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Apr 2024.