Ignite Creation Date:
2024-05-06 @ 6:50 PM
Last Modification Date:
2024-10-26 @ 2:56 PM
Study NCT ID:
NCT05807841
Status:
RECRUITING
Last Update Posted:
2023-11-27
First Post:
2023-03-29
Brief Title:
Clinical Impact of Intravascular Ultrasound-Based Artificial Intelligence Technologies INNOVATE-PCI
Sponsor:
Asan Medical Center
Organization:
Asan Medical Center
Study Overview
Official Title:
Influence of Novel Intravascular Ultrasound-Based Artificial Intelligence Technologies on Event Reduction Following Percutaneous Coronary Intervention INNOVATE-PCI
Status:
RECRUITING
Status Verified Date:
2024-08
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 is a prospective multicenter study in the real practice to validate the diagnostic performances and clinical impact of coronary angiography intravascular ultrasound IVUS-based models developed by machine learning ML
Detailed Description:
The aim of the study is to evaluate the performances and prognostic impact of coronary angiography IVUS-based algorithms for decision making and stent optimization in a multicenter prospective cohort Between January 2020 and June 2025 a total of 3000 patients who performed coronary angiography FFR and have at least one coronary stenosis requiring PCI as culprit will be enrolled from 15 centers in South Korea In addition the deferred lesions with visual estimated diameter stenosis of 30 will be evaluated as non-culprits Brief study design is as depicted in the following figure
Supervised ML algorithms include 1 angiography- and IVUS-based algorithms for predicting FFR 2 IVUS-based algorithm for plaque characterization 3 IVUS-based algorithm for predicting stent expansion and 4 post-stenting IVUS-based algorithm for predicting stent failure In the prospective cohort the performance of each model will be assessed This registry trial composed of the treated culprit and the deferred nonculprit coronary lesions has two primary objectives as follow 1 Primary objectives in treated culprit lesions is to see the impact of the integrated ML model on the development of culprit-related 2-year target vessel failure TVF 2 Primary objectives in deferred nonculprit lesions is to see the impact of the integrated ML model on the development of nonculprit-related 2-year TVF
Supervised ML algorithms include 1 angiography- and IVUS-based algorithms for predicting FFR 2 IVUS-based algorithm for plaque characterization 3 IVUS-based algorithm for predicting stent expansion and 4 post-stenting IVUS-based algorithm for predicting stent failure In the prospective cohort the performance of each model will be assessed This registry trial composed of the treated culprit and the deferred nonculprit coronary lesions has two primary objectives as follow 1 Primary objectives in treated culprit lesions is to see the impact of the integrated ML model on the development of culprit-related 2-year target vessel failure TVF 2 Primary objectives in deferred nonculprit lesions is to see the impact of the integrated ML model on the development of nonculprit-related 2-year TVF
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