Clinical Impact of Intravascular Ultrasound-Based Artificial Intelligence Technologies (INNOVATE-PCI)

ClinicalTrials.gov ID

NCT05807841

Lead Sponsor

Asan Medical CenterOTHER

Overall Status

Recruiting

Study Type

Observational

Enrollment

3,000Estimated

Last Update Posted

Jun 25, 2026Actual

Start Date

Feb 20, 2020Actual

Primary Completion Date

Dec 31, 2028Estimated

Completion Date

Dec 31, 2029Estimated

Official Title

Influence of Novel Intravascular Ultrasound-Based Artificial Intelligence Technologies on Event Reduction Following Percutaneous Coronary Intervention (INNOVATE-PCI)

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 2027, a total of 3,000 patients who performed coronary angiography (± FFR) and have at least one coronary stenosis requiring PCI (as culprit) will be enrolled from 16 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.

• Coronary Artery Disease

• percutaneous coronary intervention

Ages Eligible for Study

Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

No

Eligibility Criteria

Inclusion Criteria:

• Age 19 years or older
• Symptomatic angina patients with objective myocardial ischemia
• Patients with at least one major epicardial coronary artery that requires stent implantation
• Subject who signs with informed consent form

Patient exclusion Criteria:

• ST-segment elevation MI at admission
• Patients who underwent coronary artery bypass surgery or heart transplantation
• Left ventricular ejection fraction <30%
• Cardiogenic shock
• Patients whose life expectancy <2 years
• Woman who are breastfeeding, pregnant or planning to become pregnant during study
• Patients in whom anti-platelets or heparin is contraindicated

Lesion exclusion Criteria:

• Left main culprit lesion (angiographic diameter stenosis >50%)
• Thrombus-containing lesion
• In-stent restenosis
• Side branch lesion
• Chronic total occlusion
• Small vessel with reference diameter <2.5mm
• Coronary spasm despite administration of nitrate
• Inability for imaging catheter to pass through tight stenosis, calcification, angulations
• Poor image quality
• Angiographically visible collateral vessels

Observational Model

Case-Only

Time Perspective

Prospective

Lead Sponsor

Asan Medical CenterOTHER

Collaborators

Boston Scientific CorporationINDUSTRY

seungwlee@amc.seoul.kr82-10-7398-9897

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