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

Last updated: December 23, 2024
Sponsor: Asan Medical Center
Overall Status: Active - Recruiting

Phase

N/A

Condition

Cardiovascular Disease

Vascular Diseases

Hypercholesterolemia

Treatment

percutaneous coronary intervention

Clinical Study ID

NCT05807841
2020-0226
  • Ages > 19
  • All Genders

Study 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).

Eligibility Criteria

Inclusion

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 stentimplantation

  • Subject who signs with informed consent form

Exclusion

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

Study Design

Total Participants: 3000
Treatment Group(s): 1
Primary Treatment: percutaneous coronary intervention
Phase:
Study Start date:
February 20, 2020
Estimated Completion Date:
June 30, 2029

Study 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 3,000 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.

Connect with a study center

  • Soon Chun Hyang University Hospital Bucheon

    Bucheon,
    Korea, Republic of

    Active - Recruiting

  • Gosin University Gospel Hospital

    Busan,
    Korea, Republic of

    Active - Recruiting

  • Inje University Pusan Paik Hospital

    Busan,
    Korea, Republic of

    Active - Recruiting

  • Gyeongsang National University Changwon Hospital

    Changwon,
    Korea, Republic of

    Active - Recruiting

  • Kangwon National University Hospital

    Chuncheon,
    Korea, Republic of

    Active - Recruiting

  • Keimyung University Dongsan Medical Center

    Daegu,
    Korea, Republic of

    Active - Recruiting

  • The Catholic university of korea, daejeon st. mary's hospital

    Daejeon,
    Korea, Republic of

    Active - Recruiting

  • Gangneung Asan Hospital

    Gangneung,
    Korea, Republic of

    Active - Recruiting

  • Jesushospital

    Jeonju,
    Korea, Republic of

    Active - Recruiting

  • Chungnam National University Sejong Hospital

    Sejong,
    Korea, Republic of

    Active - Recruiting

  • Kangbuk Samsung Medical Center

    Seoul,
    Korea, Republic of

    Site Not Available

  • Seung-Whan Lee

    Seoul, 05505
    Korea, Republic of

    Active - Recruiting

  • The Catholic university of korea, Eunpyeong st. mary's hospital

    Seoul,
    Korea, Republic of

    Active - Recruiting

  • Veterans Hospital Service Medical Center

    Seoul,
    Korea, Republic of

    Active - Recruiting

  • The Catholic University of Korea ST.VINCENT'S Hospital

    Suwon,
    Korea, Republic of

    Active - Recruiting

  • Ulsan University Hospital

    Ulsan,
    Korea, Republic of

    Active - Recruiting

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