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

NCT ID: NCT05807841

Last Updated: 2024-12-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

3000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-02-20

Study Completion Date

2029-06-30

Brief Summary

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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

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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.

Conditions

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Coronary Artery Disease

Keywords

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percutaneous coronary intervention artificial intelligence machine learning intravascular ultrasound coronary angiography

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Interventions

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percutaneous coronary intervention

IVUS-guided stent implantation

Intervention Type PROCEDURE

Eligibility Criteria

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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

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


* 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
Minimum Eligible Age

19 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Boston Scientific Corporation

INDUSTRY

Sponsor Role collaborator

Asan Medical Center

OTHER

Sponsor Role lead

Responsible Party

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Seung-Whan Lee, M.D., Ph.D.

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Seung-Whan Lee, MD

Role: PRINCIPAL_INVESTIGATOR

Asan Medical Center

Locations

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Soon Chun Hyang University Hospital Bucheon

Bucheon-si, , South Korea

Site Status RECRUITING

Gosin University Gospel Hospital

Busan, , South Korea

Site Status RECRUITING

Inje University Pusan Paik Hospital

Busan, , South Korea

Site Status RECRUITING

Gyeongsang National University Changwon Hospital

Changwon, , South Korea

Site Status RECRUITING

Kangwon National University Hospital

Chuncheon, , South Korea

Site Status RECRUITING

Keimyung University Dongsan Medical Center

Daegu, , South Korea

Site Status RECRUITING

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

Daejeon, , South Korea

Site Status RECRUITING

Gangneung Asan Hospital

Gangneung, , South Korea

Site Status RECRUITING

Jesushospital

Jeonju, , South Korea

Site Status RECRUITING

Chungnam National University Sejong Hospital

Sejong, , South Korea

Site Status RECRUITING

Seung-Whan Lee

Seoul, , South Korea

Site Status RECRUITING

Kangbuk Samsung Medical Center

Seoul, , South Korea

Site Status NOT_YET_RECRUITING

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

Seoul, , South Korea

Site Status RECRUITING

Veterans Hospital Service Medical Center

Seoul, , South Korea

Site Status RECRUITING

The Catholic University of Korea ST.VINCENT'S Hospital

Suwon, , South Korea

Site Status RECRUITING

Ulsan University Hospital

Ulsan, , South Korea

Site Status RECRUITING

Countries

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South Korea

Central Contacts

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Seung-Whan Lee, MD

Role: CONTACT

Phone: 82-10-7398-9897

Email: [email protected]

Facility Contacts

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Yoon-Haeng Cho, MD

Role: primary

Jeong-Ho Heo, MD

Role: primary

Tae-Hyun Yang, MD

Role: primary

Jae-Seok Bae, MD

Role: primary

Bong-Ki Lee, MD

Role: primary

Cheol-Hyeon Lee, MD

Role: primary

Kyu-Sup Lee, MD

Role: primary

Han-Bit Park, MD

Role: primary

Jong-Pil Park, MD

Role: primary

Jae-Hwan Lee, MD

Role: primary

Seung-Whan Lee, MD

Role: primary

Jong-Young Lee, MD

Role: primary

Jung-hoon Lee, MD

Role: primary

Chang-Hoon Lee, MD

Role: primary

Sung-Ho Her, MD

Role: primary

Gyung-Min Park, MD

Role: primary

Other Identifiers

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2020-0226

Identifier Type: -

Identifier Source: org_study_id