Clinical trial NCT05807841: Clinical Impact of Intravascular Ultrasound-Based Artificial Intelligence Technologies (INNOVATE-PCI)
Official title: Influence of Novel Intravascular Ultrasound-Based Artificial Intelligence Technologies on Event Reduction Following Percutaneous Coronary Intervention (INNOVATE-PCI)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Coronary Artery Disease
Keywords: percutaneous coronary intervention
artificial intelligence
machine learning
intravascular ultrasound
coronary angiography
Lead sponsor: Asan Medical Center
Sponsor class: OTHER
Collaborators: name: Boston Scientific Corporation
First posted: 2023-04-11
Enrollment: 3000
Sex: ALL
Minimum age (years): 19
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Between February 2020 and June 2025, a total of 3,000 patients who performe coronary angiography (± FFR) and have at least one coronary stenosis requiring PCI (as culprit) will be enrolled from 15 centers in South Korea.

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 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.

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

Primary outcomes:
measure: culprit-related TVF
timeFrame: 2 years after stent implantation
measure: nonculprit-related TVF
timeFrame: 2 years after stent implantation
Secondary outcomes:
measure: each component of primary measures
timeFrame: 2 years after stent implantation
measure: all cause death
timeFrame: 2 years after stent implantation
measure: repeat revascularization
timeFrame: 2 years after stent implantation
measure: stroke
timeFrame: 2 years after stent implantation
measure: bleeding
timeFrame: 2 years after stent implantation
Other outcomes:
Locations:
city: Bucheon
country: Korea, Republic of
facility: Soon Chun Hyang University Hospital Bucheon
city: Busan
country: Korea, Republic of
facility: Gosin University Gospel Hospital
city: Busan
country: Korea, Republic of
facility: Inje University Pusan Paik Hospital
city: Changwon
country: Korea, Republic of
facility: Gyeongsang National University Changwon Hospital
city: Chuncheon
country: Korea, Republic of
facility: Kangwon National University Hospital
city: Daegu
country: Korea, Republic of
facility: Keimyung University Dongsan Medical Center
city: Daejeon
country: Korea, Republic of
facility: The Catholic university of korea, daejeon st. mary's hospital
city: Gangneung
country: Korea, Republic of
facility: Gangneung Asan Hospital
city: Jeonju
country: Korea, Republic of
facility: Jesushospital
city: Sejong
country: Korea, Republic of
facility: Chungnam National University Sejong Hospital
city: Seoul
country: Korea, Republic of
facility: Seung-Whan Lee
city: Seoul
country: Korea, Republic of
facility: Kangbuk Samsung Medical Center
city: Seoul
country: Korea, Republic of
facility: The Catholic university of korea, Eunpyeong st. mary's hospital
city: Seoul
country: Korea, Republic of
facility: Veterans Hospital Service Medical Center
city: Suwon
country: Korea, Republic of
facility: The Catholic University of Korea ST.VINCENT'S Hospital
city: Ulsan
country: Korea, Republic of
facility: Ulsan University Hospital
Study officials:
affiliation: Asan Medical Center
name: Seung-Whan Lee, MD
role: PRINCIPAL_INVESTIGATOR
References: