Evaluating AI-Gatekeeper Software in Coronary Artery Stenosis Screening: A Multicenter RCT

Coronary Artery Disease Clinical Trial

— AIGatekeeper  

Official title:

A Prospective, Multicenter, Open-label, Randomized, Comparative Clinical Trial to Verify the Effectiveness, Safety, and Cost-effectiveness of AI-Gatekeeper, a Multimodal AI Software, in Assisting the Screening of Coronary Artery Stenosis

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06178900
• Other study ID #: AI-Gatekeeper
• Status: Recruiting
• Phase: N/A
• Start date: March 1, 2024
• Est. completion date: December 1, 2025

Study information

• Verified date: April 2024
• Source: INFINITT Healthcare
• Contact: SungA Bae, MD, PhD
• Phone: 82-10-2327-3578
• Email: cardiobsa[@]yuhs.ac
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of AI-Gatekeeper software to assist clinicians in the diagnosis of coronary artery disease by predicting coronary artery stenosis (≥50%) from a multimodal AI technology that integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease (coronary stenosis).

Description:

Coronary artery disease (CAD) is a leading cause of global mortality, accounting for over 50% of heart disease-related deaths. Initial evaluations for CAD typically involve chest X-rays, electrocardiograms (ECG), risk factor assessments, and basic blood tests. However, these primary tests can't conclusively diagnose CAD. When CAD is suspected, coronary CTA (CCTA) or invasive coronary angiography (ICA) is performed, determining the need for procedures like stenting or revascularization. Interestingly, over 50% of patients undergoing CCTA or ICA don't require treatment, as CAD is either absent or not severe enough. This leads to unnecessary procedures and significant healthcare costs. For instance, in the U.S., the cost of unnecessary ICAs reaches billions annually, with similar trends in South Korea. AI-Gatekeeper software assists clinicians in diagnosing coronary artery disease by predicting coronary artery stenosis (≥50%) using multimodal AI technology. It integrates clinical risk factors and baseline blood tests, including chest X-ray, electrocardiogram, and echocardiogram, in patients with suspected coronary artery disease The purpose of this study is to determine the efficacy, safety, and cost-effectiveness of the AI-Gatekeeper software in a prospective, multicenter, randomized control trial.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 450
• Est. completion date: December 1, 2025
• Est. primary completion date: March 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 19 Years to 75 Years

Eligibility

Inclusion Criteria:

• A patient with symptoms such as chest pain suggestive of coronary artery disease, who underwent routine evaluations including blood tests, electrocardiogram, chest X-ray, and echocardiography
• Intermediate to high risk of pretest probabilities of obstructive CAD (>15%)
• Voluntarily agreed to participate in this clinical trial and signed the written consent form

Exclusion Criteria:

• Acute chest pain (in patients who have not been ruled out for ACS)
• Previously diagnosed and treated coronary artery disease (myocardial infarction, PCI, CABG)
• Patients with a life expectancy of less than 2 years due to conditions other than heart disease
• Those who have not consented to the protocol
• Participated in a drug or medical device clinical trial within the last 3 months
• Pregnant or lactating women
• Allergic to iodine preparations
• Serum creatine level greater than 1.5 mg/dL or eGFR less than 30 mL/min
• Baseline irregular and uncontrolled heart rhythm
• Heart rate greater than 100 beats/minute
• Systolic blood pressure of 90 mm Hg or less
• Contraindications to beta blockers or nitroglycerin
• Patients with complex congenital heart disease
• Body mass index greater than or equal to 35

Related Conditions & MeSH terms

• Coronary Artery Disease
• Coronary Disease
• Coronary Stenosis
• Diagnosis
• Myocardial Ischemia

Intervention

Diagnostic Test:
• Assisted by the AI-Gatekeeper software group
The group will be received a AI-Gatekeeper software report on the probability of having coronary artery stenosis (=50%) based on the routine test.

Locations

Country	Name	City	State
Korea, Republic of	Soonchunhyang University Bucheon Hospital	Bucheon	Gyeonggi-do
Korea, Republic of	Catholic Kwandong University International St. Mary's Hospital	Incheon
Korea, Republic of	Seoul National University Bundang Hospital	Seongnam	Gyeonggi-do
Korea, Republic of	Hanyang University Seoul Hospital	Seoul
Korea, Republic of	Yongin Severance Hospitall, Yonsei University College of Medicine	Yongin	Gyeonggi-do

Sponsors (2)

Lead Sponsor	Collaborator
INFINITT Healthcare	Korea Medical Device Development Fund

Country where clinical trial is conducted

Korea, Republic of, 

References & Publications (7)

Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR, de Feyter PJ, Krestin GP, Alkadhi H, Leschka S, Desbiolles L, Meijs MF, Cramer MJ, Knuuti J, Kajander S, Bogaert J, Goetschalckx K, Cademartiri F, Maffei E, Martini C, Seitun S, Aldrovandi A, Wildermuth S, Stinn B, Fornaro J, Feuchtner G, De Zordo T, Auer T, Plank F, Friedrich G, Pugliese F, Petersen SE, Davies LC, Schoepf UJ, Rowe GW, van Mieghem CA, van Driessche L, Sinitsyn V, Gopalan D, Nikolaou K, Bamberg F, Cury RC, Battle J, Maurovich-Horvat P, Bartykowszki A, Merkely B, Becker D, Hadamitzky M, Hausleiter J, Dewey M, Zimmermann E, Laule M. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012 Jun 12;344:e3485. doi: 10.1136/bmj.e3485. — View Citation

Kamel PI, Yi PH, Sair HI, Lin CT. Prediction of Coronary Artery Calcium and Cardiovascular Risk on Chest Radiographs Using Deep Learning. Radiol Cardiothorac Imaging. 2021 Jun 17;3(3):e200486. doi: 10.1148/ryct.2021200486. eCollection 2021 Jun. — View Citation

Kim J, Lee SY, Cha BH, Lee W, Ryu J, Chung YH, Kim D, Lim SH, Kang TS, Park BE, Lee MY, Cho S. Machine learning models of clinically relevant biomarkers for the prediction of stable obstructive coronary artery disease. Front Cardiovasc Med. 2022 Jul 19;9:933803. doi: 10.3389/fcvm.2022.933803. eCollection 2022. — View Citation

Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020 Apr 7;9(7):e014717. doi: 10.1161/JAHA.119.014717. Epub 2020 Mar 21. — View Citation

Min JK, Dunning A, Lin FY, Achenbach S, Al-Mallah MH, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Cheng V, Chinnaiyan KM, Chow B, Delago A, Hadamitzky M, Hausleiter J, Karlsberg RP, Kaufmann P, Maffei E, Nasir K, Pencina MJ, Raff GL, Shaw LJ, Villines TC. Rationale and design of the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter) Registry. J Cardiovasc Comput Tomogr. 2011 Mar-Apr;5(2):84-92. doi: 10.1016/j.jcct.2011.01.007. Epub 2011 Feb 1. — View Citation

Renker M, Schoepf UJ, Wang R, Meinel FG, Rier JD, Bayer RR 2nd, Mollmann H, Hamm CW, Steinberg DH, Baumann S. Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. Am J Cardiol. 2014 Nov 1;114(9):1303-8. doi: 10.1016/j.amjcard.2014.07.064. Epub 2014 Aug 12. — View Citation

Writing Committee Members; Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, Blankstein R, Boyd J, Bullock-Palmer RP, Conejo T, Diercks DB, Gentile F, Greenwood JP, Hess EP, Hollenberg SM, Jaber WA, Jneid H, Joglar JA, Morrow DA, O'Connor RE, Ross MA, Shaw LJ. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285. doi: 10.1016/j.jacc.2021.07.053. Epub 2021 Oct 28. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Composite of MACE and diagnosis without significant coronary artery stenosis (<50%) on coronary artery disease testing	All-cause death, non-fatal MI, unplanned revascularization, diagnosis without significant coronary artery stenosis (<50%) on coronary artery disease testing (coronary CTA or invasive coronary angiography)	24 weeks
Secondary	MACE (major adverse cardiovascular events)	All-cause death, non-fatal MI, unplanned revascularization	24 weeks
Secondary	Proportion of subjects classified as positive by the AI-Gatekeeper model analysis who are diagnosed with coronary artery stenosis (=50%)	This measure assesses the accuracy of the AI-Gatekeeper model in identifying patients with coronary artery stenosis of 50% or greater among those it classifies as positive.	24 weeks
Secondary	Comparison of total healthcare costs	This analysis focuses on comparing the overall financial impact of different healthcare interventions or treatments. It encompasses all associated expenses, from diagnostic procedures to treatment and follow-up care, providing a comprehensive assessment of the economic burden on the healthcare system.	24 weeks
Secondary	Comparison of changes in angina symptom score	This comparison evaluates the variation in patients' angina symptoms severity and frequency as measured by the Seattle Angina Questionnaire (SAQ), a standardized tool for assessing chest pain related to heart conditions.	24 weeks