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

NCT06178900 | Completed

• 1 other identifier: AI-Gatekeeper Pro
• Study Type: interventional
• Target: 450
• Locations: 1 country
• Sites: 5

Brief 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).

Conditions

Coronary Artery Disease; Diagnosis

Keywords

Coronary Artery Disease; Artificial Intelligence; Gatekeeper

Outcome Measures

Primary Outcomes (2)

• MACE (major adverse cardiovascular events)

  All-cause death, non-fatal MI, stroke, admission due to acute coronary syndrome

  24 weeks

• Unnecessary utilization of advanced cardiac imaging

  Defined as either (1) confirmation of non-significant coronary artery disease (stenosis ≤50%) by advanced cardiac imaging (CCTA or ICA) or (2) incorrect prediction of significant CAD by the AI-Gatekeeper software.

  24 weeks

Secondary Outcomes (4)

• Comparison of total healthcare costs

  24 weeks

• Proportion of subjects classified as positive by the AI-Gatekeeper model analysis who are diagnosed with coronary artery stenosis (≥50%)

  24 weeks

• Proportion of subjects identified as negative by the AI-Gatekeeper model who are confirmed to have non-significant stenosis (<50%)

  24 weeks

• Comparison of changes in angina symptom score

  24 weeks

Study Arms (2)

Assisted by the AI-Gatekeeper software group

EXPERIMENTAL

After a baseline examination (chest X-ray, electrocardiogram, echocardiogram, clinical risk factors and blood test), the AI-Gatekeeper software will be used to guide clinical care.

Diagnostic Test: Assisted by the AI-Gatekeeper software group

Usual care group

NO INTERVENTION

The usual care group will be managed based on established guidelines.

Interventions

Assisted by the AI-Gatekeeper software group DIAGNOSTIC_TEST

The group will be received a AI-Gatekeeper software report on the probability of having coronary artery stenosis (≥50%) based on the routine test.

Assisted by the AI-Gatekeeper software group

Eligibility Criteria

• Age: 19 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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
• Low to Intermediate risk of pretest probabilities of obstructive CAD
• Voluntarily agreed to participate in this clinical trial and signed the written consent form

You may not qualify if:

• 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

Sponsors & Collaborators

• INFINITT Healthcare: lead
• Korea Medical Device Development Fund: collaborator

Study Sites (5)

Soonchunhyang University Bucheon Hospital

Bucheon-si, Gyeonggi-do, 16995, South Korea

Location

Seoul National University Bundang Hospital

Seongnam-si, Gyeonggi-do, 16995, South Korea

Location

Yongin Severance Hospitall, Yonsei University College of Medicine

Yongin, Gyeonggi-do, 16995, South Korea

Location

Catholic Kwandong University International St. Mary's Hospital

Incheon, South Korea

Location

Hanyang University Seoul Hospital

Seoul, South Korea

Location

Related Publications (7)

• 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.

  PMID: 34756653 BACKGROUND

• 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.

  PMID: 22692650 BACKGROUND

• 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.

  PMID: 25205628 BACKGROUND

• 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.

  PMID: 34235441 BACKGROUND

• 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.

  PMID: 32200712 BACKGROUND

• 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.

  PMID: 21477786 BACKGROUND

• 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.

  PMID: 35928935 BACKGROUND

MeSH Terms

Conditions

Coronary Artery Disease; Disease

Condition Hierarchy (Ancestors)

Coronary Disease; Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• In Hyun Jung, MD, PhD

  Yongin Severance Hospital, Yonsei University College of Medicine

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 14, 2023
• First Posted: December 21, 2023
• Study Start: March 1, 2024
• Primary Completion: September 6, 2024
• Study Completion: February 28, 2025
• Last Updated: March 24, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will not share

Locations

KR (5)