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

Study Overview
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Brief Summary
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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).

Detailed Description
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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.

Official Title
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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

Conditions
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Coronary Artery Disease, Diagnosis

Intervention / Treatment
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* Diagnostic Test: Assisted by the AI-Gatekeeper software group


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 19 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Assisted by the AI-Gatekeeper software group<br>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<br>* The group will be received a AI-Gatekeeper software report on the probability of having coronary artery stenosis (≥50%) based on the routine test.<br>|
| No Intervention: Usual care group<br>The usual care group will be managed based on established guidelines. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| MACE (major adverse cardiovascular events) | All-cause death, non-fatal MI, unplanned revascularization | 24 weeks | 
| 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 | 
| 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 | 
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Coronary Artery Disease, Artificial Intelligence, Gatekeeper