Clinical trial NCT06301009: The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray
Official title: The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray: Prospective Validation Study (AI-CAC-PVS)
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Cardiovascular Diseases
Atherosclerosis
Coronary Artery Calcification
Keywords: coronary artery calcium
chest x-ray
atherosclerotic cardiovascular disease
primary prevention
risk prediction
artificial intelligence
Lead sponsor: A.O.U. Città della Salute e della Scienza
Sponsor class: OTHER
Collaborators: name: Compagnia di San Paolo
First posted: 2024-03-08
Enrollment: 500
Sex: ALL
Minimum age (years): 40
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: PREVENTION

Brief summary: The AI-CAC model is an artificial intelligence system capable of assessing the presence of subclinical atherosclerosis on a simple chest radiograph. The present study will provide prospective validation of its diagnostic performance in a primary prevention population with a clinical indication for coronary artery calcium (CAC) testing.

Detailed description: The AI-CAC-PVS project is a prospective, multicenter, single-arm clinical study, with enrollment at 5 Radiology Units in Piedmont (Italy). Consecutive individuals without prior reported cardiovascular events referred for a non-contrast chest CT for the assessment of coronary artery calcium (CAC) score for cardiovascular risk stratification purposes will be considered for inclusion in the study. Individuals who agree to participate in the study will undergo a standard chest radiograph, as the only deviation from clinical practice. The CAC score will be calculated on chest CT scans according to international standards, and the result will be provided to the patient. Any subsequent changes in behavioral habits, lipid-lowering, antiplatelet, antihypertensive, and antidiabetic therapies prescribed by the attending physician will be collected in a dedicated dataset, along with the occurrence of cardiovascular events at the last available follow-up.

The AI-CAC model will be applied to the chest radiograph, yielding an AI-CAC value as output. The patient, radiologist, and attending physician will not be informed of the AI-CAC value until the end of the study.

The primary outcome will be the accuracy of the AI-CAC model to detect the presence of subclinical atherosclerosis on chest x-ray as compared to the CT scan (i.e. CAC \>0). The ability to predict clinical outcomes at follow-up (ASCVD, atherosclerotic cardiovascular disease events comprising myocardial infarction, ischemic stroke, coronary revascularization and cardiovascular death) will be assessed as exploratory secondary outcome.

Eligibility criteria: Inclusion Criteria:

* Consent to participate in the study
* Age between 40 and 75 years
* Clinical indication from the treating physician to undergo chest CT for CAC score evaluation

Exclusion Criteria:

* Prior cardiovascular events (myocardial infarction, coronary revascularization, transient ischemic attack, stroke, symptomatic peripheral vascular disease, arterial revascularization of peripheral districts)
* Cancer or other chronic diseases with an estimated prognosis of less than five years
* Technical contraindications to the execution of chest CT with electrocardiographic gating (highly penetrant atrial fibrillation, frequent ventricular extrasystoles)

Primary outcomes:
measure: Diagnostic accuracy of the AI-CAC score to identify the presence of subclinical atherosclerosis on chest x-ray
timeFrame: Through study completion (anticipated average follow-up of 1 year).
Secondary outcomes:
measure: Percentage of individuals with a therapeutic management change by the attending physician based on the CAC score, with concordant AI-CAC.
timeFrame: Through study completion (anticipated average follow-up of 1 year).
measure: Comparison of ASCVD events occurring in patients without (AI-CAC=0) vs. with subclinical atherosclerosis (AI-CAC >0) based on the AI-CAC score, as assessed by Kaplan Meier estimates of ASCVD events occurring until study completion.
timeFrame: Through study completion (anticipated average follow-up of 1 year).
Other outcomes:
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