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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT06301009
Other study ID # AI-CAC-PVS
Secondary ID
Status Not yet recruiting
Phase N/A
First received
Last updated
Start date April 1, 2024
Est. completion date October 1, 2025

Study information

Verified date March 2024
Source Azienda Ospedaliera Città della Salute e della Scienza di Torino
Contact Fabrizio D'Ascenzo, MD
Phone +390116335575
Email fabrizio.dascenzo@gmail.com
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial 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.


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.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 500
Est. completion date October 1, 2025
Est. primary completion date October 1, 2025
Accepts healthy volunteers No
Gender All
Age group 40 Years to 75 Years
Eligibility 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)

Study Design


Intervention

Diagnostic Test:
AI-CAC score
Deep-learning based prediction of the coronary artery calcium score with a plain chest x-ray

Locations

Country Name City State
n/a

Sponsors (2)

Lead Sponsor Collaborator
Azienda Ospedaliera Città della Salute e della Scienza di Torino Compagnia di San Paolo

Outcome

Type Measure Description Time frame Safety issue
Primary Diagnostic accuracy of the AI-CAC score to identify the presence of subclinical atherosclerosis on chest x-ray Diagnostic accuracy of the AI-CAC score to identify the presence of subclinical atherosclerosis (i.e. AI-CAC >0) on chest x-ray as compared to CAC measured on a non-contrast ECG-gated CT scan (i.e. CAC >0).
The area under the curve (AUC) method will be used to evaluate the primary outcome.
Through study completion (anticipated average follow-up of 1 year).
Secondary Percentage of individuals with a therapeutic management change by the attending physician based on the CAC score, with concordant AI-CAC. Potential impact on the implementation of primary prevention strategies: i.e. percentage of individuals with a therapeutic management change by the attending physician (increase or decrease in lipid-lowering therapy, initiation or discontinuation of antiplatelet therapy, behavioral measures) based on the CAC score, with concordant AI-CAC. Through study completion (anticipated average follow-up of 1 year).
Secondary 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. Predictive ability of the AI-CAC score for the incidence of adverse cardiovascular events (myocardial infarction, stroke, cardiovascular death, or coronary revascularization) at the last available follow-up. Through study completion (anticipated average follow-up of 1 year).
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