The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray

Cardiovascular Diseases Clinical Trial

— AI-CAC-PVS  

Official title:

The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray: Prospective Validation Study (AI-CAC-PVS)

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06301009
• Other study ID #: AI-CAC-PVS
• Status: Not yet recruiting
• Phase: N/A
• 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
• 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)

Related Conditions & MeSH terms

• Atherosclerosis
• Cardiovascular Diseases
• Coronary Artery Calcification

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).