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

NCT06301009 | Not Yet Recruiting

• 1 other identifier: AI-CAC-PVS
• Study Type: interventional
• Target: 500
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Cardiovascular Diseases; Atherosclerosis; Coronary Artery Calcification

Keywords

coronary artery calcium; chest x-ray; atherosclerotic cardiovascular disease; primary prevention; risk prediction; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (2)

• Percentage of individuals with a therapeutic management change by the attending physician based on the CAC score, with concordant AI-CAC.

  Through study completion (anticipated average follow-up of 1 year).

• 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.

  Through study completion (anticipated average follow-up of 1 year).

Study Arms (1)

AI-CAC arm

EXPERIMENTAL

All patients included in the study and undergoing AI-CAC calculation on a chest x-ray

Diagnostic Test: AI-CAC score

Interventions

AI-CAC score DIAGNOSTIC_TEST

Deep-learning based prediction of the coronary artery calcium score with a plain chest x-ray

AI-CAC arm

Eligibility Criteria

• Age: 40 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• A.O.U. Città della Salute e della Scienza: lead
• Compagnia di San Paolo: collaborator

MeSH Terms

Conditions

Cardiovascular Diseases; Atherosclerosis

Condition Hierarchy (Ancestors)

Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases

Central Study Contacts

Fabrizio D'Ascenzo, MD

CONTACT

+390116335575; fabrizio.dascenzo@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD, PhD

Study Record Dates

• First Submitted: February 26, 2024
• First Posted: March 8, 2024
• Study Start: April 1, 2024
• Primary Completion: October 1, 2025
• Study Completion: October 1, 2025
• Last Updated: March 8, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will share