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

March 3, 2024 updated by: Fabrizio D'Ascenzo, Azienda Ospedaliera Città della Salute e della Scienza di Torino

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Estimated)

500

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-CAC arm
All patients included in the study and undergoing AI-CAC calculation on a chest x-ray
Diagnostic test: AI-CAC score
Deep-learning based prediction of the coronary artery calcium score with a plain chest x-ray

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic accuracy of the AI-CAC score to identify the presence of subclinical atherosclerosis on chest x-ray
Time Frame: Through study completion (anticipated average follow-up of 1 year).

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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Percentage of individuals with a therapeutic management change by the attending physician based on the CAC score, with concordant AI-CAC.
Time Frame: Through study completion (anticipated average follow-up of 1 year).
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).
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.
Time Frame: Through study completion (anticipated average follow-up of 1 year).
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).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Azienda Ospedaliera Città della Salute e della Scienza di Torino

Collaborators

Compagnia di San Paolo

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

April 1, 2024

Primary Completion (Estimated)

October 1, 2025

Study Completion (Estimated)

October 1, 2025

Study Registration Dates

First Submitted

February 26, 2024

First Submitted That Met QC Criteria

March 3, 2024

First Posted (Actual)

March 8, 2024

Study Record Updates

Last Update Posted (Actual)

March 8, 2024

Last Update Submitted That Met QC Criteria

March 3, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI-CAC-PVS

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

Publication in peer-reviewed cardiovascular journal

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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