Cardiovascular DiseasesAI-CAC-PVSNCT06301009
The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray
A study testing AI-CAC score in people with Cardiovascular Diseases and Atherosclerosis, run by A.O.U. Città della Salute e della Scienza.
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… Read more about this study
- Who is it for?
- Ages 40 to 75 years
- Conditions the study is for
- Cardiovascular Diseases, Atherosclerosis, Coronary Artery Calcification
- What is being tested?
- AI-CAC score
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2024to 1 Oct 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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)
Trial locations and contacts
No sites are listed yet.
About this study
Official title: 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.
Read the 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.
- Being tested
- AI-CAC score
- Keywords
- coronary artery calcium, chest x-ray, atherosclerotic cardiovascular disease, primary prevention, risk prediction, artificial intelligence
- Medical subject headings
- Cardiovascular Diseases, Atherosclerosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-CAC scoreDiagnostic test
Deep-learning based prediction of the coronary artery calcium score with a plain chest x-ray
| Study group | Role | What they receive |
|---|---|---|
| AI-CAC arm | Experimental | All patients included in the study and undergoing AI-CAC calculation on a chest x-ray Receives: Diagnostic Test: AI-CAC score |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
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.
Measured: 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.
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.
Measured: Through study completion (anticipated average follow-up of 1 year).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Mar 2024
- Study start1 Apr 2024 (estimated)
- Primary completion1 Oct 2025 (estimated)
- Study completion1 Oct 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiovascular DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- A.O.U. Città della Salute e della Scienza
- Sponsor type
- University, hospital or other
- Collaborators
- Compagnia di San Paolo
- Responsible party
- Principal investigator, Fabrizio D'Ascenzo, A.O.U. Città della Salute e della Scienza
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06301009
- Sponsor's protocol ID
- AI-CAC-PVS
- Acronym
- AI-CAC-PVS
- Status last verified
- Mar 2024
- First submitted
- 26 Feb 2024
- Last updated
- 8 Mar 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Publication in peer-reviewed cardiovascular journal
Study at a glance
From the registry record, last updated by the sponsor on 8 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray: Prospective Validation Study (AI-CAC-PVS)
- ClinicalTrials.gov ID
- NCT06301009
- Acronym / sponsor ID
- AI-CAC-PVS / AI-CAC-PVS
- Keywords
- coronary artery calcium, chest x-ray, atherosclerotic cardiovascular disease, primary prevention, risk prediction, artificial intelligence
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- A.O.U. Città della Salute e della ScienzaUniversity, hospital or other
- Collaborators
- Compagnia di San Paolo
- Study start
- 1 Apr 2024 (estimated)
- Primary completion
- 1 Oct 2025 (estimated)
- Study completion
- 1 Oct 2025 (estimated)
- First posted
- 8 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Prevention
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-CAC arm | Experimental | Diagnostic Test: AI-CAC scoreAll patients included in the study and undergoing AI-CAC calculation on a chest x-ray |
Who may qualify
- Ages
- 40 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (3 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-CAC scoreDeep-learning based prediction of the coronary artery calcium score with a plain chest x-ray | Diagnostic test | AI-CAC arm |
Primary outcome
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.
2 secondary outcomes
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).
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).
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Mar 2024.
- Individual participant dataSharing planned: Yes. Publication in peer-reviewed cardiovascular journal
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
A study testing AI-CAC score in people with Cardiovascular Diseases and Atherosclerosis, run by A.O.U. Città della Salute e della Scienza.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 8 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Fabrizio D'Ascenzo, MD (+390116335575, fabrizio.dascenzo@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Mar 2024.