An interventional study of AI-CAC score in Cardiovascular Diseases, Atherosclerosis and Coronary Artery Calcification, sponsored by A.O.U. Città della Salute e della Scienza. Not yet recruiting. Open to participants aged 40 Years to 75 Years. Per ClinicalTrials.gov, last updated 2024-03-08.
Sponsored by A.O.U. Città della Salute e della Scienza · Not applicable, Interventional, and Prevention
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.
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.
4,904 studies on the registry are indexed under Cardiovascular Diseases; 920 are open to participants now.
This study's planned enrollment of 500 is above the median of 100 across 2,738 interventional studies indexed under Cardiovascular Diseases.
Browse Cardiovascular Diseases studies →A.O.U. Città della Salute e della Scienza is the lead sponsor of 63 studies on the registry; 13 are open to participants now.
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Exclusion Criteria:
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
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.
Time frame: Through study completion (anticipated average follow-up of 1 year).
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.
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.
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.
Time frame: Through study completion (anticipated average follow-up of 1 year).
No study locations are listed for this record.
Plan to share: Yes — Publication in peer-reviewed cardiovascular journal
No publications or documents are linked to this record.
This study is not yet recruiting, as verified in Mar 2024. You cannot join it, but the record below documents what was studied.
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A.O.U. Città della Salute e della Scienza