Ignite Creation Date:
2024-05-06 @ 8:14 PM
Last Modification Date:
2024-10-26 @ 3:23 PM
Study NCT ID:
NCT06301009
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-03-08
First Post:
2024-02-26
Brief Title:
The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray
Sponsor:
Azienda Ospedaliera Città della Salute e della Scienza di Torino
Organization:
Azienda Ospedaliera Città della Salute e della Scienza di Torino
Study Overview
Official Title:
The AI-CAC Model for Subclinical Atherosclerosis Detection on Chest X-ray Prospective Validation Study AI-CAC-PVS
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AI-CAC-PVS
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
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 ie 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
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 ie 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None