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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
What should we do about Coronary Calcification on Thoracic CT?
Oliver Llewellyn1, Michelle C Williams2
1Department of Radiology, Royal Infirmary of Edinburgh, United Kingdom of Great Britain and Northern Ireland.
Coronary artery calcification found on non-cardiac CT scans indicates higher cardiac risk. Reporting this incidental finding is recommended by guidelines and can prompt crucial cardiovascular risk assessments.
Area of Science:
- Radiology
- Cardiology
- Preventive Medicine
Background:
- Coronary artery calcification (CAC) is often incidentally detected on thoracic computed tomography (CT) scans ordered for non-cardiac reasons.
- On electrocardiogram-gated cardiac CT, CAC is a recognized indicator of coronary artery disease (CAD) and predicts future cardiac events.
Purpose of the Study:
- This review synthesizes current evidence and guidelines on reporting CAC identified in non-electrocardiogram-gated thoracic CT scans.
- To evaluate the clinical significance of incidental CAC findings in patients undergoing thoracic CT for non-cardiac indications.
Main Methods:
- Literature review of current evidence and guidelines.
- Analysis of the association between CAC on thoracic CT and cardiovascular outcomes.
- Comparison of CAC assessment on non-gated versus gated CT.
Main Results:
- CAC on routine thoracic CT is linked to increased risk of myocardial infarction and mortality.
- CAC can be reliably assessed on non-gated thoracic CT, comparable to gated CT.
- Guidelines advocate for reporting CAC found on thoracic CT, though radiologist consensus varies.
- Incidental CAC detection can identify individuals with previously unrecognized CAD, potentially leading to risk factor assessment and preventive treatment.
Conclusions:
- Reporting incidental CAC on thoracic CT is supported by guidelines and can facilitate early cardiovascular risk stratification.
- Future research should explore the impact of management changes based on thoracic CT-detected CAC and investigate automated CAC assessment using machine learning.
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