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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Coronary artery calcium clinical utilization: An update
Ibrahim Mortada1, Esosa Odigie-Okon1, Afaq Motiwala1
1Department of Cardiovascular Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555, USA.
Insights
Coronary artery calcification (CAC) scoring refines cardiovascular risk assessment and guides treatment decisions. Automated CAC assessment in non-gated CT scans expands its use for broader population risk stratification and improved patient outcomes.
Area of Science:
- Cardiology
- Radiology
- Preventive Medicine
Background:
- Coronary artery disease (CAD) is a major global health concern.
- Coronary artery calcification (CAC) quantifies atherosclerotic burden and aids risk stratification.
- CAC scoring is integrated into primary prevention guidelines.
Purpose of the Study:
- To review current tools, evidence, and guidelines for CAC utilization.
- To highlight CAC's role in risk stratification and optimizing lipid-lowering therapy.
- To explore the expanding application of CAC assessment through artificial intelligence in non-gated CT scans.
Main Methods:
- Review of existing literature on CAC scoring methods (Agatston, non-gated CT).
- Analysis of data supporting CAC as a risk refinement tool.
- Discussion of artificial intelligence and automated CAC assessment in non-gated studies.
Main Results:
- CAC quantification provides objective subclinical coronary atherosclerosis assessment.
- CAC scoring refines cardiovascular risk stratification beyond traditional risk factors.
- Non-gated CT scans and AI enhance CAC assessment accessibility and application.
Conclusions:
- CAC scoring is a valuable tool for refining cardiovascular risk assessment.
- CAC aids in guiding lipid-lowering therapy and risk factor modification.
- Automated CAC assessment in non-gated CT scans broadens its clinical utility for improved patient outcomes.
Abstract:
Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide. Coronary artery calcification (CAC) is a well-established marker of atherosclerotic burden, and its quantification provides an objective measure of subclinical coronary atherosclerosis that can refine cardiovascular risk stratification and guide decisions regarding risk factor modification and lipid-lowering therapies. There is extensive data supporting the role of CAC scoring as an adjunct risk refinement tool, and it has been incorporated into multiple primary prevention guidelines. In addition to the Agatston method, CAC can also be quantified using non-gated computed tomography (CT) scans which are simple and widely available from non-cardiac screening strategies, including those obtained routinely for lung cancer screening. The integration of artificial intelligence and automated CAC assessment in non-gated studies is further expanding its application for risk stratification to a much larger population. This review summarizes the current tools, evidence and guidelines supporting the use of CAC to help risk stratify, optimize lipid lowering therapy, and potentially improve patient outcomes.
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