Σάββατο 28 Ιανουαρίου 2017

Addressing Knowledge Gaps in the 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk: a Review of Recent Coronary Artery Calcium Literature

Abstract

Purpose of Review

Coronary artery calcium (CAC) has been proposed as an integrator of information from traditionally measured, non-traditionally measured, and unmeasured risk factors for coronary atherosclerosis. The 2013 American College of Cardiology/American Heart Association Guideline on the Assessment of Cardiovascular Risk identified several knowledge gaps regarding CAC, including radiation risks, cost-effectiveness, and improving discrimination and reclassification of estimated risk over the Pooled Cohort Equations in the ACC/AHA Atherosclerotic Cardiovascular Disease Estimator. In this review, we focus on recent CAC literature addressing these knowledge gaps. We further highlight the potential for CAC to enrich future randomized controlled trials.

Recent Findings

The use of CAC allows for personalization of cardiovascular risk despite the presence or absence of traditional risk factors across many demographics. Avenues to reduce radiation exposure associated with CAC scanning include increasing the interval between scans for those with CAC scores of zero and estimating CAC from non-cardiac gated CT scans. While limited studies have suggested cost-effectiveness in cardiac risk assessment with the incorporation of CAC in screening algorithms, several studies have demonstrated the ability of CAC to identify non-traditional risk factors that may be used to expand cardiovascular risk personalization in other high-risk populations.

Summary

Literature from the past 2 years further supports CAC as a strong marker to personalize cardiac risk assessment. While multiple potential avenues to reduce radiation are available and cost-effectiveness analyses are encouraging, further studies are necessary to clarify patient selection for CAC scanning given the interplay between CAC and other imaging modalities in risk personalization algorithms.



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