Kianoush Sina, Mirbolouk Mohammadhassan, Makam Raghavendra Charan, Nasir Khurram, Blaha Michael J
Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins Hospital, Blalock building Suit 501, 600 N Wolfe Street, Baltimore, MD, 21287, USA.
Center for Healthcare Advancement and Outcomes, Baptist Health South Florida, 1500 San Remo Ave, Suite 340, Coral Gables, FL, 33139, USA.
Curr Treat Options Cardiovasc Med. 2017 Sep 25;19(11):85. doi: 10.1007/s11936-017-0582-y.
Detecting subclinical atherosclerosis with coronary artery calcium (CAC) is promising for identifying individuals at risk for cardiovascular events and appears to be a robust tool for guiding initiation of appropriate and timely primary prevention strategies. However, how do we best determine its clinical value? It is clear that traditional risk prediction models based primarily on age, gender, and risk factors are insufficient for ideal personalization of risk estimation. It is now well established from epidemiologic studies that CAC adds to traditional risk scores for a more accurate risk prediction. However, such traditional epidemiology studies have limitations in establishing "clinical value," and they must be supplemented by additional data before being translated into strong recommendations in clinical practice guidelines. Fortunately, over the last few years, the research around CAC has matured to include data supporting enhanced clinician-patient risk discussions, shared decision-making, flexible risk factor treatment goals, specific clinical decision algorithms, as well as favorable cost-effectiveness analyses. We had moved from a time when we asked "if CAC adds to the risk score" to a time when we are asking "does CAC facilitate a shared decision-making model matching risk, treatment, and patient preferences?" A new risk calculator incorporating CAC into global risk scoring, and 2017 guidelines on the use of CAC published by the Society of Cardiovascular Computed Tomography (SCCT), reflect this new approach. In this article, we review the recent transition to this more clinically relevant CAC research that may support a stronger recommendation for its use in future prevention guidelines.
用冠状动脉钙化(CAC)检测亚临床动脉粥样硬化对于识别有心血管事件风险的个体很有前景,并且似乎是指导启动适当和及时的一级预防策略的有力工具。然而,我们如何才能最好地确定其临床价值呢?很明显,主要基于年龄、性别和风险因素的传统风险预测模型不足以实现理想的风险估计个性化。从流行病学研究中现已明确,CAC可补充传统风险评分,以进行更准确的风险预测。然而,此类传统流行病学研究在确立“临床价值”方面存在局限性,在转化为临床实践指南中的有力建议之前,必须补充其他数据。幸运的是,在过去几年中,围绕CAC的研究已经成熟,包括支持加强医患风险讨论、共同决策、灵活的风险因素治疗目标、特定临床决策算法以及良好成本效益分析的数据。我们已经从询问“CAC是否能增加风险评分”的时代,进入到询问“CAC是否有助于建立一个将风险、治疗和患者偏好相匹配的共同决策模型”的时代。一个将CAC纳入全球风险评分的新风险计算器,以及心血管计算机断层扫描协会(SCCT)发布的2017年CAC使用指南,都反映了这种新方法。在本文中,我们回顾了最近向这种更具临床相关性的CAC研究的转变,这可能支持在未来预防指南中更有力地推荐使用CAC。