Division of Cardiology, Wayne State University, Detroit Medical Center, Detroit, MI, USA.
Department of Internal Medicine, Wayne State University, Detroit Medical Center, Detroit, MI, USA.
Best Pract Res Clin Endocrinol Metab. 2014 Jun;28(3):295-307. doi: 10.1016/j.beem.2014.01.004. Epub 2014 Jan 18.
Cardiovascular disease (CVD) remains the leading cause of mortality both in the United States and worldwide. Traditional risk factors are essential to CVD risk prediction and explain a significant portion of the between-population and between-individual variance in CVD. Nonetheless, due to the large size of the group, a substantial portion of cardiovascular events occur in individuals predicted to be at low risk based on traditional risk factor models such as the Framingham risk score. The problem is that by disregarding this low risk group, a significant proportion of events are ignored and deemed 'unpreventable'. As such, it is imperative to find new ways to improve CVD risk prediction and thereby apply preventive measures to persons more likely to develop 'preventable' disease. Focus has consequently shifted towards identification of novel markers to improve cardiovascular risk prediction. We review the role of various risk stratification models, and assess the incorporation of imaging markers to guide treatment for lipids in prevention of CVD.
心血管疾病（CVD）仍然是美国和全球范围内导致死亡的主要原因。传统危险因素对于 CVD 风险预测至关重要，并解释了 CVD 在人群之间和个体之间差异的很大一部分。尽管如此，由于人群规模庞大，根据Framingham 风险评分等传统风险因素模型预测为低风险的个体中仍有相当一部分发生心血管事件。问题是，由于忽视了这个低风险群体，很大一部分事件被忽视，并被认为是“不可预防的”。因此，必须寻找新的方法来改善 CVD 风险预测，从而对更有可能患上“可预防”疾病的人采取预防措施。因此，人们的注意力转向了确定新的标志物以改善心血管风险预测。我们回顾了各种风险分层模型的作用，并评估了影像学标志物的纳入在预防 CVD 中的脂质治疗指导中的作用。
