General Practice and Primary Care Research Unit, University of Cambridge, UK.
Br J Clin Pharmacol. 2012 Sep;74(3):396-410. doi: 10.1111/j.1365-2125.2012.04219.x.
Cardiovascular disease is a major, growing, worldwide problem. It is important that individuals at risk of developing cardiovascular disease can be effectively identified and appropriately stratified according to risk. This review examines what we understand by the term risk, traditional and novel risk factors, clinical scoring systems, and the use of risk for informing prescribing decisions. Many different cardiovascular risk factors have been identified. Established, traditional factors such as ageing are powerful predictors of adverse outcome, and in the case of hypertension and dyslipidaemia are the major targets for therapeutic intervention. Numerous novel biomarkers have also been described, such as inflammatory and genetic markers. These have yet to be shown to be of value in improving risk prediction, but may represent potential therapeutic targets and facilitate more targeted use of existing therapies. Risk factors have been incorporated into several cardiovascular disease prediction algorithms, such as the Framingham equation, SCORE and QRISK. These have relatively poor predictive power, and uncertainties remain with regards to aspects such as choice of equation, different risk thresholds and the roles of relative risk, lifetime risk and reversible factors in identifying and treating at-risk individuals. Nonetheless, such scores provide objective and transparent means of quantifying risk and their integration into therapeutic guidelines enables equitable and cost-effective distribution of health service resources and improves the consistency and quality of clinical decision making.
心血管疾病是一个主要的、不断增长的全球性问题。重要的是,有发展心血管疾病风险的个体可以根据风险进行有效识别和适当分层。这篇综述探讨了我们对风险一词的理解、传统和新型风险因素、临床评分系统,以及风险在告知处方决策中的应用。已经确定了许多不同的心血管风险因素。已确立的传统因素,如年龄,是不良结局的有力预测因素,在高血压和血脂异常的情况下,是治疗干预的主要目标。还描述了许多新型生物标志物,如炎症和遗传标志物。这些尚未被证明在改善风险预测方面具有价值,但可能代表潜在的治疗靶点,并促进对现有治疗方法的更有针对性的使用。风险因素已被纳入几种心血管疾病预测算法中,如 Framingham 方程、SCORE 和 QRISK。这些算法的预测能力相对较差,在选择方程、不同的风险阈值以及相对风险、终生风险和可逆因素在识别和治疗高危个体中的作用等方面仍存在不确定性。尽管如此,这些评分提供了量化风险的客观和透明手段,其纳入治疗指南可使卫生服务资源的公平和有效分配,并提高临床决策的一致性和质量。