Dartmouth Medical School, 1 Rope Ferry Road, Hanover, NH 03755, USA.
BMC Med. 2010 May 25;8:29. doi: 10.1186/1741-7015-8-29.
Cardiovascular disease is the most common cause of death and risk prediction formulae such as the Framingham Risk Score have been developed to easily identify patients at high risk that may require therapeutic interventions.
Using cardiovascular risk formulae at a population level to estimate and compare average cardiovascular risk among groups has been recently proposed as a way to facilitate surveillance of net cardiovascular risk and target public health interventions. Risk prediction formulas may help to compare interventions that cause effects of different magnitudes and directions in several cardiovascular risk factors, because these formulas assess the net change in risk using easily obtainable clinical variables. Because of conflicting data estimates of the incidence and prevalence of cardiovascular disease, risk prediction formulae may be a useful tool to estimate such risk at a population level.
Although risk prediction formulae were intended on guiding clinicians to individualized therapy, they also can be used to ascertain trends at a population-level, particularly in situations where changes in different cardiovascular risk factors over time have different magnitudes and directions. The efficacy of interventions that are proposed to reduce cardiovascular risk impacting more than one risk factor can be well assessed by these means.
心血管疾病是最常见的死亡原因,因此开发了弗莱明汉风险评分等风险预测公式,以便于识别可能需要治疗干预的高危患者。
最近有人提出,在人群层面使用心血管风险公式来估计和比较不同群体的平均心血管风险,以此促进对净心血管风险的监测并针对公共卫生干预措施进行目标定位。风险预测公式可以帮助比较在多个心血管风险因素中引起不同程度和方向的效果的干预措施,因为这些公式使用易于获得的临床变量来评估风险的净变化。由于心血管疾病的发病率和患病率存在相互矛盾的数据估计,因此风险预测公式可能是在人群层面估计此类风险的有用工具。
尽管风险预测公式旨在指导临床医生进行个体化治疗,但它们也可用于确定人群层面的趋势,尤其是在不同心血管风险因素随时间变化的程度和方向不同的情况下。通过这些方法,可以很好地评估拟议用于降低影响一个以上风险因素的心血管风险的干预措施的效果。