Centre for Reviews and Dissemination, University of York, York, UK (formerly of the Wolfson institute).
J Med Screen. 2012 Dec;19(4):201-5. doi: 10.1258/jms.2012.012076. Epub 2013 Jan 4.
Risk of future cardiovascular disease (CVD) events is typically estimated from risk factors such as age, sex, blood pressure and cholesterol. Many 'risk algorithms' exist to estimate CVD risk. All should have similar screening performances because of the dominant effect of age in predicting who will and will not have a CVD event, regardless of the accuracy of CVD risk estimation. Six CVD risk algorithms were compared (Framingham 1991, Framingham 2008, Reynolds risk, ASSIGN, SCORE and QRISK2), each differing in the risk factors used and in CVD outcomes.
The six algorithms were applied to a simulated sample of 500,000 people aged 40-74, based on the population of England. CVD risk was calculated for each individual using all risk algorithms, and who did and did not have a CVD event in 10 years was simulated according to those estimated risks. Screening performance was assessed by estimating the detection rate (sensitivity) and false-positive rate (1 - specificity) at a range of cut-off values of CVD risk for each algorithm. The accuracy (calibration) of risk estimation was compared across the six algorithms.
At a 20% false-positive rate the detection rates of the six algorithms ranged from 72% to 79%. The estimated risk cut-offs to achieve the same false-positive rate varied five-fold, from 4% to 21% because of the different risk factors and outcomes considered.
All six risk algorithms had similar screening performances. The accuracy (calibration) of CVD risk estimation does not materially affect screening performance. In distinguishing who will and will not develop CVD it is screening performance that matters rather than the accuracy of the risk estimation.
未来心血管疾病 (CVD) 事件的风险通常通过年龄、性别、血压和胆固醇等危险因素来估计。有许多“风险算法”用于估计 CVD 风险。由于年龄在预测谁将发生 CVD 事件以及谁不会发生 CVD 事件方面具有主导作用，因此所有这些算法都应该具有相似的筛查性能，而与 CVD 风险估计的准确性无关。比较了六种 CVD 风险算法（Framingham 1991、Framingham 2008、Reynolds 风险、ASSIGN、SCORE 和 QRISK2），它们在使用的危险因素和 CVD 结局方面有所不同。
根据英格兰的人口，将这六种算法应用于模拟的 50 万人 40-74 岁人群样本中。使用所有风险算法为每个个体计算 CVD 风险，并根据这些估计的风险模拟在 10 年内发生 CVD 事件的个体和未发生 CVD 事件的个体。通过估计每个算法的 CVD 风险的一系列截断值的检出率（灵敏度）和假阳性率（1-特异性）来评估筛查性能。比较了六种算法的风险估计准确性。
在 20%的假阳性率下，六种算法的检出率范围为 72%-79%。由于考虑了不同的危险因素和结局，实现相同假阳性率的估计风险截断值相差五倍，从 4%到 21%不等。
所有六种风险算法的筛查性能都相似。CVD 风险估计的准确性（校准）不会对筛查性能产生实质性影响。在区分谁将发生 CVD 以及谁不会发生 CVD 时，重要的是筛查性能，而不是风险估计的准确性。
