Risk Estimation Versus Screening Performance: A Comparison of Six Risk Algorithms for Cardiovascular Disease

Abstract

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.