Implications of Five Different Risk Models In Primary Prevention Guidelines

Abstract Background A lack of consensus exists across guidelines as to which risk model should be used for the primary prevention of cardiovascular disease (CVD). Our objective was to determine potential improvements in the number needed to treat (NNT) and number of events prevented (NEP) using different risk models in patients eligible for risk stratification. Methods and results A retrospective observational cohort was assembled from primary care patients in Ontario, Canada, between 1 January 2010 and 31 December 2014 and followed for up to 5 years. Risk estimation was undertaken in patients 40–75 years of age, without CVD, diabetes, or chronic kidney disease using the Framingham Risk Score (FRS), the Pooled Cohort Equations (PCEs), a recalibrated FRS (R-FRS), the Systematic Coronary Risk Evaluation 2 (SCORE2), and the low-risk region recalibrated SCORE2 (LR-SCORE2). The cohort consisted of 47 399 patients (59% women, mean age 54 years). The NNT with statins was lowest for the SCORE2 at 40, followed by the LR-SCORE2 at 41, the R-FRS at 43, the PCEs at 55, and the FRS at 65. Models that selected for individuals with a lower NNT recommended statins to fewer, but higher-risk patients. For instance, the SCORE2 recommended statins to 7.9% of patients (5-year CVD incidence 5.92%). The FRS, however, recommended statins to 34.6% of patients (5-year CVD incidence 4.01%). Accordingly, the NEP was highest for the FRS at 406 and lowest for the SCORE2 at 156. Conclusions Newer models such as the SCORE2 may improve statin allocation to higher-risk groups with a lower NNT but prevent fewer events at the population level.