Comparison of Swiss and European risk algorithms for cardiovascular prevention in Switzerland

BACKGROUND: In Switzerland, two distinct algorithms are recommended for cardiovascular prevention: (a) Arbeitsgruppe Lipide und Atherosklerose (AGLA); and (b) European Society of Cardiology (ESC). We validated and determined which algorithm better predicts incident atherosclerotic cardiovascular disease and assessed statin eligibility in Switzerland. DESIGN: A prospective population-based cohort. METHODS: We employed longitudinal data of the CoLaus study involving 6733 individuals, aged 35-75 years, with a 10-year follow-up. Using discrimination and calibration, we evaluated the predictive performance of the AGLA and ESC algorithms for the prediction of atherosclerotic cardiovascular disease. RESULTS: From the 6733 initial participants, 5529 were analysed with complete baseline and follow-up data. Mean age (SD) was 52.4 (10.6) years and 54% were women. During an average follow-up (SD) of 10.2 years (1.7), 370 (6.7%) participants developed an incident atherosclerotic cardiovascular disease. The sensitivity of AGLA and ESC algorithms to predict atherosclerotic cardiovascular disease was 51.6% (95% confidence interval (CI) 46.4-56.8) and 58.6% (53.4-63.7), respectively. Discrimination and calibration were similar between the AGLA and ESC algorithms, with area under the receiver operating characteristic curve values of 0.78 (95% CI 0.76-0.80) and 0.79 (0.76-0.81), and Brier scores of 0.059 and 0.041, respectively. Among 370 individuals developing incident atherosclerotic cardiovascular disease, only 278 (75%) were eligible for statin therapy at baseline, including 210 (57%) according to both algorithms, 4 (1%) to AGLA only and 64 (17%) to ESC only. CONCLUSION: AGLA and ESC algorithms presented similar accuracy to predict atherosclerotic cardiovascular disease in Switzerland. A quarter of adults developing atherosclerotic cardiovascular disease were not identified by preventive algorithms to be eligible for statin therapy.