Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment

Cardiovascular disease (CVD) risk prediction models are used to identify high-risk individuals and guide statin-initiation. However, these models are usually derived from individuals who may initiate statins during follow-up. We present a simple approach to address statin-initiation to predict “statin-naïve” CVD risk. We analyzed primary care data (2004-2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (40-85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin-initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., numbers-needed-to-screen to prevent one case) against models ignoring statin-initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for versus ignoring statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in numbers-needed-to-screen to prevent one case. In conclusion, incorporating statin effects from trial results into risk prediction models enables statin-naïve CVD risk estimation, provides moderate gains in predictive ability, but had a limited impact on treatment decision-making under current guidelines in this population.The work was supported by an Alan Turing Institute/British Heart Foundation (BHF) grant. The Cardiovascular Epidemiology Unit is underpinned by program grants from the BHF and UK National Institute for Health Research Cambridge Biomedical Research Centre. ZX is funded by Chinese Scholarship Council. MA, LP and SK is funded by a British Heart Foundation Programme Grant (RG/18/13/33946). DS is funded by the Medical Research Council (MRC), School of Clinical Medicine at University of Cambridge, a BHF-Turing Cardiovascular Data Science Award and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]. JB was funded by the MRC. MS was funded by the MRC, the BHF and the National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics (NIHR BTRU-2014-10024). AW is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart)