Cholesterol and Hypertension Treatment Improve Coronary Risk Prediction but Not Time-Dependent Covariates or Competing Risks

Abstract

Background and Aims: Cardiovascular (CV) risk functions are the recommended tool to identify high-risk individuals. However, their discrimination ability is not optimal. While the effect of biomarkers in CV risk prediction has been extensively studied, there are no data on CV risk functions including time-dependent covariates together with other variables. Our aim was to examine the effect of including time-dependent covariates, competing risks, and treatments in coronary risk prediction. Methods: Participants from the REGICOR population cohorts (North-Eastern Spain) aged 35–74 years without previous history of cardiovascular disease were included (n = 8470). Coronary and stroke events and mortality due to other CV causes or to cancer were recorded during follow-up (median = 12.6 years). A multi-state Markov model was constructed to include competing risks and time-dependent classical risk factors and treatments (2 measurements). This model was compared to Cox models with basal measurement of classical risk factors, treatments, or competing risks. Models were cross-validated and compared for discrimination (area under ROC curve), calibration (Hosmer–Lemeshow test), and reclassification (categorical net reclassification index). Results: Cancer mortality was the highest cumulative-incidence event. Adding cholesterol and hypertension treatment to classical risk factors improved discrimination of coronary events by 2% and reclassification by 7–9%. The inclusion of competing risks and/or 2 measurements of risk factors provided similar coronary event prediction, compared to a single measurement of risk factors. Conclusion: Coronary risk prediction improves when cholesterol and hypertension treatment are included in risk functions. Coronary risk prediction does not improve with 2 measurements of covariates or inclusion of competing risks.