Integrating Information From Novel Risk Factors With Calculated Risks

Case vignette: a 60-year-old man visits his physician for assessment of his 10-year cardiovascular risk. On the basis of his systolic blood pressure, lipid profile, smoking status, and the fact that he is nondiabetic, the Framingham risk score estimates his risk to be 8%. The physician wonders if he could further specify the patients risk by performing an additional test like coronary calcium score or microalbuminuria (MA). For matters of convenience and costs he decides to test MA, which turns out positive. Assuming that MA has an invariable and exact relative risk (RR), independent from the aforementioned classical risk factors, of 2.0, what would this man's estimated risk become?Prediction of absolute disease risk is an essential component of cost-effective disease prevention strategies. In cardiovascular disease (CVD) prevention, for example, antiplatelet and statin therapy is applied if absolute risk of CVD is considered sufficiently high. Various prediction models are available for the purpose of risk calculation. These models are derived from large population-based cohorts in which conventional CVD risk factors and prospective event registrations are available. Well known examples include the Framingham risk score and the risk model of the European SCORE consortium.1,2Obviously, with regard to individual risk estimation, risk models have inherent shortcomings in terms of precision and reliability. In an attempt to improve risk prediction, much focus has been on the potential benefit of adding information relating to novel risk factors. Various statistical methods have been developed to assess the ability of novel risk factors to improve risk stratification. These methods include assessment of discrimination and calibration of the conventional versus the updated risk model.3,4 The ultimate goal of adding novel risk factors is to improve a patient's health by correctly reclassifying him or her into high, intermediate, and low risk …