Integrating Information From Novel Risk Factors With Calculated Risks

Abstract

C ase 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 microalbumin-uria (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? The Problem Prediction of absolute disease risk is an essential component of cost-effective disease prevention strategies. In cardiovas-cular 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. 1,2 Obviously, 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 categories for which the net reclassification improvement is one appropriate parameter. 5,6 Although models may, as judged from the net reclassifi-cation improvement, improve as a result of including a novel risk factor, such expanded models are hardly used in clinical practice. Moreover, literature addressing novel risk factors often does not provide these expanded risk models but, instead, provides an independent RR or standardized of the novel risk factor. Integrating a novel risk factor in a new model is very different from using a novel risk factor on top of an existing model. In the latter context, the model delivers a baseline risk and the independent RR from the novel risk factor must somehow be used to convert this baseline risk into a recalculated risk. Although several national and international guidelines encourage the use of novel risk factors, they do not describe how to obtain a new recalculated risk using this additional information. Intuitively, and sometimes explicitly, the RR of novel risk factor is directly translated into a multiplication factor. 7,8 In other words, the risk of the patient in the case vignette would be multiplied by 2.0 to give a recalculated risk of 16%, assuming that the RR of 2 implies doubling of risk. We will explain that this reasoning is incorrect. Imagine the RR and the multiplication factor to be identical. In the example of the case vignette, if the multiplication factor would be either 2.0 (MA present) or 1.0 (MA absent), the recalculated risk can only remain unchanged or adjusted upward but never downward. Hence, in the stratum (ie, the imaginary group of individuals with the same Framingham risk factor profile), the average risk would increase, solely by adding risk information. This cannot be correct because adding risk information simply cannot increase average risk. Hence, upward adjustments of risk due to presence of an additional risk factor in some individuals should be compensated by downward adjustments for absence of the same risk From the The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA. 111.035725/-/DC1.