Abstract

It is recognized that traditional risk factors do not identify everyone who will develop cardiovascular disease. There is a growing interest in the discovery of novel biomarkers that will augment the predictive potential of traditional cardiovascular risk factors. The era of genome-wide association studies (GWAS) has resulted in the discovery of common genetic polymorphisms associated with a multitude of cardiovascular traits and raises the possibility that these variants can be used in clinical risk prediction. Assessing and evaluating the new genetic risk markers and quantification of the improvement in risk prediction models that incorporate this information is a major challenge. In this paper we discuss the key metrics that are used to assess prediction models—discrimination, calibration, reclassification, and demonstration on how to calculate and interpret these metrics.