The Evaluation of Novel Biomarkers and the Multiple Biomarker Approach in the Prediction of Cardiovascular Disease

Since the release of the Framingham Risk Score in 1998 Wilson et al. (Circulation 97:1837–47, 1998), the search for blood-based biomarkers to better predict cardiovascular disease (CVD) and its associated outcomes has intensified. Multiple cardiac biomarkers have been tested over the past 2 decades, and some have been discovered to be successful in predicting such events. However, these individual biomarkers have not significantly added to existing risk algorithms (ie, the Framingham Risk Score). The hypothesis that biomarkers with small amounts of individual benefit used collectively would provide better overall prediction led to the development of aggregate biomarker models. Herein, we discuss the qualities of an ideal biomarker, statistical methods used to interrogate biomarker risk prediction algorithms, the multiple biomarker approach to cardiovascular disease, and potential strategies to use in selecting the right biomarker for aggregate biomarker models. © 2014, Springer Science+Business Media New York.