This AHA scientific statement described criteria for assessing the predictive and clinical utility of novel cardiovascular risk models, biomarkers, and tools, providing a methodological framework for evaluating new risk assessment technologies.
Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate established cardiovascular risk factors and have evolved over time from the Framingham Risk Model to the pooled cohort equations to the PREVENT (Predicting Risk of CVD Events) equations. Recent scientific (ie, genomics, proteomics, metabolomics) and methodologic (ie, artificial intelligence) advances have led to a proliferation of novel models, biomarkers, and tools for potential use in risk prediction. In parallel, the growing armamentarium of preventive therapies, some with considerable cost, underscores the need for more accurate and precise risk assessment to prioritize those at highest risk who will derive the greatest absolute benefit. Accompanying the considerable enthusiasm for the potential of newer approaches to improve risk prediction is the need for rigorous evaluation and assessm
