Abstract
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 assessment of their performance (ie, accuracy, precision, incremental performance when added to contemporary multivariable risk models or established risk factors) and clinical utility (ie, actionability, scalability, generalizability) before adoption in clinical practice. Additional considerations in risk tool evaluation include reproducibility, cost-value considerations (including impact on downstream health care costs), and implications for health equity. This scientific statement defines a standardized framework for general considerations in risk prediction, statistical assessment of predictive utility, and critical appraisal of clinical utility and readiness. This scientific statement is intended to support clinicians, researchers, and policymakers in how best to evaluate current and emerging risk prediction tools and ultimately improve the prevention of cardiovascular disease in diverse populations.