Atherosclerotic Cardiovascular Disease Risk Estimates Using the New Predicting Risk of Cardiovascular Disease Events Equations: Implications for Statin Use.

Abstract

Purpose of review: Atherosclerotic cardiovascular disease (ASCVD) continues to remain a leading cause of morbidity and mortality worldwide. Risk estimation is fundamental for primary prevention by ensuring that interventions such as lipid lowering or antihypertensive therapy are targeted towards the populations that would most benefit from their use. The Pooled Cohort Equations (PCE), developed in 2013 by the American College of Cardiology (ACC) and American Heart Association (AHA), have been extensively applied to ASCVD risk estimation. However, limitations posed by the PCE include, but are not limited to, race-based adjustments, overdependence on age, limited generalizability, and the development of larger epidemiologic cohorts, all of which eventually necessitated the development of the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations. The PREVENT equations are intended to address the limitations posed by the former equations by expanding the applicable age range, including additional risk factors, and providing 10- and 30-year predictions for cardiovascular disease (CVD), ASCVD, and heart failure (HF). The purpose of this review is to evaluate the rationale for risk estimation, the evolution of cardiovascular risk prediction tools, the derivation and limitations surrounding PREVENT, and its potential implications for recommendations regarding preventive therapy initiation and continuation. Further, this review elects to focus on the outcome of ASCVD and not discuss HF.

Recent findings: Analysis of the PREVENT equation, especially in comparison to the PCE, shows that PREVENT leads to lower predicted risk and therefore lower provision of preventive therapies, including reducing statin eligibility by 17.3 million U.S. adults. This review summarizes the recent data regarding the changes in risk stratification, the potential changes in preventive treatment allocation, and some of the limitations that arise from the new risk prediction equations. While the PREVENT equations are an improvement in cardiovascular risk prediction, their impact on treatment raises questions that will need to be carefully studied as PREVENT is implemented in clinical practice. Future studies will need to evaluate the clinical impact of PREVENT across diverse populations and ascertain the impact on preventive care and cardiovascular outcomes.