The PREVENT Equations: Implications for Stroke Prevention

Abstract

Stroke remains a leading cause of death and long-term disability. Yet much of its burden is preventable through earlier and more intensive management of vascular and cardio-kidney-metabolic (CKM) risk factors. Primary prevention is challenging as the first clinical event is often unpredictable and may manifest as stroke, myocardial infarction, heart failure, or peripheral arterial disease. Contemporary tools therefore estimate overall cardiovascular risk rather than stroke risk in isolation. The goal of this review is to emphasize that effective stroke prevention requires a shift toward global cardiovascular risk assessment and highlight the potential clinical utility of newer risk-prediction tools. The PREVENT (Predicting Risk of cardiovascular disease EVENTs) equations, developed by the American Heart Association, update absolute risk estimation by modeling overall cardiovascular disease risk using sex-specific, race-free equations that incorporate kidney function, account for competing non-cardiovascular death, and optionally include neighborhood deprivation. External validations suggest improved calibration compared with pooled-cohort equations, supporting a more reliable estimation of absolute treatment benefit. For clinicians managing patients at risk for stroke, PREVENT is most useful when the clinical question is how aggressively to optimize risk factors before the first event, including decisions about blood pressure, lipid-lowering therapy, CKM management, as well as patient communication using 10-year, 30-year, and “risk age” outputs. Key limitations include its focus on overall rather than cause-specific cardiovascular risk, lack of stroke mechanism–specific estimates, absence of major non-atherosclerotic stroke drivers such as atrial fibrillation, and lack of imaging-stratified treatment-effect estimates. PREVENT’s ultimate clinical impact and equity will depend on implementation within electronic health record workflows, auto-populated inputs, treatment pathways tied to estimated risk, and complementary cause-specific evaluation when clinically indicated.