PREVENT Equations: Implications for Stroke Prevention

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 cardiovascular-kidney-metabolic 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 to highlight the potential clinical utility of newer risk prediction tools. The 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 noncardiovascular 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, predicting risk of cardiovascular disease events 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, cardiovascular-kidney-metabolic management, and 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 nonatherosclerotic stroke drivers such as atrial fibrillation, and lack of imaging-stratified treatment-effect estimates. Predicting the risk of cardiovascular disease events' ultimate clinical impact and equity will depend on implementation within electronic health record workflows, autopopulated inputs, treatment pathways tied to estimated risk, and complementary cause-specific evaluation when clinically indicated.