C-6 | Empowering Women in Cardiovascular Care: Bridging the Gender Gap in ASCVD Risk Prediction with Women-Specific Factors

Atherosclerotic cardiovascular disease (ASCVD) is the leading global cause of mortality, with gender disparities in risk assessment and outcomes. Traditional models like the Pooled Cohort Equations (PCE) and Framingham Risk Score (FRS) underestimate women’s risk, leading to undertreatment. Women-specific factors, such as adverse pregnancy outcomes, hormonal changes, and autoimmune diseases, are largely excluded. This review examines current model limitations, unique risk factors in women, and the potential of machine learning (ML) to improve prediction.