Cardiovascular disease (CVD) in women is often underdiagnosed compared to men, largely due to the use of sex-neutral diagnostic criteria. Traditional risk models, such as the Framingham Risk Score, are commonly used to estimate the likelihood of developing heart disease in the next decade. However, these models, which rely on factors like age, sex, cholesterol levels, and blood pressure, do not account for physiological differences between men and women. Research by scientists in the US and the Netherlands highlights this disparity, revealing that women are more likely to be misdiagnosed with conditions like first-degree atrioventricular block and dilated cardiomyopathy. In fact, women were found to be twice as likely to be underdiagnosed for certain heart disorders when using sex-neutral criteria.
The researchers proposed new cardiovascular risk models that incorporate advanced diagnostic tools beyond the Framingham Risk Score. By analyzing data from over 20,000 individuals in the UK Biobank, the study integrated cardiac magnetic resonance imaging, pulse wave analysis, electrocardiograms (EKGs), and carotid ultrasounds, offering more accurate predictions for both men and women. Of these, EKGs were found to be particularly effective at improving disease detection in both sexes. The researchers suggest that clinicians could first screen patients using traditional risk factors and then follow up with EKGs for those at higher risk.
Despite the promising results, the study acknowledges some limitations, such as the binary classification of sex in the UK Biobank, which does not capture the full complexity of sex and gender. Additionally, the study population was primarily middle-aged and older adults from the UK, which may limit the applicability of the findings to other populations. Moving forward, the researchers emphasize the importance of personalized medicine to improve heart disease risk prediction for all individuals.
