Machine Learning Models Aid in Women’s Heart Disease Diagnosis
A significant issue has been identified in the diagnosis of cardiovascular diseases, particularly in women, with current scoring systems showing underdiagnosis compared to men. While the Framingham Risk Score is commonly used to estimate the likelihood of developing cardiovascular disease over a ten-year period, researchers from the US and the Netherlands have found that it may not be adequately accurate, especially for women.
The differences in heart anatomy between men and women play a crucial role in this underdiagnosis. Female hearts are typically smaller and have thinner walls compared to male hearts. However, the diagnostic criteria remain the same for both genders, requiring women’s hearts to exhibit disproportional changes before reaching the same risk threshold as men.
The study revealed that the use of sex-neutral criteria leads to a significant underdiagnosis of heart conditions in female patients. For instance, women are twice as likely to be underdiagnosed for first-degree atrioventricular block (AV block) and 1.4 times more likely to be underdiagnosed for dilated cardiomyopathy compared to men. This underdiagnosis applies to various other heart disorders as well.
To address these issues and provide more accurate predictions for both sexes, researchers utilized advanced metrics not considered in the Framingham Risk Score. By analyzing data from the UK Biobank – a database containing health information from over 20,000 individuals – they identified four additional factors, including cardiac magnetic resonance imaging, pulse wave analysis, EKGs, and carotid ultrasounds, that could enhance early disease detection.
Machine learning played a crucial role in the development of new risk models, highlighting the effectiveness of EKGs in improving cardiovascular disease detection in both men and women. The researchers recommend a two-stage screening approach for higher-risk patients, using traditional risk factors as an initial assessment and EKGs for follow-up evaluation.
Although the study marks a significant advancement in rethinking risk factors for heart disease, there are limitations to be addressed in future research. The binary classification of sex in the UK Biobank overlooks the complexity of gender, encompassing hormonal, chromosomal, and physical differences that may impact heart health. Additionally, the study primarily focused on middle-aged and older individuals in the UK, raising questions about the generalizability of the findings to diverse populations.
Moving forward, the researchers advocate for personalized medicine tailored to individual patients, emphasizing the need for patient-specific risk assessment strategies to enhance health outcomes for everyone. By leveraging new technologies and innovative approaches, the field of cardiovascular medicine is poised to make significant strides in improving risk prediction and disease management.
