Historically, cardiovascular medicine has been biased towards males, resulting in underdiagnosis and delayed treatment for women. However, recent advancements in machine learning are revolutionizing risk prediction models, particularly benefiting female patients. Researchers from the US and the Netherlands developed a more accurate cardiovascular risk models compared to the traditional Framingham Risk Score by using a vast dataset and machine learning algorithms. They revealed a concerning trend: women are underdiagnosed twice as often as men for certain heart conditions, emphasizing the urgency for sex-specific risk criteria.

The anatomical differences between male and female hearts pose a challenge in accurate diagnosis. Despite these variations, diagnostic criteria have remained uniform across genders, leading to disparities in detection. The study found that employing sex-neutral criteria exacerbates the underdiagnosis of female patients, particularly for conditions like atrioventricular block and dilated cardiomyopathy.

To address these discrepancies, the researchers integrated additional metrics such as cardiac magnetic resonance imaging and pulse wave analysis, alongside traditional factors, into their models. Among these, electrocardiograms (EKGs) emerged as the most effective tool for early disease detection in both sexes.

However, while machine learning offers promising avenues for personalized medicine, the study acknowledges certain limitations. The binary treatment of sex overlooks its complex nature, encompassing hormonal, chromosomal, and physical variations. Moreover, the study’s focus on middle-aged individuals in the UK Biobank raises questions about the generalizability of findings across diverse demographics.

Despite these challenges, the study marks a pivotal step towards gender-specific medicine in cardiovascular care. By embracing new technologies and refining risk prediction models, clinicians can tailor interventions to individual patient needs, ultimately improving outcomes for all. As lead researcher Skyler St. Pierre notes, while sex-specific medicine is crucial, the ultimate goal should be patient-specific care, ensuring optimal health for everyone.

Article written by Deborah Pirchner