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Post: Women’s heart disease is underdiagnosed, but new machine learning models can help solve this problem

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Women's heart disease is underdiagnosed, but new machine learning models can help solve this problem
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Credit: Unsplash/CC0 Public Domain When it comes to matters of the heart, cardiovascular disease in women is underdiagnosed compared to men. A popular scoring system used to estimate how likely a person is to develop a cardiovascular disease within the next 10 years is the Framingham Risk Score. It is based on factors including age, sex, cholesterol levels, and blood pressure.

Researchers in the US and the Netherlands have now used a large dataset to build more accurate cardiovascular risk models than the Framingham Risk Score. They also quantified the underdiagnosis of women compared to men. The results were published in Frontiers in Physiology .

"We found that that sex-neutral criteria fail to diagnose women adequately. If sex-specific criteria were used, this underdiagnosis would be less severe," said Skyler St. Pierre, a researcher at Stanford University’s Living Matter Lab. "We also found the best exam to improve detection of cardiovascular disease in both men and women is the electrocardiogram (EKG)." Underdiagnosis due to heart differences

Anatomically, female and male hearts are different. For example, female hearts are smaller and have thinner walls. Yet, the diagnostic criteria for certain heart diseases are the same for women and men, meaning that women’s hearts must increase disproportionally more than men’s before the same risk criteria are met.

When the researchers quantified the underdiagnosis of women compared to men, they found that the use of sex-neutral criteria leads to severe underdiagnosis of female patients.

"Women are underdiagnosed for first degree atrioventricular block (AV) block, a disorder affecting […]

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