New Delhi, April 23: Cardiovascular disease in women remains underdiagnosed compared to men, so a new machine learning model that uses gender-specific criteria not only improves treatment outcomes, but also improves treatment outcomes. A study has revealed that it may help overcome the problem.
Although anatomical differences exist between male and female hearts because women's hearts are smaller and have thinner walls, the diagnostic criteria for certain heart diseases are the same for both.
“This means that women's hearts need to increase disproportionately more than men's before the same risk criteria are met,” the researchers wrote, published in the journal Frontiers in Physiology. argued in the paper.
The researchers found that this gender-neutral approach was particularly useful in “men with first-degree atrioventricular block (AV) block, a disease that affects the heartbeat, and dilated cardiomyopathy, a disease of the heart muscle. “This has led to severe underdiagnosis of 2 and 1.4 times more women.” Each. “
Skyler St. Pierre, a researcher at the Living Matter Institute at Stanford University in the United States, said: “We found that gender-neutral criteria do not adequately diagnose women, and that this underdiagnosis could be reduced if gender-specific criteria were used.'' It won't be as serious.”
“We also found that the best test to improve the detection of cardiovascular disease in both men and women is the electrocardiogram (EKG),” he added.
To build a more accurate cardiac risk model based on gender-specific criteria, the research team used four indicators not considered in the common Framingham risk score: cardiac magnetic resonance imaging, pulse wave analysis, electrocardiogram; Carotid artery ultrasound examination) has been added.
The Framingham Risk Score is a popular system for diagnosing heart risk based on age, gender, cholesterol levels, and blood pressure. This diagnostic system can estimate the likelihood that a person will develop heart disease within the next 10 years.
The research team used data from more than 20,000 individuals who took these tests at UK Biobank.
Using machine learning, researchers determined that of the indicators tested, electrocardiograms were the most effective at improving the detection of cardiovascular disease in both men and women. However, this does not mean traditional risk factors are not important tools for risk assessment, the researchers said.
“We believe that clinicians can first screen people using simple surveys that include traditional risk factors, and then perform a second stage of screening using electrocardiograms for high-risk patients. We propose,” they added.
