New AI Heart Attack Risk Assessment Aims To Break Down Gender Inequities And Improve Treatment Outcomes Among Women
According to the CDC, heart attacks are one of the leading causes of death among both men and women in the U.S.
However, women are more likely to suffer fatal heart attacks than men because of inadequate risk assessment and treatment practices.
For example, symptoms of heart attacks are most commonly thought to manifest via chest pain that radiates through the left arm. But, this warning signal is actually just unique to men.
Women, on the other hand, often experience heart attack pain in their abdomen. Then, this pain radiates to the back or manifests as nausea and vomiting.
In turn, these heart attack symptoms are often misconstrued by both patients and medical professionals upon examination– leading to a higher morbidity rate.
But, a new study led by Thomas F. Luscher of the Center for Molecular Cardiology at the University of Zurich has analyzed the role of biological gender in heart attacks and potentially found a more viable treatment option to avoid the common pitfalls.
“There are notable differences in the disease phenotype observed in females and males. Our study shows that women and men differ significantly in their risk factor profile at hospital admission,” Luscher began.
“However, when these differences are taken into account statistically, women and men have similar mortality.”
The study, which has since been published in The Lancet, included data from over four hundred and twenty thousand European patients who have suffered the most common heart attack type.
annanahabed – stock.adobe.com – illustrative purpose only, not the actual person
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And it overwhelmingly found that current assessment models are not equipped to properly diagnose women.
“The study shows that established risk models, which guide current patient management, are less accurate in females and favor the undertreatment of female patients,” underscored Florian A. Wenzl, the study’s first author.
The researchers did not stop there, though. They also worked to develop a new machine learning algorithm to diagnose and treat both genders more accurately.
“We were able to develop a novel artificial intelligence-based risk score which accounts for gender-related differences in the baseline risk profile and improves the prediction of mortality in both genders,” Wenzl explained.
Now, the research team hopes this personalized patient care system will help break down gender inequities, refine treatment strategies, and vastly improve heart attack survival rates for people of both genders.
To read the study’s complete findings, visit the link here.
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