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HomeHeadline StoryGender bias in heart attacks treatment ‘killing thousands of women’

Gender bias in heart attacks treatment ‘killing thousands of women’

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A study has shown that due to sexism in medicine, thousands of women have suffered heart attacks and have been deprived of getting the right treatment, The Telegraph reports.

The Imperial College London study which was published in The Lancet took into account data from 420,781 heart patients with non-ST-segment elevation acute coronary syndromes (the most common type of heart attacks) and reportedly tracked women in the UK and Switzerland between (2005 and 2017) for 12 years.

The research that involved medics from Royal Brompton and Harefield Hospitals and the University of Zurich discovered that on average, 5.2 per cent more women with a heart attack per year in the UK should have been classified (as being at high risk of death) thus, requiring timely interventional treatment. However, they were wrongly classified as not at high risk of death, The Sun informs.

The researchers reportedly said, when the numbers were extrapolated, it equated to 11,651 females potentially being incorrectly classified (misdiagnosed) in the past two decades and likely to have missed the right emergency treatment.

Also, according to The Telegraph, previous studies found that women suffering heart attacks were 50 per cent more likely than men, to get a wrong initial diagnosis, thus leading to thousands of unnecessary deaths.

Experts claim the greater risk of misdiagnosis (missing of heart attacks in women) is partly because doctors still assumed victims were likely to be male and partly because symptoms could be less obvious for women.

Commenting on the results of the study, associate medical director at the British Heart Foundation and a consultant cardiologist Dr Sonya Babu-Narayan, reportedly said, “We live in a world where inequalities in heart attack care are costing women’s lives every day. Again and again, data from large numbers of people in different countries show that the odds of surviving a heart attack are stacked differently if you are a woman.

“To end this injustice requires change. We must ensure that heart tests and treatments are as equally well proven in women as they are in men and that we tackle the persistent biases that pervade society and healthcare – because heart attacks happen to women too.”

The Sun informs that in the UK, around 100,000 people a year are admitted to hospital following a heart attack. However, experts warn that the current tests for diagnosis of heart attacks were developed using data on men only – meaning they are less accurate for women.

With the objective of eliminating gender bias, experts now want hospitals to replace the current tests with more sensitive checks using artificial intelligence (allowing computers to make a decision), as these tests may be better than medics at diagnosing heart attacks in women.

The lead author of the study, Dr Florian Wenzl, from the University of Zurich is reported to have said, “The study shows that established risk models which guide current patient management are less accurate in females and favour the undertreatment of female patients.

“Using a machine learning algorithm and the largest datasets in Europe, we were able to develop a novel artificial intelligence-based risk score which accounts for sex-related differences in the baseline risk profile and improves the prediction of mortality in both sexes.”

Algorithms can be best understood as efficient in finding patterns and coming up with a process to make a decision. According to researchers, the study showed the need to harness artificial intelligence to ensure everyone gets the right treatment.

With regard to the novel score in treatment, Thomas F Lüscher, from Royal Brompton and Harefield Hospitals is quoted as saying, “I hope the implementation of this novel score in treatment algorithms will refine current treatment strategies, reduce sex inequalities, and eventually improve the survival of patients with heart attack – both male and female.”

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