Sexism means thousands of women needlessly die from heart attacks

Woman heart attack
Woman heart attack

Sexism in medicine means thousands of women have suffered heart attacks without getting the right treatment, a study has shown.

Research presented at the world’s largest heart conference suggested that almost 12,000 women in the UK who should have been identified as at high risk of death were failed in the last two decades.

Previous studies have found that women suffering heart attacks were 50 per cent more likely than men to receive a wrong initial diagnosis, leading to thousands of needless deaths.

Experts said heart attacks in women were often missed, partly because medics still assumed victims were likely to be male and partly because symptoms could be less obvious.

The research involving medics from Royal Brompton and Harefield Hospitals and the University of Zurich, published in The Lancet, tracked women for 12 years.

It found 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 and thus requiring timely interventional treatment.

When the numbers were extrapolated, it equated to 11,651 females potentially being incorrectly classified and likely to have missed the right treatment, said researchers.

The study reported data from 420,781 patients with non-ST-segment elevation acute coronary syndromes – the most common type of heart attacks – recruited across Europe.

It found men were far more likely to be identified as high-risk patients.

‘Persistent biases’ in heart care

Dr Florian Wenzl, from the University of Zurich, the lead author of the study, 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.”

Researchers said the study showed the need to harness artificial intelligence to ensure all patients get the right treatment.

Thomas F Lüscher, from Royal Brompton and Harefield Hospitals, said: “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.”

Dr Sonya Babu-Narayan, associate medical director at the British Heart Foundation and a consultant cardiologist, 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.”

Robots could be better at diagnosing heart attacks

The research, presented at the European Society for Cardiology annual conference in Barcelona, follows a separate study that found artificial intelligence may be better than medics at diagnosing heart attacks in women.

The study of 13,000 patients by Edinburgh University found artificial intelligence could eradicate a “gender bias” which means female cases are often missed.

It found that the algorithm was developed from data on patients who went to hospital with a suspected heart attack. It was able to accurately rule out a heart attack 99.5 per cent of the time – meaning patients could be safely sent home.

It was also able to confirm whether a patient required further hospital tests in 83.7 per cent of cases. This is compared with accuracy rates of just 49.4 per cent for standard checks.

Scientists intend to develop an app to help doctors make an instant accurate diagnosis, simply by inputting blood test results and patient data.

Published by anthonyhayble

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