AI COULD SOON BE USED TO DIAGNOSE HEART ATTACKS

[AN ALGORITHM developed using artificial intelligence could soon be used by doctors to diagnose heart attacks with better speed and accuracy, according to new research from the University of Edinburgh. The research, funded by the British Heart Foundation and the National Institute for Health and Care Research, has been published in the journal Nature Medicine

The effectiveness of the algorithm, named CoDE-ACS, was tested on 10,286 patients in six countries around the world. Researchers found that, compared to current testing methods, CoDE-ACS was able to rule out a heart attack in more than double the number of patients, with an accuracy of 99.6%.

That ability to rule out a heart attack faster than before could greatly reduce hospital admissions. Clinical trials are now underway in Scotland with support from the Wellcome Leap, to assess whether the tool can help doctors reduce pressure on our overcrowded EDs.

Tackling inequality

As well as quickly ruling out heart attacks in patients, CoDEACS could help doctors to identify those whose abnormal troponin levels were due to a heart attack rather than another condition. The AI tool performed well regardless of age, sex or pre-existing health conditions, showing its potential for reducing misdiagnosis and inequalities across the population.

The current gold standard for diagnosing a heart attack is measuring levels of the protein troponin in the blood; but the same threshold is used for every patient. That means factors such age, sex and other health problems which affect troponin levels are not considered, affecting how accurate heart attack diagnoses are.

That can lead to inequalities in diagnosis. For example, previous BHF-funded research has shown that women are 50% more likely to get a wrong initial diagnosis. People who are initially misdiagnosed have a 70% higher risk of dying after 30 days. The new algorithm is an opportunity to prevent that.

The research was led by Professor Nicholas Mills, BHF Professor of Cardiology at the Centre for Cardiovascular Science, University of Edinburgh. He explained: “For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives.

“Unfortunately, many conditions cause these common symptoms and the diagnosis is not always straightforward. Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy emergency departments.”

Professor Sir Nilesh Samani of the BHF added: “Chest pain is one of the most common reasons that people present to emergency departments. Every day, doctors around the world face the challenge of separating patients whose pain is due to a heart attack from those whose pain is due to something less serious.

“CoDE-ACS, developed using cutting-edge data science and AI, has the potential to rule-in or rule-out a heart attack more accurately than current approaches. It could be transformational for emergency departments, shortening the time needed to make a diagnosis, and much better for patients.” q