HEART attacks could be diagnosed more quickly and accurately using an artificial intelligence tool developed by scientists in Scotland.
Researchers at Edinburgh University found that an algorithm developed using AI technology was able to rule out a heart attack in more than double the number of patients compared to standard approaches, with an accuracy of 99.6 per cent.
The ability to rule out a heart attack faster than ever before has the potential to significantly reduce hospital admissions.
Chest pain is one of the most common reasons for people presenting at A&E.
Clinical trials are now underway in Scotland with support from the Wellcome Leap, which funds unconventional projects which have the capacity to deliver breakthroughs in human health in five to 10 years.
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The trials will investigate whether use of the algorithm can help doctors reduce pressure on overcrowded emergency departments by identifying patients earlier who are safe to discharge home and flagging up those who require additional tests.
The formula, named CoDE-ACS, was developed using data from 10,038 patients in Scotland who had arrived at hospital with a suspected heart attack.
It uses routinely collected patient information, such as age, sex, ECG findings and medical history, as well as troponin levels, to predict the probability that an individual has had a heart attack.
The result is a probability score from 0 to 100 for each patient.
The effectiveness of CoDE-ACS was then tested on 10,286 patients in six countries around the world in a study led by Edinburgh University with funding from the British Heart Foundation and the National Institute for Health and Care Research.
The findings are published today in the journal, Nature.
The current gold standard for diagnosing a heart attack is measuring levels of the protein troponin in the blood.
However, as the same threshold is used for every patient, this means that factors such as age, sex and other health problems - which affect troponin levels - are not considered.
This can impact on the accuracy of heart attack diagnoses, and lead to inequalities in diagnosis.
For example, previous BHF-funded research has shown that women are 50 per cent more likely to get a wrong initial diagnosis.
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People who are initially misdiagnosed have a 70% higher risk of dying after 30 days - something that researchers hope their algorithm can help to prevent.
As well as quickly ruling out heart attacks in patients, CoDE-ACS could also 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.
Professor Nicholas Mills, the BHF Professor of Cardiology who led the research at Edinburgh University's Centre for Cardiovascular Science, said: “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 straight forward.
"Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy emergency departments.”
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Professor Sir Nilesh Samani, Medical Director of the British Heart Foundation, said:
"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.”
Steve Goodacre, a professor of emergency medicine at Sheffield University said the findings from the initial study were "intriguing".
He added: “This doesn't (yet) show that we can replace doctors with computers. Experienced clinicians know that diagnosis is a complex business.
"Indeed, the 'ground truth' used to judge whether the AI algorithm was accurate was a judgement made by clinicians.
“It will be interesting to see how clinicians in the emergency department use this algorithm. What will they do if they think the algorithm has got it wrong?
"The next stage of the research will hopefully answer that question.”
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