The University’s researchers — who have developed an AI-based tool to help clinicians diagnose heart attacks more accurately — found that their technology, dubbed “CoDE-ACS,” was able to rule out a heart attack in more than double the number of patients when compared to current testing methods, and with an accuracy of 99.6%.

CoDE-ACS was developed using data from over 10,000 patients in Scotland who had set foot in hospitals 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.

The researchers said that the algorithm performed well regardless of age, sex, or pre-existing health conditions, showing its potential for reducing misdiagnosis and inequalities across the population.

Scottish clinical trials are now underway, with support from the Wellcome Leap, the innovation investing firm, to assess whether the tool can help doctors reduce pressure on overcrowded A&E departments.

In addition to 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 a separate condition.

The current gold standard for diagnosing a heart attack is measuring levels of troponin, a protein that’s released during a heart attack, in the blood. However, the same threshold is used for every patient.

This means that factors — like age, sex, and any underlying issues — which affect troponin levels are not considered, affecting the accuracy of heart attack diagnosis. This can then lead to inequalities in diagnosis, which the new algorithm could help to prevent.

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“For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives,” said Professor Nicholas Mills, BHF Professor of Cardiology at the Centre for Cardiovascular Science, University of Edinburgh, who led the research on CoDE-ACS.

“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, Medical Director at the British Heart Foundation, also commented, saying: “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.”

The University of Edinburgh’s research into CoDE-ACS and its effectiveness was funded by the British Heart Foundation and the National Institute for Health and Care Research. The findings were recently published in the Nature Medicine journal.