New AI model may soon help doctors diagnose heart attacks accurately

According to researchers from the University of Edinburgh, the new algorithm, called CoDE-ACS, was able to rule out a heart attack in more than double the number of patients, with an accuracy of 99.6 per cent in comparison to current testing methods.

CoDE-ACS may also greatly help in reducing hospital admissions and rapidly identify patients that are safe to go home. The findings are published in the journal Nature Medicine.

“For patients with acute chest pain due to a heart attack, early diagnosis and treatment saves lives,” said Prof. Nicholas Mills, who led the research.

“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,” Mills noted.

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In addition to ruling out heart attacks, CoDE-ACS could also help doctors identify those whose abnormal troponin (protein released into the bloodstream during a heart attack) levels were due to a heart attack rather than another condition.”Chest pain is one of the most common reasons that people present to emergency departments,” said Prof. Sir Nilesh Samani, medical director of the British Heart Foundation.

“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,” he added.

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.

Clinical trials are now under way in Scotland to assess whether the tool can help doctors reduce pressure on overcrowded emergency departments.