Heart attack diagnosis could be achieved quicker and more accurately via the use of a new artificial intelligence algorithm developed by the University of Edinburgh.
Tested on 10,286 patients in six countries, the algorithm – named CoDE-ACS – was able to rule out a heart attack in more than double the number of patients compared to guideline-recommended pathways, and with an accuracy of 99.6%.
The tool was also found to identify those whose abnormal troponin levels were due to a heart attack rather than another condition.
It performed well regardless of age, sex or pre-existing health condition, which the authors say reduces the potential for misdiagnosis and inequalities across the population.
Funded by the British Heart Foundation (BHF) and the UK’s National Institute for Health and Care Research, the study was published in the journal Nature Medicine.
Its lead author, Professor Nicholas Mills, BHF professor of cardiology at the Centre for Cardiovascular Science, University of Edinburgh, 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.’
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 underway in Scotland to assess whether the AI tool can help doctors reduce pressure on overcrowded emergency departments.