An algorithm developed with artificial intelligence could soon be adopted by doctors in Scotland to improve the speed and accuracy of heart attacks diagnoses – and reduce pressure on A&E departments.

A newly published study led by the University of Edinburgh reveals that the algorithm ruled out a heart attack in more than double the number of patients than current testing methods, with an accuracy of 99.6% when tested on 10,286 patients in Scotland.

The research details how the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) algorithm compares to current testing methods used in emergency departments.

Funded by the British Heart Foundation and the UK’s National Institute for Health and Care Research, CoDE-ACS was developed using data from patients 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.

Researchers say their AI-supported work means hospital admissions could be greatly reduced with the ability to rule out a heart attack faster than ever before:

‘CoDE-ACS has the potential to make emergency care more efficient and effective, by rapidly identifying patients that are safe to go home, and by highlighting to doctors all those that need to stay in hospital for further tests.’

In January, Lenus Health partnered with NHS Lothian and Edinburgh researchers to co-design a more effective digital health pathway and improve emergency cardiac care for patients in Scotland.

Initially tested in six countries across the world, the algorithm is currently being tested clinical trials across Scotland. The clinical trials are led by the partnership, with support from Wellcome Leap, and will assess the algorithm's impact on reducing A&E pressure.

The current ‘gold standard’ for diagnosing a heart attack is measuring the levels of the protein troponin in the blood – with this same threshold used for every patient.

Alongside quickly ruling out heart attacks in patients, the team which developed CoDE-ACS hopes it could also help doctors identify when abnormal troponin levels are due to a heart attack.

Professor Nicholas Mills, BHF Professor of Cardiology at the Centre for Cardiovascular Science, University of Edinburgh, who led the research, 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.”

The AI tool performed well regardless of age, sex or pre-existing health conditions – with researchers hoping this indicates a potential for reducing misdiagnoses and health inequalities.

Factors like age sex and other health problems which affect troponin may be considered by clinicians but are not directly accounted in the measurement system for troponin levels. This can affect how accurate heart attack diagnoses are and lead to inequalities in diagnoses.

Previous research has shown that women are 50% more likely to get a wrong initial diagnosis, and people who are initially misdiagnosed have a 70% higher risk of dying. The Edinburgh team says this new algorithm presents an opportunity to prevent this.

Routes into clinical settings for the CoDE-ACS algorithm are currently being explored and developed, with support from Edinburgh Innovations. Dr John Lonsdale, Head of Enterprise at Edinburgh Innovations, said:

“We are proud to be supporting Nick and his team as they take their research and invention out of the University and into clinical settings where it can really make a difference to healthcare outcomes.”

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