New algorithm set to reduce pressure on emergency departments
Artifical intelligence is at the heart of the new CoDE-ACS algorithm to improve diagnoses of heart attacks
An algorithm developed using artificial intelligence could soon be used by doctors to diagnose heart attacks with better speed and accuracy than ever before.
Research from the University of Edinburgh, funded by the British Heart Foundation charity and the UK’s National Institute for Health and Care Research, has been published this week in Nature Medicine.
The effectiveness of the algorithm, named CoDE-ACS, was tested on 10,286 patients in six countries around the world.
And researchers found that, compared to current testing methods, CoDE-ACS was able to rule out a heart attack in more than double the number of patients, with an accuracy of 99.6%.
This ability to rule out a heart attack faster than ever before could greatly reduce hospital admissions.
Clinical trials are now underway in Scotland, with support from the Wellcome Leap, to assess whether the tool can help doctors reduce pressure on overcrowded emergency departments.
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.
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
And 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.
The current gold standard for diagnosing a heart attack is measuring levels of the protein troponin in the blood.
But the same threshold is used for every patient and this means that factors like age, sex, and other health problems which affect troponin levels are not considered, affecting how accurate heart attack diagnoses are.
This can lead to inequalities in diagnosis.
For example, 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 new algorithm is an opportunity to prevent this.
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 0-100 for each patient.
The intellectual property underpinning the CoDE-ACS algorithm has been developed and protected with support from the University’s commercialisation service, Edinburgh Innovations. And routes into clinical settings are currently being explored and developed.
Professor Nicholas Mills, BHF professor of cardiology at the Centre for Cardiovascular Science at the 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 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.”
Dr John Lonsdale, head of enterprise at Edinburgh Innovations, added: “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.”