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Artificial intelligence could speed up
heart attack diagnosis
New research funded by NIHR and the British Heart Foundation could
soon be used by doctors to diagnose heart attacks more quickly and
accurately. The research was led by scientists at the University of
Edinburgh, who tested an algorithm on over 10,000 patients around
the world.
The algorithm, called CoDE-ACS, uses patient information including
age, sex, heart measurements and other test results. This is combined
to give a score of how likely it is that each patient has had a heart
attack. By quickly spotting patients who are very unlikely to have had
a heart attack, the algorithm can ensure they’re sent home. And it can
make sure patients who are at higher risk stay in hospital for further
tests.
The algorithm was tested on patients in 6 countries around the world.
Researchers found that CoDE-ACS was able to rule out a heart attack in
more than double the number of patients, compared to current tests.
It had an accuracy of 99.6 per cent.
Improving on current tests
At the moment, the best way to diagnose a heart attack is to measure
levels of a protein called troponin in the patient’s blood. But the same
threshold is used for every patient. This means that other factors that
might affect troponin levels aren’t taken into account.
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.”
Steve Barclay, the health and social care secretary, said:
“Ground-breaking research like this — backed by over £134,000 of
government funding through the AI in Health and Care Awards — has
the potential to improve diagnosis and speed up access to treatment
for a range of conditions like heart attacks.
“This tool this could also reduce pressure on busy A&E departments and
support clinical decision-making. We continue to invest in the latest
technology, including AI, to help us cut waiting lists so people can get
the care they need more quickly.”
CoDE-ACS combines troponin measurements with other key information.
This gives a more accurate estimate of whether a particular patient has
had a heart attack.
The tool is now being tested in clinical trials in Scotland with support
from Wellcome Leap, to see whether it can help reduce pressure on
Emergency Departments. The research was funded by NIHR through
the NHS AI Lab’s AI in Health and Care Award, and published in Nature
Medicine.
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The Operating Theatre Journal
Issue 393
June
in
2023
TM
5