diagnosing heart attacks

Researchers from UK have created a revolutionary Artificial Intelligence (AI) algorithm that could one-day aid medical professionals in accurately and speedily diagnosing heart attacks.

Researchers from the University of Edinburgh claim that the new algorithm, known as CoDE-ACS, was able to rule out a heart attack in more than twice as many patients with an accuracy of 99.6% when compared to existing testing techniques.

CoDE-ACS may also greatly help in reducing hospital admissions and rapidly identifying 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, a lot of illnesses can cause these typical symptoms, and diagnosing them is not always easy.”

“Harnessing data and artificial intelligence to support clinical decisions has enormous potential to improve care for patients and efficiency in our busy Emergency Departments,” observed Mills.

CoDE-ACS could not only rule out heart attacks but also aid medical professionals in determining if a patient’s abnormal troponin (a protein released into the circulation after a heart attack) levels were caused by a heart attack as opposed to 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.

Data from 10,038 patients in Scotland who had been admitted to the hospital with a suspected heart attack were used to create CoDE-ACS.

It analyses troponin levels and commonly gathered patient data to determine the likelihood that a patient has had a heart attack, including age, sex, ECG results, and medical history.

The outcome is a likelihood score for each patient ranging from 0 to 100.

Clinical trials are currently being conducted in Scotland to see whether the technology can assist doctors in easing the burden on crowded emergency rooms.

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