New Artificial Intelligence Tool May Revolutionise Heart Attack Diagnosis

New Artificial intelligence alogirthm CoDE-ACS can diagnose heart attacks with better speed and accuracy than ever before, according to new research from the University of Edinburgh, funded by the British Heart Foundation and the National Institute for Health and Care Research. The effectiveness of the algorithm was tested on 10,286 patients in six 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, with an accuracy of 99.6 per cent compared to current testing methods (1^✔ ✔Trusted Source
Early Diagnosis of Myocardial Infarction Using Absolute and Relative Changes in Cardiac Troponin Concentrations

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Can Heart Attack Be Detected Through Artificial Intelligence Tool?

This ability to rule out a heart attack faster than ever before could greatly reduce hospital admissions. Clinical trials are now underway in Scotland to assess whether the tool can help doctors reduce pressure on our 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 (2^✔ ✔Trusted Source
Machine Learning to Predict the Likelihood of Acute Myocardial Infarction

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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. 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 BHF-funded research has shown that women are 50 per cent more likely to get a wrong initial diagnosis. People who are initially misdiagnosed have a 70 per cent higher risk of dying after 30 days. The new algorithm is an opportunity to prevent this.

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CoDE-ACS 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 (3^✔ ✔Trusted Source
Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations

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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.

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

Chest pain is one of the most common reasons that people present to Emergency Departments. 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.

Hence, CoDE-ACS developed using cutting edge data science and AI could be transformational for Emergency Departments, shortening the time needed to make a diagnosis, and much better for patients.

References:

1. Early Diagnosis of Myocardial Infarction Using Absolute and Relative Changes in Cardiac Troponin Concentrations - (https://www.amjmed.com/article/S0002-9343(13)00351-3/fulltext)
2. Machine Learning to Predict the Likelihood of Acute Myocardial Infarction - (https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.119.041980)
3. Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations - (https://www.nature.com/articles/s41591-023-02325-4)

Source-Eurekalert