Artificial intelligence could improve heart attack diagnosis to reduce pressure on emergency departments

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Artificial intelligence could improve heart attack diagnosis to reduce pressure on emergency departments
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Artificialintelligence could improve heartattack diagnosis to reduce pressure on emergency departments NatureMedicine

External validation of the performance of the CoDE-ACS pathway in 10,286 patients with possible myocardial infarction. Diagnostic performance of CoDE-ACS models in 10,286 patients from seven international cohorts. Sensitivity, negative predictive value , specificity and positive predictive value with 95% CIs of the CoDE-ACS scores were used to identify patients as low probability or high probability of myocardial infarction at presentation and after serial troponin testing if required.

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 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-existingCoDE-ACS has the potential to makemore 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 B d research has shown that women are 50 percent more likely to get a wrong initial diagnosis. People who are initially misdiagnosed have a 70 percent higher risk of dying after 30 days. The new algorithm is an opportunity to prevent this.

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