New AI tool could help rule out heart attacks in A&E patients
A new artificial intelligence tool could rapidly rule out heart attacks in people attending A&E and free up valuable NHS time, a study revealed today.
The AI software developed by Imperial College London could prevent tens of thousands of unnecessary hospital stays each year in the UK by allowing patients to be safely sent home.
When someone arrives in hospital with a suspected heart attack, blood tests are used to help confirm their diagnosis. These tests measure levels of a protein called troponin, which rises when there is damage to the heart muscle.
Rises in troponin levels are not seen for hours, meaning patients are kept in hospital for ongoing evaluation. Many of these patients will be discharged without needing treatment.
The AI tool, named Rapid-RO, was developed using information from over 60,000 patients across the UK including age, initial results from blood tests and other basic health information.
It works by combining the results of patients’ initial troponin blood tests with other data collected upon hospital presentation. Patients are then classified as low or high risk.
The AI was tested on more than 33,000 patients across four UK hospitals over various time periods.
The team found that of the 33,727 patients, the AI tool successfully ruled out heart attacks in over a third (36 per cent) of patients, compared to 27 per cent ruled out by troponin testing alone.
Among these patients, the current approach of troponin testing missed four times as many heart attacks as the AI.
The researchers found that the AI was effective regardless of ethnicity, sex and whether patients had Covid.
The tool was developed by Imperial College PhD student Dario Sesia, who was supported by the British Heart Foundation.
Professor James Leiper, Associate Medical Director at the British Heart Foundation, said: “This research demonstrates the important role AI could play in guiding treatment decision for heart patients. By quickly identifying patients who are safe to be discharged, this technology could help people avoid unnecessary hospital stays, allowing valuable NHS time and resource to be redirected to where it could have the greatest benefit.”
