Medical doctor holding red heart symbol on white background

AI tool has potential to predict future heart attacks

Image credit: Tatyana Vychegzhanina | Dreamstime.com

Researchers from the University of Oxford have developed a new biomarker, which uses artificial intelligence (AI), that can identify people at high risk of a fatal heart attack at least five years before it strikes.

In research funded by the British Heart Foundation (BHF), the team developed the biomarker, or ‘fingerprint’ – called the fat radiomic profile (FRP), using machine learning. The FRP reveals biological red flags in the perivascular space lining blood vessels which supply blood to the heart.

Furthermore, the tool identifies inflammation, scarring, and changes to these blood vessels, which all indicate the chances of a heart attack in the future.

Very often when an individual goes to the hospital with chest pain, a standard component of care is to have a coronary CT angiogram (CCTA). This is a scan of the coronary arteries to check for any narrowed or blocked segments.

If there is no significant narrowing of the artery, which accounts for about 75 per cent of scans, people are sent home. However, some of these people who have previously gone for check-ups will still have a heart attack in the future. Also, there are currently no methods used routinely by doctors that can spot all of these underlying red flags for a future heart attack.

“Just because someone’s scan of their coronary artery shows there's no narrowing, that does not mean they are safe from a heart attack,” said Professor Charalambos Antoniades, professor of cardiovascular medicine and BHF senior clinical fellow at the University of Oxford.

To challenge this issue, Antoniades and his team used fat biopsies from 167 people undergoing cardiac surgery, analysing the expression of genes associated with inflammation, scarring, and new blood vessel formation. They then matched these to the CCTA scan images to determine which features best indicate changes to the fat surrounding the heart vessels, called perivascular fat.

Next, the team compared the CCTA scans of the 101 people, from a pool of 5,487 individuals, who went on to have a heart attack or cardiovascular death within five years of having a CCTA with matched controls who did not, to understand the changes in the perivascular space which indicate that someone is at higher risk of a heart attack.

Using machine learning, they developed the FRP fingerprint that captures the level of risk, and according to the researchers, the more heart scans that are added, the more accurate the predictions will become, and the more information embedded in the tool will become ‘core knowledge’.

They tested the performance of this perivascular fingerprint in 1,575 people in the SCOT-HEART trial, showing that the FRP had a striking value in predicting heart attacks, above what can be achieved with any of the tools currently used in clinical practice.

“By harnessing the power of AI, we’ve developed a fingerprint to find 'bad' characteristics around people’s arteries,” Antoniades said. “This has huge potential to detect the early signs of disease, and to be able to take all preventative steps before a heart attack strikes, ultimately saving lives.”

“We genuinely believe this technology could be saving lives within the next year,” he added.

The team hopes the technology will enable a greater number of people to avoid a heart attack. They also plan to roll it out to healthcare professionals in the next year, with the hope that it will be included in routine NHS practice alongside CCTA scans in the next two years.

“Every five minutes, someone is admitted to a UK hospital due to a heart attack,” said Professor Metin Avkiran, associate medical director at the British Heart Foundation. “This research is a powerful example of how innovative use of machine learning technology has the potential to revolutionise how we identify people at risk of a heart attack and prevent them from happening,”

“This is a significant advance. The new ‘fingerprint’ extracts additional information about underlying biology from scans used routinely to detect narrowed arteries. Such AI-based technology to predict an impending heart attack with greater precision could represent a big step forward in personalised care for people with suspected coronary artery disease.”

The findings are being presented at the European Society of Cardiology (ESC) Congress in Paris and published in the European Heart Journal.

In August, the Government announced the funding of £250m for the creation of a National Artificial Intelligence Lab that will help the NHS more effectively treat conditions such as cancer, dementia, and heart disease.

Also in June, researchers from the University of Washington said that virtual assistants such as Amazon’s Alexa and Google Home can be used to detect when a user is having a heart attack.

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