Oxford researchers have developed a new biomarker – derived through artificial intelligence analysis of routine CT scans – that can identify people at high risk of a fatal heart attack at least five years before it strikes.
The researchers, who are supported by the NIHR Oxford Biomedical Research Centre, developed the new fat radiomic profile (FRP) biomarker, using machine learning.
This FRP ‘fingerprint’ detects biological ‘red flags’ in the perivascular space lining the vessels that supply blood to the heart. It identifies inflammation, scarring and changes to these blood vessels, which can all indicate a future heart attack.
When someone goes to hospital with chest pain, a standard component of care is to have a coronary CT angiogram (CCTA), a scan of the coronary arteries, to check for any narrowed or blocked segments.
If there is no significant narrowing of the artery, which is the case in about 75 per cent of scans, people are sent home. Yet some of them will still have a heart attack at some point in the future. There are no methods used routinely by doctors that can spot all of the underlying red flags for a future heart attack.
The findings from the study, which was also funded by the British Heart Foundation (BHF), were being presented at the European Society of Cardiology (ESC) Congress in Paris and published in the European Heart Journal.
Prof Charalambos Antoniades and his team used fat biopsies from 167 people undergoing cardiac surgery. They analysed the expression of genes associated with inflammation, scarring and new blood vessel formation, and matched these to the CCTA scan images to determine which features best indicate changes to the fat surrounding the heart vessels, called perivascular fat.
They then compared the CCTA scans of 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 that 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. The more heart scans that are added, the more accurate the predictions will become, and the more information that will become ‘core knowledge’.
They tested the performance of this perivascular fingerprint in 1,575 people and found that the FRP was far more effective at predicting heart attacks that any of the tools currently used in clinical practice. The team plan to roll this powerful technology out to health care professionals in the next year, with the hope that it will be included in routine NHS practice alongside CCTA scans in the next 2 years.
Prof Antoniades said: “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.
“By harnessing the power of AI, we’ve developed a fingerprint to find ‘bad’ characteristics around people’s arteries. 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. We genuinely believe this technology could be saving lives within the next year.”
Professor Metin Avkiran, Associate Medical Director at the British Heart Foundation, said: “Every five minutes, someone is admitted to a UK hospital due to a heart attack. 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.”