New technology can spot several of the underlying red flags for a future heart attack, British developers say.
Professor Charalambos Antoniades at the University of Oxford, UK, and colleagues created a new biomarker which they call the fat radiomic profile. It was discovered using machine learning, to detect biological red flags in the perivascular space lining blood vessels which supply blood to the heart.
Details appeared yesterday (3 September) in The European Heart Journal. The authors explain that it identifies inflammation, scarring and changes to these blood vessels.
The team hopes this will be a significant improvement on the current approach when someone arrives at hospital with chest pain. Currently, people are given a coronary CT angiogram to check for any narrowed or blocked segments, which tends to only identify the highest-risk patients.
Professor Antoniades and the team developed the new method after testing fat biopsies from 167 people undergoing cardiac surgery, to analyse the expression of genes associated with inflammation, scarring and new blood vessel formation.
Findings were compared to coronary CT angiogram results. This was then verified on figures from 5,487 people who went on to have a heart attack or cardiovascular death.
Professor 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, ultimately saving lives.”
Oikonomou, E. K. et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal 3 September 2019 doi: 10.1093/eurheartj/ehz592
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