patients with genetic heart disease
A new, personalised heart modelling approach can predict abnormal heart rhythms and risk of sudden death in patients with hypertrophic cardiomyopathy, and may help determine who might benefit from a defibrillator.

Date:
January 25, 2022
Source:
eLife
Summary:
Modelling the heart in 3D using combined imaging techniques can
help predict heart rhythm abnormalities, or arrhythmias, in patients
with the genetic heart disease hypertrophic cardiomyopathy.



FULL STORY ========================================================================== Modelling the heart in 3D using combined imaging techniques can help
predict heart rhythm abnormalities, or arrhythmias, in patients with a
genetic heart disease, shows a study published today in eLife.


==========================================================================
The technique may one day help clinicians determine which patients with
a condition called hypertrophic cardiomyopathy might benefit the most
from a defibrillator.

Hypertrophic cardiomyopathy is a disease that causes thickening and
scarring in the heart muscles. Some people may not have clear symptoms
of the condition, but others may develop heart rhythm abnormalities that
can lead to sudden death.

"There are ways to predict which patients are at risk of developing
heart arrhythmias and who might benefit from a defibrillator, but these
methods are not highly accurate," explains first author Ryan O'Hara,
Research Assistant at the Trayanova Lab, Johns Hopkins University,
Baltimore, Maryland, US. "We wanted to develop a more accurate
and personalised approach for predicting abnormal heart rhythms in
patients with hypertrophic cardiomyopathy." To do this, O'Hara and his colleagues combined two techniques: contrast- enhanced cardiac magnetic resonance imaging with late gadolinium enhancement, and post-contrast T1 mapping. Together, these methods enabled the team to construct digital, personalised replicas of patients' hearts, including specific scarring
and thickening characteristics, and to evaluate the likelihood that they
would go on to develop heart arrhythmias.

Testing this approach in 26 patients showed that it accurately predicted
which patients would develop abnormal heart rhythms approximately 80%
of the time.

"This is a substantial improvement over currently used prediction
techniques, which are only accurate about half of the time," O'Hara says.

Their work also showed that cardiac diffuse fibrosis -- lesions that are associated with progression to heart failure, but are rarely assessed in patients with hypertrophic cardiomyopathy -- can increase these patients' likelihood of developing an abnormal heart rhythm.

More studies are now needed to confirm if this personalised modelling
approach can be used to guide patient care before the technique can be
adopted more widely.

"If our technology is shown by larger studies to be superior to existing prediction methods, then it could help ensure that patients at high
risk of heart arrhythmia receive a defibrillator or other appropriate intervention," concludes senior author Natalia Trayanova, the Murray
B. Sachs Professor of Biomedical Engineering and Medicine at Johns
Hopkins University. "Likewise, it could also ensure that patients
who are unlikely to benefit are spared the risks of implantation." ========================================================================== Story Source: Materials provided by eLife. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Ryan P O'Hara, Edem Binka, Adityo Prakosa, Stefan L Zimmerman,
Mark J
Cartoski, M Roselle Abraham, Dai-Yin Lu, Patrick M Boyle,
Natalia A Trayanova. Personalized computational heart models
with T1-mapped fibrotic remodeling predict sudden death risk in
patients with hypertrophic cardiomyopathy. eLife, 2022; 11 DOI:
10.7554/eLife.73325 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220125112604.htm

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