New heart modeling method could help doctors curb sudden cardiac death

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A team led by Johns Hopkins engineers has found that modeling the heart in 3D using combined imaging techniques can help predict abnormal heart rhythms, called arrhythmias, in patients with heart disease genetic. This approach could one day help clinicians determine which patients with a condition called hypertrophic cardiomyopathy might benefit from the implantation of a defibrillator. The team’s study appears today in eLife.

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

“There are ways to predict which patients are at risk of developing cardiac arrhythmias and who might benefit from a defibrillator, but these methods are not accurate,” says study first author Ryan O’Hara, PhD candidate. in the lab of Natalia Trayanova, a professor in the Department of Biomedical Engineering. “We wanted to develop a more accurate and personalized approach to predict abnormal heart rhythms in patients with hypertrophic cardiomyopathy.”

Legend: Natalia Trayanova talks about her computer simulation approach to health at TEDxJHU 2017.

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 allowed the team to build digital and personalized replicas of patients’ hearts, including specific scarring and thickening features, and assess the likelihood that they would continue to develop cardiac arrhythmias.

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

“This is a substantial improvement over currently used prediction techniques, which are only accurate about half the time,” O’Hara said.

Their work also showed that cardiac diffuse fibrosis — lesions associated with progression to heart failure but rarely evaluated in patients with hypertrophic cardiomyopathy — can increase the likelihood that these patients will develop an abnormal heart rhythm.

Further studies are now needed to confirm whether this personalized modeling approach can be used to guide patient care before the technique can be adopted more widely.

“If our technology proves superior to existing prediction methods in larger studies, it could help ensure that patients at high risk of cardiac arrhythmia receive a defibrillator or other appropriate intervention,” concludes lead author Trayanova. . “Similarly, it could also ensure that patients who are unlikely to benefit are spared the risk of implantation.”

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