Using Patient-Specific Modeling of the Heart for Risk Stratification of Ventricular Arrhythmia in Patients with Hypertrophic Cardiomyopathy (HCM) via Image-Based Computational Simulations

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Unmet Need
Hyperthrophic cardiomyopathy (HCM), predominantly an asymptomatic disease, frequently leads to sudden cardiac death resulting from ventricular arrhythmias, i.e. abnormal uncoordinated heart rhythms, particularly among young patients. For patients at high risk for sudden cardiac death, mortality is reduced by the prophylactic insertion of implantable cardioverter defibrillators (ICDs). Approaches to identify which HCM patients at risk for arrhythmia and thus need an ICD have been attempted with little success, and overall, there is no contemporary means for determining whether a patient with HCM is at risk for sudden cardiac death. Since ICD insertion carries risks—infections, device malfunctions and inappropriate shocks, inadequate sudden cardiac death risk assessment in HCM patients poses a public health and socioeconomic burden. Development of an accurate non-invasive means of arrhythmia risk stratification in HCM is an important unmet clinical need.

Technology Overview
Here, we develop a personalized approach to assess non-invasively risk of sudden cardiac death in HCM patients based on cardiac imaging and computational modeling. We construct personalized three-dimensional models of hearts from HCM patients’ clinical magnetic resonance imaging data (T1 and LGE-MRI) that incorporate the biology and physics of current flow in the structurally- and electrophysiologically- remodeled HCM heart, and use these “virtual hearts” to assess the propensity of each of them to develop arrhythmia.

Stage of Development
We are currently conducting a proof-of-concept retrospective study to ascertain the predictive capabilities the virtual heart test. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent sudden cardiac death in HCM patients and to avoid unnecessary ICD implantations.



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Jon Gottlieb
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