Honorary VPH Lecture
Natalia Trayanova. Computational Cardiology
Wednesday June 20th 2018 - 18:00h - 19:30h
Come to the auditorium UPF Poble Nou Campus (Roc Boronat 138, Barcelona)
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Natalia Trayanova (Johns Hopkins University). Computational Cardiology
Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter-defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. There is a critical clinical need to develop risk metrics that directly assess the interplay between abnormal myocardial structure and electrical instability in the heart, that together predispose to SCD. Here we present a novel non-invasive personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modeling. This is an example of the emerging field of computational cardiology.
In computational cardiology, we construct personalized 3D computer models of post-infarction hearts from patients’ clinical magnetic resonance imaging data. Each heart model incorporates not only myocardial structure, but electrophysiological functions from the sub-cellular to the organ, allowing for representation of electrical instability. Thus the interplay between abnormal myocardial structure and electrical instability in the heart that predisposes to SCD can be directly assessed. In each heart model, we conduct a virtual multi-site delivery of electrical stimuli from ventricular locations at different distances to remodeled tissue so that the patient’s heart propensity to develop infarct-related ventricular arrhythmias can be comprehensively evaluated. Simulations are conducted for each virtual heart, probing its propensity to develop infarct-related ventricular arrhythmia. We term this non-invasive SCD risk assessment approach VARP, virtual-heart arrhythmia risk predictor.
In a proof-of-concept retrospective study, we assessed the predictive capability of the VARP approach as compared to that of other clinical metrics in a cohort of 41 patients. Statistical analysis demonstrated that a positive VARP test was significantly associated with the primary endpoint, with a four-fold higher arrhythmia risk than patients with negative VARP test. Our results also demonstrate that VARP significantly outperformed clinical metrics in predicting future arrhythmic events.
The robust and non-invasive VARP approach has the potential to prevent SCD and eliminate unnecessary ICD implantations in post-infarction patients. Importantly, the methodology could be applied to patients with prior MI but preserved ejection fraction who could also be at significant risk for arrhythmia because of their remodeled myocardium, but are generally not targeted for therapy under current clinical recommendations. Finally, the same approach is easily extendable to other heart diseases.
Dr. Natalia Trayanova is the Murray B. Sachs Professor in the Department of Biomedical Engineering and the Institute for Computational Medicine, and directs the Computational Cardiology Laboratory at Johns Hopkins University. She is also a Professor in the Department of Medicine. In 2013, she received the NIH Director’s Pioneer Award for her project “Virtual Electrophysiology Laboratory”. Dr. Trayanova was also the inaugural William R. Brody Faculty Scholar at Johns Hopkins University. She is a Fellow of the Heart Rhythm Society, American Heart Association, Biomedical Engineering Society, and the American Institute for Medical and Biological Engineering.