I propose to develop a highly innovative patient-specific MRI-based heart modeling environment that represents cardiac functions from molecular processes to electrophysiological and electromechanical interactions at the organ level. I term this environment virtual electrophysiology lab, and propose to translate it into the clinic and apply it to the non-invasive diagnosis and treatment of heart rhythm and contractile disorders in patients with structural heart disease. This pioneering effort offers to integrate, for the first time, computational modeling of the heart, traditionally a basic-science discipline, within the milieu of contemporary patient care. The robust and inexpensive non-invasive approaches for individualized arrhythmia risk stratification and guidance of electrophysiological therapies proposed here will lead to optimized therapy delivery and reduction in health care costs, and will have a dramatic personal, medical and economic impact on society. This project seeks to shift the paradigm of cardiac patient care by utilizing the virtual electrophysiology laboratory environment in three applications pertinent to patients with myocardial infarction: 1. Noninvasive prediction of the optimal ablation targets for infarct-related ventricular tachycardia. The vital electrophysiology lab will be used to accurately identify the optimal targets of ablation in each patient heart non-invasively prior to the clinical procedure. Delivery of ablation will then be swift and precise, eradicating all infarct-related ventricular tachycardias with minimum lesion sizes. This will result in a dramatic improvement in the efficacy of and tolerance for the therapy, as well as in reduction of post-procedure complications. 2. Arrhythmia risk assessment to determine the need for implantable defibrillator deployment. Personalized simulations of arrhythmia inducibility will be used as a noninvasive, inexpensive, and risk-free surrogate for a clinical electrophysiological study to predict lethal arrhythmia risk in patients with myocardial infarction and low left ventricular ejection fraction. The results of the in-silico ventricular arrhythmia inducibility tests will be combined into a new index and used to stratify risk of arrhythmias, thus radically changing the process of patient selection for defibrillator device implantation. 3. Determining the optimal left ventricular pacing location(s) for cardiac resynchronization therapy. The virtual electrophysiology lab approach will be used to predict, in patients with myocardial infarction, the optimal individualized location of the LV pacing lead for CRT. Successful execution of the studies will eliminate the one size fits all approach to LV CRT pacing and thus transform resynchronization therapy.

Public Health Relevance

This research offers to integrate computational modeling of the heart within the milieu of contemporary patient care by developing a highly innovative patient-specific MRI-based cardiac electrophysiological modeling environment. The robust and inexpensive non-invasive approaches for individualized arrhythmia risk stratification and guidance of electrophysiological therapies proposed here will lead to optimized therapy delivery and reduction in health care costs, and will have a dramatic personal, medical and economic impact on society.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
NIH Director’s Pioneer Award (NDPA) (DP1)
Project #
5DP1HL123271-02
Application #
8740550
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Lee, Albert
Project Start
2013-09-25
Project End
2018-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Baltimore
State
MD
Country
United States
Zip Code
21218
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Zile, Melanie A; Trayanova, Natalia A (2017) Myofilament protein dynamics modulate EAD formation in human hypertrophic cardiomyopathy. Prog Biophys Mol Biol 130:418-428

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