This proposal is in response to PAR-08-023 "Predictive Models of the Heart in Health and Disease". Heart failure is a major cause of morbidity and mortality, contributing significantly to global health expenditure. Heart failure patients often exhibit contractile dyssynchrony, which diminishes cardiac systolic function. Cardiac resynchronization therapy (CRT) employs bi-ventricular pacing to re-coordinate the contraction of the heart. CRT has been shown to improve heart failure symptoms and reduce hospitalization, yet approximately 30% of patients fail to respond to the therapy. The poor predictive ability of current approaches to identify potential responders to CRT reflects the incomplete understanding of the complex pathophysiologic and electromechanical factors that underlie mechanical dyssynchrony. Specifically, given that a large portion of CRT non-responders are heart failure patients with chronic myocardial infarction (MI), it is of paramount importance to the improvement in CRT effectiveness that the contribution of chronic MI to dyssynchronous heart failure (DHF) is identified, and the mechanisms by which it limits CRT benefit thoroughly explored. The present application addresses this need. The overall objective of this research is to elucidate the role of chronic MI in heart failure dyssyn- chrony and its effect on CRT effectiveness. To achieve the objective of the proposed research, we will de- flop, from magnetic resonance imaging (MRI) and diffusion tensor MRI scans, individualized 3D image-based multiscale computational models of ventricular electromechanics in canine hearts that incorporate the deleterious- ous structural, mechanical, and electrophysiological remodeling associated with DHF and chronic MI, from the level of the molecule to that of the intact heart. This powerful predictive modeling approach will then be used 1) to provide mechanistic insight into the contribution of the infarct location and of the degree of transmural scar extent to left ventricular heart failure contractile dyssynchrony, and 2) to determine the optimal CRT strategy. The development of a validated predictive model of ventricular electromechanics in the setting of DHF and chronic MI (DHF+MI heart model), as proposed in this application, overcomes the inability of current experimental techniques to simultaneously record the 3D electrical and mechanical activity of the heart with high spatiotemporal resolution, and thus to provide an understanding of the contribution of chronic MI to heart failure dyssynchrony and CRT effectiveness. The new basic-science insights into the electromechanical behavior in the DHF+MI heart to be acquired under this study are expected to ultimately lead to rational optimization of CRT delivery in patients with ischemic cardiomyopathy and to improvements in the selection criteria for viable CRT candidates.

Public Health Relevance

Cardiac resynchronization therapy (CRT) employs bi-ventricular pacing to re-coordinate the contraction of the heart, yet approximately 30% of patients fail to respond to the therapy. In the current environment which emphasizes reducing health care costs and optimizing therapy, robust diagnostic approaches to identify patients that would benefit from CRT and distinguish those who could not, would have a dramatic personal, medical and economic impact. The proposed project offers mechanistic insight into heart failure dissynchrony and CRT that can contribute to the development of such approaches.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Larkin, Jennie E
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Johns Hopkins University
Biostatistics & Other Math Sci
Schools of Engineering
United States
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