Hypertrophic cardiomyopathy (HCM) is the commonest inherited cardiovascular disorder occurring in 1 in 500 persons worldwide. It is also the commonest cause of sudden death in the young and is associated with heart failure, cardiac arrhythmias and death. It is likely that routinely captured clinical information pertaining to primary or secondary features of the disease, manifesting as changes in cardiac structure and/or function, may allow identification of patients at risk for these adverse outcomes. Our overall goal is to examine potential novel predictors of clinically important outcomes of HCM by leveraging the power of large numbers of well-characterized HCM population available in a conglomeration of existing research communities across the world. Accordingly, our specific aims are: 1) To determine the role of dyssynchrony in predicting heart failure in HCM;2) To ascertain the predictors of atrial fibrillation and 3) To determine the electrophysiologic predictors of syncope and sudden cardiac death. A comprehensive evaluation of these issues would ideally require large, well- characterized, systematically accrued, and preferably multi-ethnic populations of patients with HCM that have been followed longitudinally in a well-defined protocolized fashion. Although such research populations are organizationally and geographically dispersed with dissimilar scientific goals, almost all collect key clinical and imaging information that could help address our hypothesis. The challenges in bringing such entities together are manifold but primarily related to the fact that the volume and complexity of information does not lend itself to easy integration and analyses. Merging data from multiple research programs requires sophisticated and powerful computational and informatics tools that will allow collection, storage and analyses of disparate forms of information. This proposal seeks to collaboratively amalgamate several large, distinct research communities with populations of HCM patients to test our hypotheses exploiting the unique architecture and tools offered by the CVRG. We plan to use the CVRG expertise and resources to establish a data sharing infrastructure wherein participating investigators will securely, and in a HIPAA compliant fashion, upload large volumes of information on to a common platform. Subsequently, we will apply existing custom CVRG tools for management and analyses of electrophysiology and imaging data. We will use existing cross-sectional and longitudinal data to ascertain if parameters of electrophysiology, cardiac structure and function, either singly or in combination, predict clinically relevant outcomes in HCM patients.

Public Health Relevance

This project seeks to develop a clinical consortium of hypertrophic cardiomyopathy using the cardiovascular research grid with several participating centers from the United States, Europe and Asia. This consortium, a first of its kind, would allow pooling of clinical and imaging information thus creating a large database capable of answering critical clinical questions concerning the management of hypertrophic cardiomyopathy, a common genetic disorder, not otherwise possible with smaller, single institution volumes.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-F (50))
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Boineau, Robin
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Johns Hopkins University
Internal Medicine/Medicine
Schools of Medicine
United States
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Sorensen, Lars Lindholm; Liang, Hsin-Yueh; Pinheiro, Aurelio et al. (2017) Safety profile and utility of treadmill exercise in patients with high-gradient hypertrophic cardiomyopathy. Am Heart J 184:47-54
Lin, Xiaoping; Steinberg, Steven; Kandasamy, Suresh K et al. (2016) Common miR-590 Variant rs6971711 Present Only in African Americans Reduces miR-590 Biogenesis. PLoS One 11:e0156065
Bravo, Paco E; Luo, Hong-Chang; Pozios, Iraklis et al. (2016) Late gadolinium enhancement confined to the right ventricular insertion points in hypertrophic cardiomyopathy: an intermediate stage phenotype? Eur Heart J Cardiovasc Imaging 17:293-300
Ardekani, Siamak; Jain, Saurabh; Sanzi, Alianna et al. (2016) Shape analysis of hypertrophic and hypertensive heart disease using MRI-based 3D surface models of left ventricular geometry. Med Image Anal 29:12-23
Sorensen, Lars L; Pinheiro, Aurelio; Dimaano, Veronica Lea et al. (2016) Comparison of Clinical Features in Blacks Versus Whites With Hypertrophic Cardiomyopathy. Am J Cardiol 117:1815-20
Rahman, Quazi Abidur; Tereshchenko, Larisa G; Kongkatong, Matthew et al. (2015) Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification. IEEE Trans Nanobioscience 14:505-12
Pozios, Iraklis; Corona-Villalobos, Celia; Sorensen, Lars L et al. (2015) Comparison of Outcomes in Patients With Nonobstructive, Labile-Obstructive, and Chronically Obstructive Hypertrophic Cardiomyopathy. Am J Cardiol 116:938-44
Canepa, Marco; Malti, Omar; David, Melissa et al. (2014) Prevalence, clinical correlates, and functional impact of subaortic ventricular septal bulge (from the Baltimore Longitudinal Study of Aging). Am J Cardiol 114:796-802
Canepa, Marco; Sorensen, Lars L; Pozios, Iraklis et al. (2013) Comparison of clinical presentation, left ventricular morphology, hemodynamics, and exercise tolerance in obese versus nonobese patients with hypertrophic cardiomyopathy. Am J Cardiol 112:1182-9
Lin, Xiaoping; Liang, Hsin-Yueh; Pinheiro, Aurelio et al. (2013) Electromechanical relationship in hypertrophic cardiomyopathy. J Cardiovasc Transl Res 6:604-15

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