全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Computational Cardiology: The Heart of the Matter

DOI: 10.5402/2012/269680

Full-Text   Cite this paper   Add to My Lib

Abstract:

This paper reviews the newest developments in computational cardiology. It focuses on the contribution of cardiac modeling to the development of new therapies as well as the advancement of existing ones for cardiac arrhythmias and pump dysfunction. Reviewed are cardiac modeling efforts aimed at advancing and optimizing existent therapies for cardiac disease (defibrillation, ablation of ventricular tachycardia, and cardiac resynchronization therapy) and at suggesting novel treatments, including novel molecular targets, as well as efforts to use cardiac models in stratification of patients likely to benefit from a given therapy, and the use of models in diagnostic procedures. 1. Introduction The iterative interaction between experimentation and simulation has long played a central role in the advancement of biological sciences. Among computational models of the various physiological systems, the heart is the most highly advanced example of a virtual organ, capable of integrating data at multiple scales, from genes to the whole organ [1]. State-of-the-art whole-heart models of electrophysiology and electromechanics are currently being used to study a wide range of mechanisms in the workings of the normal and the diseased heart [2, 3]. The focus of this paper is on the contribution of heart computational models to the treatment of the diseased heart, that is, on the computational medicine aspect of cardiac modeling applications. Reviewed below are cardiac modeling efforts aimed at advancing and optimizing existent therapies for cardiac disease and at suggesting novel treatments, including novel molecular targets, as well as efforts to use cardiac models in stratification of patients likely to benefit from a given therapy, and the use of models in diagnostic procedures 2. Modeling Ventricular Arrhythmias: From Mechanisms to the Clinic Modeling arrhythmias in the whole heart to reveal mechanisms and suggest better treatments has become one of the most important hallmarks in the utilization of biophysically detailed computational modeling of the heart. A number of ventricular models have focused on arrhythmia dynamics, and specifically on the self-sustained reentrant propagation of complex 3D waves in the ventricles. Historically, these were the first applications of ventricular modeling. Ventricular modeling studies have revealed important aspects of reentrant arrhythmias, among which the dynamic characteristics of ventricular fibrillation (VF), and the role of alternans and restitution in arrhythmogenesis. Ventricular models have been used extensively in

References

[1]  D. Noble, “Modeling the heart—from genes to cells to the whole organ,” Science, vol. 295, no. 5560, pp. 1678–1682, 2002.
[2]  N. A. Trayanova, “Whole-heart modeling : applications to cardiac electrophysiology and electromechanics,” Circulation Research, vol. 108, no. 1, pp. 113–128, 2011.
[3]  E. Vigmond, F. Vadakkumpadan, V. Gurev et al., “Towards predictive modelling of the electrophysiology of the heart,” Experimental Physiology, vol. 94, no. 5, pp. 563–577, 2009.
[4]  R. H. Clayton, “Vortex filament dynamics in computational models of ventricular fibrillation in the heart,” Chaos, vol. 18, no. 4, Article ID 043127, 12 pages, 2008.
[5]  K. H. W. J. ten Tusscher, R. Hren, and A. V. Panfilov, “Organization of ventricular fibrillation in the human heart,” Circulation Research, vol. 100, no. 12, pp. e87–e101, 2007.
[6]  A. Garfinkel, Y. H. Kim, O. Voroshilovsky et al., “Preventing ventricular fibrillation by flattening cardiac restitution,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 11, pp. 6061–6066, 2000.
[7]  O. Bernus, B. van Eyck, H. Verschelde, and A. V. Panfilov, “Transition from ventricular fibrillation to ventricular tachycardia: a simulation study on the role of Ca2+-channel blockers in human ventricular tissue,” Physics in Medicine and Biology, vol. 47, no. 23, pp. 4167–4179, 2002.
[8]  B. Echebarria and A. Karma, “Mechanisms for initiation of cardiac discordant alternans,” European Physical Journal, vol. 146, no. 1, pp. 217–231, 2007.
[9]  E. M. Cherry and F. H. Fenton, “Suppression of alternans and conduction blocks despite steep APD restitution: electrotonic, memory, and conduction velocity restitution effects,” American Journal of Physiology, vol. 286, no. 6, pp. H2332–H2341, 2004.
[10]  R. H. Keldermann, K. H. W. J. ten Tusscher, M. P. Nash et al., “A computational study of mother rotor VF in the human ventricles,” American Journal of Physiology, vol. 296, no. 2, pp. H370–H379, 2009.
[11]  R. H. Keldermann, K. H. W. J. ten Tusscher, M. P. Nash, R. Hren, P. Taggart, and A. V. Panfilov, “Effect of heterogeneous APD restitution on VF organization in a model of the human ventricles,” American Journal of Physiology, vol. 294, no. 2, pp. H764–H774, 2008.
[12]  K. S. McDowell, H. J. Arevalo, M. M. Maleckar, and N. A. Trayanova, “Susceptibility to arrhythmia in the infarcted heart depends on myofibroblast density,” Biophysical Journal, vol. 101, no. 6, pp. 1307–1315, 2011.
[13]  R. Bordas, K. Gillow, Q. Lou et al., “Rabbit-specific ventricular model of cardiac electrophysiological function including specialized conduction system,” Progress in Biophysics and Molecular Biology, vol. 107, no. 1, pp. 90–100, 2011.
[14]  M. Deo, P. M. Boyle, A. M. Kim, and E. J. Vigmond, “Arrhythmogenesis by single ectopic beats originating in the Purkinje system,” American Journal of Physiology, vol. 299, no. 4, pp. H1002–H1011, 2010.
[15]  X. Jie and N. A. Trayanova, “Mechanisms for initiation of reentry in acute regional ischemia phase 1B,” Heart Rhythm, vol. 7, no. 3, pp. 379–386, 2010.
[16]  X. Jie, B. Rodríguez, J. R. de Groot, R. Coronel, and N. Trayanova, “Reentry in survived subepicardium coupled to depolarized and inexcitable midmyocardium: insights into arrhythmogenesis in ischemia phase 1B,” Heart Rhythm, vol. 5, no. 7, pp. 1036–1044, 2008.
[17]  X. Jie, V. Gurev, and N. Trayanova, “Mechanisms of mechanically induced spontaneous arrhythmias in acute regional ischemia,” Circulation Research, vol. 106, no. 1, pp. 185–192, 2010.
[18]  F. Vadakkumpadan, H. Arevalo, A. J. Prassl et al., “Image-based models of cardiac structure in health and disease,” Wiley Interdisciplinary Reviews, vol. 2, no. 4, pp. 489–506, 2010.
[19]  M. J. Bishop, G. Plank, R. A. B. Burton et al., “Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function,” American Journal of Physiology, vol. 298, no. 2, pp. H699–H718, 2010.
[20]  W. G. Stevenson, P. Brugada, and B. Waldecker, “Clinical, angiographic, and electrophysiologic findings in patients with aborted sudden death as compared with patients with sustained ventricular tachycardia after myocardial infarction,” Circulation, vol. 71, no. 6, pp. 1146–1152, 1985.
[21]  M. Pop, M. Sermesant, T. Mansi, E. Crystal, S. Ghate, J. Peyrat, et al., “Correspondence between simple 3-D MRI-based computer models and in-vivo EP measurements in swine with chronic infarctions,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 12, pp. 3483–3486, 2011.
[22]  H. Arevalo, G. Plank, P. Helm, H. Halperin, and N. Trayanova, “Volume of peri-infarct zone determines arrhythmogenesis in infarcted heart,” Heart Rhythm, vol. 6, no. 5, pp. S232–S233, 2009.
[23]  H. Arevalo, H. Estner, C. Park, H. Halperin, and N. Trayanova, “In-vivo MRI-based models of infarct- related ventricular tachycardia successfully predict optimal ablation site,” Heart Rhythm, vol. 9, no. 5, p. S181, 2012.
[24]  H. Ashikaga, H. Arevalo, F. Vadakkumpadan, R. Blake, R. Berger, H. Calkins, et al., “MRI-based patient-specific virtual electrophysiology laboratory for scar-related ventricular tachycardia,” Circulation, vol. 124, p. A541, 2011.
[25]  J. Ng, J. T. Jacobson, J. K. Ng, D. Gordon, D. C. Lee, J. C. Carr, et al., “Virtual electrophysiological study in a 3-dimensional cardiac magnetic resonance imaging model of porcine myocardial infarction,” Journal of the American College of Cardiology, vol. 60, no. 5, pp. 423–430, 2012.
[26]  J. Relan, P. Chinchapatnam, M. Sermesant, K. Rhode, M. Ginks, H. Delingette, et al., “Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia,” Interface Focus, vol. 1, no. 3, pp. 396–407, 2011.
[27]  F. Vadakkumpadan, H. Arevalo, C. Ceritoglu, M. Miller, and N. Trayanova, “Image-based estimation of ventricular fiber orientations for personalized modeling of cardiac electrophysiology,” IEEE Transactions on Medical Imaging, vol. 31, no. 5, pp. 1051–1060, 2012.
[28]  J. D. Bayer, R. C. Blake, G. Plank, and N. A. Trayanova, “A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models,” Annals of Biomedical Engineering, vol. 40, no. 10, pp. 2243–2254, 2012.
[29]  R. L. Winslow, N. Trayanova, D. Geman, and MI. Miller, “Computational medicine: translating models to clinical care,” Science Translational Medicine, vol. 4, no. 158, p. 158rv11, 2012.
[30]  N. Virag, V. Jacquemet, C. S. Henriquez et al., “Study of atrial arrhythmias in a computer model based on magnetic resonance images of human atria,” Chaos, vol. 12, no. 3, pp. 754–763, 2002.
[31]  E. S. Di Martino, C. Bellini, and D. S. Schwartzman, “In vivo porcine left atrial wall stress: computational model,” Journal of Biomechanics, vol. 44, no. 15, pp. 2589–2594, 2011.
[32]  G. Seemann, C. H?per, F. B. Sachse, O. D?ssel, A. V. Holden, and H. Zhang, “Heterogeneous three-dimensional anatomical and electrophysiological model of human atria,” Philosophical Transactions of the Royal Society A, vol. 364, no. 1843, pp. 1465–1481, 2006.
[33]  J. Freudenberg, T. Schiemann, U. Tiede, and K. H. H?hne, “Simulation of cardiac excitation patterns in a three-dimensional anatomical heart atlas,” Computers in Biology and Medicine, vol. 30, no. 4, pp. 191–205, 2000.
[34]  V. M. Spitzer and D. G. Whitlock, “The visible human dataset: the anatomical platform for human simulation,” The Anatomical Record, vol. 253, no. 2, pp. 49–57, 1998.
[35]  S. Kharche, C. J. Garratt, M. R. Boyett et al., “Atrial proarrhythmia due to increased inward rectifier current (IK1) arising from KCNJ2 mutation—a simulation study,” Progress in Biophysics and Molecular Biology, vol. 98, no. 2-3, pp. 186–197, 2008.
[36]  M. E. Ridler, M. Lee, D. McQueen, C. Peskin, and E. Vigmond, “Arrhythmogenic consequences of action potential duration gradients in the atria,” Canadian Journal of Cardiology, vol. 27, no. 1, pp. 112–119, 2011.
[37]  J. Kneller, R. Q. Zou, E. J. Vigmond, Z. G. Wang, L. J. Leon, and S. Nattel, “Cholinergic atrial fibrillation in a computer model of a two-dimensional sheet of canine atrial cells with realistic ionic properties,” Circulation Research, vol. 90, no. 9, pp. E73–E87, 2002.
[38]  V. Jacquemet, “Pacemaker activity resulting from the coupling with nonexcitable cells,” Physical Review E, vol. 74, no. 1, part 1, Article ID 011908, 2006.
[39]  N. Kuijpers, H. ten Eikelder, and S. Verheule, “Atrial anatomy influences onset and termination of atrial fibrillation: a computer model study,” in Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (FIMH '09), vol. 5528 of Lecture Notes in Computer Science, pp. 285–294, Nice, France, June 2009.
[40]  T. Krogh-Madsen, G. W. Abbott, and D. J. Christini, “Effects of electrical and structural remodeling on atrial fibrillation maintenance: a simulation study,” PLOS Computational Biology, vol. 8, no. 2, Article ID e1002390, 2012.
[41]  E. J. Vigmond, N. A. Trayanova, and R. A. Malkin, “Excitation of a cardiac muscle fiber by extracellularly applied sinusoidal current,” Journal of Cardiovascular Electrophysiology, vol. 12, no. 10, pp. 1145–1153, 2001.
[42]  E. J. Vigmond, V. Tsoi, S. Kuo et al., “The effect of vagally induced dispersion of action potential duration on atrial arrhythmogenesis,” Heart Rhythm, vol. 1, no. 3, pp. 334–344, 2004.
[43]  M. Rotter, L. Dang, V. Jacquemet, N. Virag, L. Kappenberger, and M. Ha?ssaguerre, “Impact of varying ablation patterns in a simulation model of persistent atrial fibrillation,” Pacing and Clinical Electrophysiology, vol. 30, no. 3, pp. 314–321, 2007.
[44]  P. Ruchat, L. Dang, J. Schlaepfer, N. Virag, L. K. von Segesser, and L. Kappenberger, “Use of a biophysical model of atrial fibrillation in the interpretation of the outcome of surgical ablation procedures,” European Journal of Cardio-Thoracic Surgery, vol. 32, no. 1, pp. 90–95, 2007.
[45]  P. Ruchat, L. Dang, N. Virag, J. Schlaepfer, L. K. von Segesser, and L. Kappenberger, “A biophysical model of atrial fibrillation to define the appropriate ablation pattern in modified maze,” European Journal of Cardio-Thoracic Surgery, vol. 31, no. 1, pp. 65–69, 2007.
[46]  V. Jacquemet, A. van Oosterom, J. M. Vesin, and L. Kappenberger, “Analysis of electrocardiograms during atrial fibrillation,” IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 6, pp. 79–88, 2006.
[47]  E. J. Vigmond and L. J. Leon, “Electrophysiological basis of mono-phasic action potential recordings,” Medical and Biological Engineering and Computing, vol. 37, no. 3, pp. 359–365, 1999.
[48]  E. J. Vigmond, V. Tsoi, Y. Yin, P. Pagé, and A. Vinet, “Estimating atrial action potential duration from electrograms,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 5, pp. 1546–1555, 2009.
[49]  V. Jacquemet and C. S. Henriquez, “Genesis of complex fractionated atrial electrograms in zones of slow conduction: a computer model of microfibrosis,” Heart Rhythm, vol. 6, no. 6, pp. 803–810, 2009.
[50]  M. W. Krueger, S. Severi, K. Rhode et al., “Alterations of atrial electrophysiology related to hemodialysis session: insights from a multiscale computer model,” Journal of Electrocardiology, vol. 44, no. 2, pp. 176–183, 2011.
[51]  K. M. Lim, S. B. Hong, J. W. Jeon, M. S. Gyung, B. H. Ko, S. K. Bae, et al., “Predicting the optimal position and direction of a ubiquitous ECG using a multi-scale model of cardiac electrophysiology,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '11), pp. 993–996, Boston, Mass, USA, September 2011.
[52]  R. MacLeod, J. Blauer, E. Kholmovski, R. Ranjan, N. Marrouche, N. Trayanova, et al., “Subject specific, image based analysis and modeling in patients with atrial fibrillation from MRI,” in Proceedings of the 9th IEEE International Symposium on Biomedical Imaging (ISBI '12), ISBI Meeting Proceedings, p. 1364, Barcelona, Spain, May 2012.
[53]  K. S. McDowell, F. Vadakkumpadan, R. C. Blake, J. Blauerb, G. Plank, R. S. MacLeod, et al., “Methodology for patient-specific modeling of atrial fibrosis as a substrate for atrial fibrillation,” Journal of Electrocardiology, vol. 45, no. 6, pp. 640–645, 2012.
[54]  J. Constantino, Y. Hu, and N. A. Trayanova, “A computational approach to understanding the cardiac electromechanical activation sequence in the normal and failing heart, with translation to the clinical practice of CRT,” Progress in Biophysics and Molecular Biology, vol. 110, no. 2-3, pp. 372–379, 2012.
[55]  V. Gurev, T. Lee, J. Constantino, H. Arevalo, and N. A. Trayanova, “Models of cardiac electromechanics based on individual hearts imaging data: image-based electromechanical models of the heart,” Biomechanics and Modeling in Mechanobiology, vol. 10, no. 3, pp. 295–306, 2011.
[56]  R. C. P. Kerckhoffs, A. D. McCulloch, J. H. Omens, and L. J. Mulligan, “Effect of pacing site and infarct location on regional mechanics and global hemodynamics in a model based study of heart failure,” in Proceedings of the 4th International Conference on Functional Imaging and Modeling of the Heart (FIMH '07), vol. 4466 of Lecture Notes in Computer Science, pp. 350–360, June 2007.
[57]  R. C. P. Kerckhoffs, A. D. McCulloch, J. H. Omens, and L. J. Mulligan, “Effects of biventricular pacing and scar size in a computational model of the failing heart with left bundle branch block,” Medical Image Analysis, vol. 13, no. 2, pp. 362–369, 2009.
[58]  S. A. Niederer, G. Plank, P. Chinchapatnam et al., “Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy,” Cardiovascular Research, vol. 89, no. 2, pp. 336–343, 2011.
[59]  R. C. P. Kerckhoffs, J. Lumens, K. Vernooy et al., “Cardiac resynchronization: insight from experimental and computational models,” Progress in Biophysics and Molecular Biology, vol. 97, no. 2-3, pp. 543–561, 2008.
[60]  S. A. Niederer, A. K. Shetty, G. Plank, J. Bostock, R. Razavi, N. P. Smith, et al., “Biophysical modeling to simulate the response to multisite left ventricular stimulation using a quadripolar pacing lead,” Pacing and Clinical Electrophysiology, vol. 35, no. 2, pp. 204–214, 2011.
[61]  S. A. Niederer, A. K. Shetty, G. Plank, J. Bostock, R. Razavi, N. P. Smith, et al., “Biophysical modeling to simulate the response to multisite left ventricular stimulation using a quadripolar pacing lead,” Pacing and Clinical Electrophysiology, vol. 35, no. 2, pp. 204–214, 2012.
[62]  J. Aguado-Sierra, A. Krishnamurthy, C. Villongco, J. Chuang, E. Howard, M. J. Gonzales, et al., “Patient-specific modeling of dyssynchronous heart failure: a case study,” Progress in Biophysics and Molecular Biology, vol. 107, no. 1, pp. 147–155, 2011.
[63]  M. Sermesant, R. Chabiniok, P. Chinchapatnam, T. Mansi, F. Billet, P. Moireau, et al., “Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation,” Medical Image Analysis, vol. 16, no. 1, pp. 201–215, 2012.
[64]  P. Lamata, S. Niederer, D. Nordsletten et al., “An accurate, fast and robust method to generate patient-specific cubic Hermite meshes,” Medical Image Analysis, vol. 15, no. 6, pp. 801–813, 2011.
[65]  J. Constantino, Y. Long, T. Ashihara, and N. A. Trayanova, “Tunnel propagation following defibrillation with ICD shocks: hidden postshock activations in the left ventricular wall underlie isoelectric window,” Heart Rhythm, vol. 7, no. 7, pp. 953–961, 2010.
[66]  J. D. Moreno, Z. I. Zhu, P. C. Yang, J. R. Bankston, M. T. Jeng, C. Kang, et al., “A computational model to predict the effects of class I anti-arrhythmic drugs on ventricular rhythms,” Science Translational Medicine, vol. 3, no. 98, p. 98ra83, 2011.
[67]  C. Anderson and N. A. Trayanova, “Success and failure of biphasic shocks: results of bidomain simulations,” Mathematical Biosciences, vol. 174, no. 2, pp. 91–109, 2001.
[68]  H. Arevalo, B. Rodriguez, and N. Trayanova, “Arrhythmogenesis in the heart: multiscale modeling of the effects of defibrillation shocks and the role of electrophysiological heterogeneity,” Chaos, vol. 17, no. 1, Article ID 015103, 13 pages, 2007.
[69]  T. Ashihara and N. A. Trayanova, “Asymmetry in membrane responses to electric shocks: insights from bidomain simulations,” Biophysical Journal, vol. 87, no. 4, pp. 2271–2282, 2004.
[70]  D. W. Bourn, R. A. Gray, and N. A. Trayanova, “Characterization of the relationship between preshock state and virtual electrode polarization-induced propagated graded responses resulting in arrhythmia induction,” Heart Rhythm, vol. 3, no. 5, pp. 583–595, 2006.
[71]  E. Entcheva, N. A. Trayanova, and F. J. Claydon, “Patterns of and mechanisms for shock-induced polarization in the heart: a bidomain analysis,” IEEE Transactions on Biomedical Engineering, vol. 46, no. 3, pp. 260–270, 1999.
[72]  A. E. Lindblom, B. J. Roth, and N. A. Trayanova, “Role of virtual electrodes in arrhythmogenesis: pinwheel experiment revisited,” Journal of Cardiovascular Electrophysiology, vol. 11, no. 3, pp. 274–285, 2000.
[73]  B. Rodríguez, J. C. Eason, and N. Trayanova, “Differences between left and right ventricular anatomy determine the types of reentrant circuits induced by an external electric shock. A rabbit heart simulation study,” Progress in Biophysics and Molecular Biology, vol. 90, no. 1–3, pp. 399–413, 2006.
[74]  B. Rodríguez, L. Li, J. C. Eason, I. R. Efimov, and N. A. Trayanova, “Differences between left and right ventricular chamber geometry affect cardiac vulnerability to electric shocks,” Circulation Research, vol. 97, no. 2, pp. 168–175, 2005.
[75]  N. Trayanova, K. Skouibine, and P. Moore, “Virtual electrode effects in defibrillation,” Progress in Biophysics and Molecular Biology, vol. 69, no. 2-3, pp. 387–403, 1998.
[76]  N. Trayanova, J. Constantino, T. Ashihara, and G. Plank, “Modeling defibrillation of the heart: approaches and insights,” IEEE Reviews in Biomedical Engineering, vol. 4, pp. 89–102, 2011.
[77]  T. Ashihara, J. Constantino, and N. A. Trayanova, “Tunnel propagation of postshock activations as a hypothesis for fibrillation induction and isoelectric window,” Circulation Research, vol. 102, no. 6, pp. 737–745, 2008.
[78]  B. Rodríguez, B. Tice, R. Blake, D. Gavaghan, and N. Trayanova, “Vulnerability to electric shocks in the regionally-ischemic ventricles,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06), vol. 1, pp. 2280–2283, New York, NY, USA, September 2006.
[79]  B. Rodríguez, B. M. Tice, J. C. Eason, F. Aguel, J. M. Ferrero Jr., and N. Trayanova, “Effect of acute global ischemia on the upper limit of vulnerability: a simulation study,” American Journal of Physiology, vol. 286, no. 6, pp. H2078–H2088, 2004.
[80]  L. J. Rantner, H. J. Arevalo, J. L. Constantino, I. R. Efimov, G. Plank, and N. A. Trayanova, “Three-dimensional mechanisms of increased vulnerability to electric shocks in myocardial infarction: altered virtual electrode polarizations and conduction delay in the peri-infarct zone,” The Journal of Physiology, vol. 590, part 18, pp. 4537–4551, 2012.
[81]  N. Trayanova, V. Gurev, J. Constantino, and Y. Hu, “Mathematical models of ventricular mechano-electric coupling and arrhythmia,” in Cardiac Mechano-Electric Feedback and Arrhythmias, P. Kohl, F. Sachs, and M. R. Franz, Eds., pp. 258–268, 2011.
[82]  H. Tandri, S. H. Weinberg, K. C. Chang, R. Zhu, N. A. Trayanova, L. Tung, et al., “Reversible cardiac conduction block and defibrillation with high-frequency electric field,” Science Translational Medicine, vol. 3, no. 102, p. 102ra96, 2011.
[83]  P. J. Schwartz, S. G. Priori, C. Spazzolini et al., “Genotype-phenotype correlation in the long-QT syndrome: gene-specific triggers for life-threatening arrhythmias,” Circulation, vol. 103, no. 1, pp. 89–95, 2001.
[84]  M. Perry, F. B. Sachse, and M. C. Sanguinetti, “Structural basis of action for a human ether-a-go-go-related gene 1 potassium channel activator,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 34, pp. 13827–13832, 2007.
[85]  H. Sale, J. Wang, T. J. O'Hara et al., “Physiological properties of hERG 1a/1b heteromeric currents and a hERG 1b-specific mutation associated with long-QT syndrome,” Circulation Research, vol. 103, no. 7, pp. e81–e95, 2008.
[86]  P. S. Spector, M. E. Curran, M. T. Keating, and M. C. Sanguinetti, “Class III antiarrhythmic drugs block HERG, a human cardiac delayed rectifier K+ channel open-channel block by methanesulfonanilides,” Circulation Research, vol. 78, no. 3, pp. 499–503, 1996.
[87]  C. E. Clancy, Z. I. Zhu, and Y. Rudy, “Pharmacogenetics and anti-arrhythmic drug therapy: a theoretical investigation,” American Journal of Physiology, vol. 292, no. 1, pp. H66–H75, 2007.
[88]  V. V. Nesterenko, A. C. Zygmunt, S. Rajamani, L. Belardinelli, and C. Antzelevitch, “Mechanisms of atrial-selective block of Na channels by ranolazine: II. Insights from a mathematical model,” American Journal of Physiology, vol. 301, no. 4, pp. H1615–H1624, 2011.
[89]  C. Antzelevitch, L. Belardinelli, A. C. Zygmunt et al., “Electrophysiological effects of ranolazine, a novel antianginal agent with antiarrhythmic properties,” Circulation, vol. 110, no. 8, pp. 904–910, 2004.
[90]  A. Burashnikov, J. M. Di Diego, A. C. Zygmunt, L. Belardinelli, and C. Antzelevitch, “Atrium-selective sodium channel block as a strategy for suppression of atrial fibrillation: differences in sodium channel inactivation between atria and ventricles and the role of ranolazine,” Circulation, vol. 116, no. 13, pp. 1449–1457, 2007.
[91]  N. Morita, J. H. Lee, Y. Xie et al., “Suppression of re-entrant and multifocal ventricular fibrillation by the late sodium current blocker ranolazine,” Journal of the American College of Cardiology, vol. 57, no. 3, pp. 366–375, 2011.
[92]  B. Rodriguez, K. Burrage, D. Gavaghan, V. Grau, P. Kohl, and D. Noble, “The systems biology approach to drug development: application to toxicity assessment of cardiac drugs,” Clinical Pharmacology and Therapeutics, vol. 88, no. 1, pp. 130–134, 2010.
[93]  A. X. Sarkar and E. A. Sobie, “Regression analysis for constraining free parameters in electrophysiological models of cardiac cells,” PLoS Computational Biology, vol. 6, no. 9, Article ID e1000914, 2010.
[94]  D. M. Roden and T. Yang, “Protecting the heart against arrhythmias: potassium current physiology and repolarization reserve,” Circulation, vol. 112, no. 10, pp. 1376–1378, 2005.
[95]  T. O'Hara and Y. Rudy, “Quantitative comparison of cardiac ventricular myocyte electrophysiology and response to drugs in human and nonhuman species,” American Journal of Physiology, vol. 302, no. 5, pp. H1023–H1030, 2011.
[96]  H. Nakamura, J. Kurokawa, C. X. Bai et al., “Progesterone regulates cardiac repolarization through a nongenomic pathway: an in vitro patch-clamp and computational modeling study,” Circulation, vol. 116, no. 25, pp. 2913–2922, 2007.
[97]  P. C. Yang, J. Kurokawa, T. Furukawa, and C. E. Clancy, “Acute effects of sex steroid hormones on susceptibility to cardiac arrhythmias: a simulation study,” PLoS Computational Biology, vol. 6, no. 1, Article ID e1000658, 2010.
[98]  A. P. Benson, M. Al-Owais, and A. V. Holden, “Quantitative prediction of the arrhythmogenic effects of de novo hERG mutations in computational models of human ventricular tissues,” European Biophysics Journal, vol. 40, no. 5, pp. 627–639, 2011.
[99]  S. Ghosh, E. K. Rhee, J. N. Avari, P. K. Woodard, and Y. Rudy, “Cardiac memory in patients with Wolff-Parkinson-White syndrome: noninvasive imaging of activation and repolarization before and after catheter ablation,” Circulation, vol. 118, no. 9, pp. 907–915, 2008.
[100]  P. S. Cuculich, J. Zhang, Y. Wang, K. A. Desouza, R. Vijayakumar, P. K. Woodard, et al., “The electrophysiological cardiac ventricular substrate in patients after myocardial infarction: noninvasive characterization with electrocardiographic imaging,” Journal of the American College of Cardiology, vol. 58, no. 18, pp. 1893–1902, 2011.
[101]  S. Ghosh, J. N. A. Silva, R. M. Canham et al., “Electrophysiologic substrate and intraventricular left ventricular dyssynchrony in nonischemic heart failure patients undergoing cardiac resynchronization therapy,” Heart Rhythm, vol. 8, no. 5, pp. 692–699, 2011.
[102]  Y. Wang, P. S. Cuculich, J. Zhang, K. A. Desouza, R. Vijayakumar, J. Chen, et al., “Noninvasive electroanatomic mapping of human ventricular arrhythmias with electrocardiographic imaging,” Science Translational Medicine, vol. 3, no. 98, p. 98ra84, 2011.
[103]  P. S. Cuculich, Y. Wang, B. D. Lindsay et al., “Noninvasive characterization of epicardial activation in humans with diverse atrial fibrillation patterns,” Circulation, vol. 122, no. 14, pp. 1364–1372, 2010.
[104]  P. M. van Dam, T. F. Oostendorp, A. C. Linnenbank, and A. van Oosterom, “Non-invasive imaging of cardiac activation and recovery,” Annals of Biomedical Engineering, vol. 37, no. 9, pp. 1739–1756, 2009.
[105]  T. Berger, B. Pfeifer, F. F. Hanser et al., “Single-beat noninvasive imaging of ventricular endocardial and epicardial activation in patients undergoing CRT,” PLoS ONE, vol. 6, no. 1, Article ID e16255, 2011.
[106]  C. Han, C. Liu, S. Pogwizd, and B. He, “Noninvasive three-dimensional cardiac activation imaging on a rabbit model,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), pp. 3271–3273, September 2009.
[107]  T. Berger, G. Fischer, B. Pfeifer et al., “Single-beat noninvasive imaging of cardiac electrophysiology of ventricular pre-excitation,” Journal of the American College of Cardiology, vol. 48, no. 10, pp. 2045–2052, 2006.
[108]  A. V. Kalinin, “Iterative algorithm for the inverse problem of electrocardiography in a medium with piecewise-constant electrical conductivity,” Computational Mathematics and Modeling, vol. 22, no. 1, pp. 30–34, 2011.
[109]  D. Lai, C. Liu, M. D. Eggen, P. A. Iaizzo, and B. He, “Localization of endocardial ectopic activity by means of noninvasive endocardial surface current density reconstruction,” Physics in Medicine and Biology, vol. 56, no. 13, pp. 4161–4176, 2011.
[110]  C. Han, S. M. Pogwizd, C. R. Killingsworth, and B. He, “Noninvasive imaging of three-dimensional cardiac activation sequence during pacing and ventricular tachycardia,” Heart Rhythm, vol. 8, no. 8, pp. 1266–1272, 2011.
[111]  C. Han, S. M. Pogwizd, C. R. Killingsworth, and B. He, “Noninvasive reconstruction of the three-dimensional ventricular activation sequence during pacing and ventricular tachycardia in the canine heart,” American Journal of Physiology, vol. 302, no. 1, pp. H244–H252, 2012.
[112]  A. V. Kalinin, “Iterative algorithm for the inverse problem of electrocardiography in a medium with piecewise-constant electrical conductivity,” Computational Mathematics and Modeling, vol. 22, no. 1, pp. 30–34, 2011.
[113]  L. A. Bokeriia, A. S. Revishvili, A. V. Kalinin, V. V. Kalinin, O. A. Liadzhina, and E. A. Fetisova, “Hardware-software system for noninvasive electrocardiographic examination of heart based on inverse problem of electrocardiography,” Meditsinskaia Tekhnika, no. 6, pp. 1–7, 2008.
[114]  J. J. Goldberger, A. E. Buxton, M. Cain et al., “Risk stratification for arrhythmic sudden cardiac death: identifying the roadblocks,” Circulation, vol. 123, no. 21, pp. 2423–2430, 2011.
[115]  D. L. Kuchar, C. W. Thorburn, and N. L. Sammel, “Prediction of serious arrhythmic events after myocardial infarction: signal-averaged electrocardiogram, Holter monitoring and radionuclide ventriculography,” Journal of the American College of Cardiology, vol. 9, no. 3, pp. 531–538, 1987.
[116]  M. K. Das, B. Khan, S. Jacob, A. Kumar, and J. Mahenthiran, “Significance of a fragmented QRS complex versus a Q wave in patients with coronary artery disease,” Circulation, vol. 113, no. 21, pp. 2495–2501, 2006.
[117]  D. S. Rosenbaum, L. E. Jackson, J. M. Smith, H. Garan, J. N. Ruskin, and R. J. Cohen, “Electrical alternans and vulnerability to ventricular arrhythmias,” The New England Journal of Medicine, vol. 330, no. 4, pp. 235–241, 1994.
[118]  R. D. Berger, E. K. Kasper, K. L. Baughman, E. Marban, H. Calkins, and G. F. Tomaselli, “Beat-to-beat QT interval variability: novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy,” Circulation, vol. 96, no. 5, pp. 1557–1565, 1997.
[119]  J. P. Couderc, W. Zareba, S. McNitt, P. Maison-Blanche, and A. J. Moss, “Repolarization variability in the risk stratification of MADIT II patients,” Europace, vol. 9, no. 9, pp. 717–723, 2007.
[120]  S. M. Narayan, J. D. Bayer, G. Lalani, and N. A. Trayanova, “Action potential dynamics explain arrhythmic vulnerability in human heart failure. A clinical and modeling study implicating abnormal calcium handling,” Journal of the American College of Cardiology, vol. 52, no. 22, pp. 1782–1792, 2008.
[121]  J. D. Bayer, S. M. Narayan, G. G. Lalani, and N. A. Trayanova, “Rate-dependent action potential alternans in human heart failure implicates abnormal intracellular calcium handling,” Heart Rhythm, vol. 7, no. 8, pp. 1093–1101, 2010.
[122]  A. N. Doshi and S. F. Idriss, “Effect of resistive barrier location on the relationship between T-wave alternans and cellular repolarization alternans: a 1-D modeling study,” Journal of Electrocardiology, vol. 43, no. 6, pp. 566–571, 2010.
[123]  J. T. Zhao, A. P. Hill, A. Varghese et al., “Not all hERG pore domain mutations have a severe phenotype: G584S has an inactivation gating defect with mild phenotype compared to G572S, which has a dominant negative trafficking defect and a severe phenotype,” Journal of Cardiovascular Electrophysiology, vol. 20, no. 8, pp. 923–930, 2009.
[124]  C. Jons, J. O-Uchi, A. J. Moss, M. Reumann, J. J. Rice, I. Goldenberg, et al., “Use of mutant-specific ion channel characteristics for risk stratification of long QT syndrome patients,” Science Translational Medicine, vol. 3, no. 76, p. 76ra28, 2011.
[125]  T. O'Hara and Y. Rudy, “Arrhythmia formation in subclinical (“silent”) long QT syndrome requires multiple insults: quantitative mechanistic study using the KCNQ1 mutation Q357R as example,” Heart Rhythm, vol. 9, no. 2, pp. 275–282, 2012.
[126]  X. Chen, Y. Hu, B. J. Fetics, R. D. Berger, and N. A. Trayanova, “Unstable QT interval dynamics precedes ventricular tachycardia onset in patients with acute myocardial infarction: a novel approach to detect instability in QT interval dynamics from clinical ECG,” Circulation, vol. 4, no. 6, pp. 858–866, 2011.
[127]  S. M. Narayan, “T-wave alternans and the susceptibility to ventricular arrhythmias,” Journal of the American College of Cardiology, vol. 47, no. 2, pp. 269–281, 2006.
[128]  Z. Qu, Y. Xie, A. Garfinkel, and J. N. Weiss, “T-wave alternans and arrhythmogenesis in cardiac diseases,” Frontiers in Physiology, vol. 1, p. 154, 2010.
[129]  D. M. Bloomfield, J. T. Bigger, R. C. Steinman et al., “Microvolt T-wave alternans and the risk of death or sustained ventricular arrhythmias in patients with left ventricular dysfunction,” Journal of the American College of Cardiology, vol. 47, no. 2, pp. 456–463, 2006.
[130]  S. H. Hohnloser, T. Ikeda, and R. J. Cohen, “Evidence regarding clinical use of microvolt T-wave alternans,” Heart Rhythm, vol. 6, no. 3, supplement, pp. S36–S44, 2009.
[131]  J. N. Weiss, A. Karma, Y. Shiferaw, P. S. Chen, A. Garfinkel, and Z. Qu, “From pulsus to pulseless: the saga of cardiac alternans,” Circulation Research, vol. 98, no. 10, pp. 1244–1253, 2006.
[132]  J. M. Pastore, S. D. Girouard, K. R. Laurita, F. G. Akar, and D. S. Rosenbaum, “Mechanism linking T-wave alternans to the genesis of cardiac fibrillation,” Circulation, vol. 99, no. 10, pp. 1385–1394, 1999.
[133]  S. M. Narayan, M. R. Franz, G. Lalani, J. Kim, and A. Sastry, “T-wave alternans, restitution of human action potential duration, and outcome,” Journal of the American College of Cardiology, vol. 50, no. 25, pp. 2385–2392, 2007.
[134]  J. N. Weiss, M. Nivala, A. Garfinkel, and Z. Qu, “Alternans and arrhythmias : from cell to heart,” Circulation Research, vol. 108, no. 1, pp. 98–112, 2011.
[135]  F. M. Merchant and A. A. Armoundas, “Role of substrate and triggers in the genesis of cardiac alternans, from the myocyte to the whole heart: implications for therapy,” Circulation, vol. 125, no. 3, pp. 539–549, 2012.
[136]  X. Chen and N. A. Trayanova, “A novel methodology for assessing the bounded-input bounded-output instability in QT interval dynamics: application to clinical ECG with ventricular tachycardia,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 8, pp. 2111–2117, 2012.

Full-Text

Contact Us

[email protected]

QQ:3279437679

WhatsApp +8615387084133