Auricchio, Angelo

Conte, Giulio

Gharaviri, Ali

Krause, Rolf

Pezzuto, Simone

Potse, Mark

Schotten, Ulrich

Verheule, Sander

Circulation Arrhythmia and Electrophysiology

ZORA

Left Atrial Appendage Electrical Isolation Reduces Atrial Fibrillation Recurrences: A Simulation Study

Supplemental Digital Content is available in the text

Gharaviri, A.

Pezzuto, S.

Potse, M.

Verheule, S.

Conte, G.

Krause, R.

Schotten, U.

Auricchio, A.

NARCIS 

Left Atrial Appendage Electrical Isolation Reduces Atrial Fibrillation Recurrences A Simulation Study

ERACoSysMed - Collaboration on systems medicine funding to promote the implementation of systems biology approaches in clinical research and medical practic

GHARAVIRI, Ali

PEZZUTO, Simone

POTSE, Mark

VERHEULE, Sander

CONTE, Giulio

KRAUSE, Rolf

SCHOTTEN, Ulrich

AURICCHIO, Angelo

Oskar Bordeaux

a simulation study

Maastricht University Research Portal

INRIA a CCSD electronic archive server

Supplemental Material for Left atrial appendage electrical 1 isolation reduces atrial fibrillation recurrences: a simulation 2 study 3  4 Ali Gharaviri, PhD; Simone Pezzuto, PhD; Mark Potse, PhD; Sander Verheule, PhD, Giulio 5 Conte, MD, PhD, Rolf Krause, PhD, Ulrich Schotten, MD, PhD, and 6 Angelo Auricchio, MD, PhD. 7  8 S1 Computational model of AF 9 A structurally detailed model of the human atria, extensively described previously1, was used 10 in this study. The global geometry was reconstructed using magnetic resonance image data of 11 a subject with normal atria. Additional anatomical structures and properties, such as wall 12 thickness heterogeneities, twenty pectinate muscles, Bachmann’s Bundle, interatrial bundles, 13 and the crista terminalis, and left atrial appendage trabeculated network were added 14 manually.2 One to three layers of fiber orientations manually included in the model based on 15 anatomical studies using a combination of manual editing and computer algorithms.1 These 16 structures are considered to play a key role in AF initiation and maintenance. 3, 4  17 Simulations were performed with a second-order accurate finite-difference method 5 on a 18 hexahedral mesh at 0.2mm resolution including of approximately 5 million nodes. 19 Differential equations for potentials and ion concentrations were integrated with the forward 20 Euler method and gating variables with the Rush-Larsen method6 using a time step of 21 0.01ms. The implementation of the boundary conditions is implicit in the formulation of 22 Saleheen and Ng.7. Simulations were performed with a monodomain reaction-diffusion 23 model using the propag-5 software8 and run on a Cray XC50 supercomputer with GPU 24 support. 25 The material properties for the atria were set to produce an approximately normal P wave in 26 case of sinus rhythm.2 The effective monodomain conductivities along and across the fiber 27 are given in Table S1. The tissue surface to volume ratio was 800 cm−1 throughout the atrial 28 myocardium. 29 Ionic currents for each node were described by the Courtemanche-Ramirez-Nattel model.9  30 Changes in ionic currents caused by AF were incorporated by setting the conductivities for 31 Ito, ICa,L, and IK1 at 40%, 35%, and 200% of their normal values, respectively.10  32 S2 Fibrosis distribution 33 Fibrosis patterns similar to those obtained from LGE-MRI were generated with an algorithm 34 that produces spatially-correlated, anatomy-tailored random fields (Figure S1).1, 11  35 The fibrosis was distributed within the atrial wall, while the endocardial bundles remain 36 intact. The spatial distribution of fibrosis was uneven, with patches or island presenting 37 higher degree of fibrosis. Such distribution was based on spatially correlated, anatomy-38 tailored random fields (Figure S1).1, 11, 12 Fibrotic elements were assumed to be electrically 39 active and conductive along the fiber direction and electrically isolated in the transverse 40 direction (Table S1). The fibrosis was modelled in this way to represent the loss of side-to-41 side electrical connections between atrial muscle bundles found by Spach et al. in fibrotic 42 atrial tissue.13 Simulations were performed without fibrosis, with moderate fibrosis, and with 43 severe fibrosis, in which 0%, 50%, and 70% of elements were fibrotic as described earlier. 1 44 S3 AF initiation in pre- and post-catheter ablations  45 In order to assess the likelihood of AF initiation, both before and after catheter ablations, 20 46 pacing locations were selected in both atria, including the area between the PVs, left atrium 47 (LA), left atrial appendage (LAA), right atrial appendage (RAA), coronary sinus, superior 48 caval vein (SCV), inferior caval vein (ICV), and right atrium (RA). These points were 49 selected based on reported possible sources of extra-PV ectopic focal activity in AF 50 patients.14 All pacing points, except the one located on the LAA were located outside the 51 ablated area. As a consequence, in simulations with LAAI the AF initiation rates were 52 corrected by excluding simulations in which pacing point was located in the LAA. In each 53 simulation one pacing point were selected. The stimulation protocol consisted of a 2 seconds 54 incremental pacing from a selected location, in which a train of stimuli with progressive 55 reduction in pacing interval was applied, followed by 3 seconds of simulation with no pacing.  56 S4 Definition of a successful AF initiation 57 The outcome of the stimulation protocol was analyzed in terms of the type of self-sustained 58 rhythm after 2 seconds of stimulations. In the presence of no activity, the initiation was 59 considered unsuccessful. Otherwise, a distinction between AF and atrial flutter (AFL) was 60 made, with the latter not being considered a successful AF initiation. To differentiate between 61 AF and AFL conduction patterns, we computed 12-leads ECGs from the simulated atrial 62 electrical activity. To construct the ECGs, the atrial model was incorporated into an 63 inhomogeneous torso model including lungs and intracavitary blood masses. Body surface 64 potentials were simulated using a bi-domain equation solved at 1-mm resolution.2 65 In all 12-lead ECGs, after the pacing periods, we detected positive fibrillation wave peaks 66 and calculated fibrillation cycle length (FCL) as the time interval between two successive F-67 wave peaks (Figure S2-A). These FCL lengths were used to generate Poincaré plots (Figure 68 S2-B &C). The FCL were described as a vector 𝐹𝐶𝐿 = [𝑙1, 𝑙2, … , 𝑙𝑁], where 𝑙𝑖 is the ith FCL. 69 Each Poincaré plot is composed of the points of 𝑙𝑖 on the 𝑥-axis versus 𝑙𝑖+1 on the 𝑦-axis. 70 The point cloud dispersion (PCD) in Poincaré plots was used as a sensitive parameter 71 describing the regularity of activations and to differentiate between AFL and AF simulations. 72 PCD were calculated using as an averaged Euclidean distance of each point in a point cloud 73 to the point cloud centroid:  74 𝑃𝐶𝐷 =  1𝑁 − 1∑ √(𝑙𝑖 − 𝑐𝑥)2 + (𝑙𝑖+1 − 𝑐𝑦)2                                                                             (1)𝑁−1𝑖=1 where 𝐶(𝑐𝑥 , 𝑐𝑦) is the centroid of the point cloud in Poincaré plot. Finally, by applying an 75 arbitrary threshold to the PCD we classified AF conduction patterns in simulations from 76 AFL. All algorithms were developed in Matlab (v. R2016a, Mathworks). 77 Figure S3 illustrates representative conduction patterns of simulations and corresponding 78 three-lead ECG in the control group with three different degrees of fibrosis. In the absence of 79 fibrosis AF was not inducible while in moderate and severe fibrosis the stimulation of the 80 atria resulted in AF induction. Figure S4 shows activation patterns in PVI + LAAI 81 simulations and corresponding ECGs without fibrosis, with moderate fibrosis, and with 82 severe fibrosis. In these examples, AF was inducible only in severe fibrosis but not without or 83 in moderate fibrosis.  84 S5 Detection and tracking fibrillation waves 85 A fibrillation wave was defined as a contiguous volume in which all nodes had 86 transmembrane voltages above −60mV. The number of waves was calculated at each 87 millisecond of simulated time.  88 Fibrillation waves were tracked in both time and space, as described in our previous study.15 89 Briefly, the temporal dynamics of waves can be described by three events: 90  Generation: appearance of a new wave due to a wave breaking up into two or more 91 waves. 92  Fusion: the fusion of two or more waves into one wave as they merge with each other. 93  Extinction: the extinction of a wave, because either it hits a boundary or runs into 94 unexcitable tissue. 95 S6 Statistics 96 All statistical analyses were performed using GraphPad Prism software version 8.0. All 97 values were expressed as the mean ± SD. Statistical tests were performed to compare the 98 effect of two parameters, fibrosis levels and ablation patterns, in AF initiation rate, using 99 Two-Way ANOVA followed by post hoc Bonferroni test. A value of P < 0.05 was considered 100 significant. 101   102 Figure legends: 103 Figure S1: Posterior and anterior view of the atria with patchy fibrosis. A) Patchy fibrosis 104 model, 50% fibrotic (anterior view). B) Patchy fibrosis model, 50% fibrotic (posterior view). 105 C) Cross sectional view of the atrial geometry demonstrating fibrosis distribution within the 106 atrial wall. 107 Figure S2: Local electrograms recorded during simulated AF episodes at 4 different point on 108 both atria (E1-E4) and 2 ECG leads (II and V1). 109 Figure S3: Atrial tachycardia patterns. A) An example of simulated lead II ECG in a 110 simulation with atrial fibrillation conduction pattern. Red stars indicate the positive peak of 111 the fibrillation wave. B) Corresponding Poincaré plot generated from the ECG presented in 112 section A. C) An example of ECGs (Lead I, II, and V1) and corresponding Poincaré plots in a 113 simulation with flutter conduction pattern. D) An example of ECGs and corresponding 114 Poincaré plots in a simulation with a fibrillation conduction pattern. 115 Figure S4: AF initiation in control simulations. A) Consecutive snapshots of conduction 116 patterns and corresponding three ECG leads (II, V1, and V3) in control simulations with no, 117 moderate, and severe fibrotic simulations.  118 Figure S5: AF initiation in ablation simulations. Series of conduction pattern snapshots in 119 simulations with pulmonary vein isolations accompanied by LAA isolation and their 120 corresponding three ECG leads (II, V1, and V3) without, with moderate, and with severe 121 fibrosis.  122   123 References: 124 1. Gharaviri A, Bidar E, Potse M, Zeemering S, Verheule S, Pezzuto S, Krause R, 125 Maessen JG, Auricchio A and Schotten U. Epicardial Fibrosis Explains Increased Endo-126 Epicardial Dissociation and Epicardial Breakthroughs in Human Atrial Fibrillation. Front 127 Physiol. 2020;11:68. 128 2. Potse M, Lankveld TA, Zeemering S, Dagnelie PC, Stehouwer CD, Henry RM, 129 Linnenbank AC, Kuijpers NH and Schotten U. P-wave complexity in normal subjects and 130 computer models. J Electrocardiol. 2016;49:545-53. 131 3. Whitaker J, Rajani R, Chubb H, Gabrawi M, Varela M, Wright M, Niederer S and 132 O'Neill MD. The role of myocardial wall thickness in atrial arrhythmogenesis. Europace. 133 2016;18:1758-1772. 134 4. Verheule S, Eckstein J, Linz D, Maesen B, Bidar E, Gharaviri A and Schotten U. Role 135 of endo-epicardial dissociation of electrical activity and transmural conduction in the 136 development of persistent atrial fibrillation. Prog Biophys Mol Biol. 2014;115:173-85. 137 5. Potse M, Dube B, Richer J, Vinet A and Gulrajani RM. A comparison of 138 monodomain and bidomain reaction-diffusion models for action potential propagation in the 139 human heart. IEEE Trans Biomed Eng. 2006;53:2425-35. 140 6. Rush S and Larsen H. A practical algorithm for solving dynamic membrane 141 equations. IEEE Trans Biomed Eng. 1978;25:389-92. 142 7. Saleheen HI and Ng KT. New finite difference formulations for general 143 inhomogeneous anisotropic bioelectric problems. IEEE Trans Biomed Eng. 1997;44:800-9. 144 8. Krause D, Potse M, Dickopf T, Krause R, Auricchio A and Prinzen F. Hybrid 145 Parallelization of a Large-Scale Heart Model. In: R. Keller, D. Kramer and J.-P. Weiss, eds. 146 Facing the Multicore - Challenge II: Springer Berlin Heidelberg; 2012(7174): 120-132. 147 9. Courtemanche M, Ramirez RJ and Nattel S. Ionic mechanisms underlying human 148 atrial action potential properties: insights from a mathematical model. Am J Physiol. 149 1998;275:H301-21. 150 10. Gharaviri A, Verheule S, Eckstein J, Potse M, Kuklik P, Kuijpers NH and Schotten U. 151 How disruption of endo-epicardial electrical connections enhances endo-epicardial 152 conduction during atrial fibrillation. Europace. 2017;19:308-318. 153 11. Pezzuto S, Gharaviri A, Schotten U, Potse M, Conte G, Caputo ML, Regoli F, Krause 154 R and Auricchio A. Beat-to-beat P-wave morphological variability in patients with 155 paroxysmal atrial fibrillation: an in silico study. Europace. 2018;20:iii26-iii35. 156 12. Pezzuto S, Quaglino A and Potse M. On Sampling Spatially-Correlated Random 157 Fields for Complex Geometries. 2019:103-111. 158 13. Spach MS and Dolber PC. Relating extracellular potentials and their derivatives to 159 anisotropic propagation at a microscopic level in human cardiac muscle. Evidence for 160 electrical uncoupling of side-to-side fiber connections with increasing age. Circ Res. 161 1986;58:356-71. 162 14. Santangeli P and Marchlinski FE. Techniques for the provocation, localization, and 163 ablation of non-pulmonary vein triggers for atrial fibrillation. Heart Rhythm. 2017;14:1087-164 1096. 165 15. Gharaviri A, Verheule S, Eckstein J, Potse M, Kuijpers NH and Schotten U. A 166 computer model of endo-epicardial electrical dissociation and transmural conduction during 167 atrial fibrillation. Europace. 2012;14 Suppl 5:v10-v16. 168 Table S1.  Tissue conductivity parameters (𝜎) used in the simulations. The units are mS/cm. 169 The subscript ‘i’ stands for intracellular, ‘e’ for extracellular, ‘L’ for longitudinal, ‘T’ for 170 transverse (within a tissue sheet) and ‘C’ for across-sheet. 171  172  material  𝜎𝑖𝐿 𝜎𝑖𝑇 𝜎𝑖𝐶 𝜎𝑒𝐿 𝜎𝑒𝑇 𝜎𝑒𝐶 𝐺𝑚𝐿 𝐺𝑚𝑇 𝐺𝑚𝐶 wall 3.0 0.3 0.3 3.0 1.2 1.2 1.5 0.24 0.24 iso 1.5 1.5 1.5 1.5 1.5 1.5 0.75 0.75 0.75 BB 9.0 0.3 0.3 9.0 1.2 1.2 4.5 0.24 0.24 fibrotic 3.0 0.0 0.0 3.0 1.2 1.2 1.5 0 0  173   174 Table. S2 Atrial Fibrillation Cycle length (AFCL) in simulations with different 175 ablation patterns (* P Value < 0.05). 176  177  178   179    Simulation groups  Fibrosis Control PVI BOX PVI + LAAI BOX + LAAI without  149±3.2ms 154± 2.3ms* 155± 2.7ms* 163± 2.4ms* 167± 2.7ms* moderate  142± 3ms 149± 2ms* 153± 4.1ms* 157± 3ms* 158± 4.1ms* severe  139± 2.4ms 146± 1.9ms 148 ± 3.4ms 151 ± 2.5ms* 152 ± 3.4ms* Figure S1 180   181 Figure S2 182  183 184   185 Figure S3186  187  188 Figure S4189 190   191 Figure S5 192  193 

Left Atrial Appendage Electrical Isolation Reduces Atrial Fibrillation Recurrences: a simulation study

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Left Atrial Appendage Electrical Isolation Reduces Atrial Fibrillation Recurrences: a simulation study

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NARCIS

Oskar Bordeaux

Maastricht University Research Portal

Maastricht University Research Portal

Oskar Bordeaux

INRIA a CCSD electronic archive server