Radiofrequency ablation (RFA) is a widely used clinical treatment for many types of cardiac arrhythmias. However, nontransmural lesions and gaps between linear lesions often lead to recurrence of the arrhythmia. Intracardiac electrograms (IEGMs) provide real-time information regarding the state of the cardiac tissue surrounding the catheter tip. Nevertheless, the formation and interpretation of IEGMs during the RFA procedure is complex and yet not fully understood. In this in-silico study, we propose a computational model for acute ablation lesions. Our model consists of a necrotic scar core and a border zone, describing irreversible and reversible temperature induced electrophysiological phenomena. These phenomena are modeled by varying the intra- and extracellular conductivity of the tissue as well as a regulating zone factor. The computational model is evaluated regarding its feasibility and validity. Therefore, this model was compared to an existing one and to clinical measurements of five patients undergoing RFA. The results show that the model can indeed be used to recreate IEGMs. We computed IEGMs arising from complex ablation scars, such as scars with gaps or two overlapping ellipsoid scars. For orthogonal catheter orientation, the presence of a second necrotic core in the near-field of a punctiform acute ablation lesion had minor impact on the resulting signal morphology. The presented model can serve as a base for further research on the formation and interpretation of IEGMs

Dössel, Olaf

Greiner, Joachim

Lenis, Gustavo

Pollnow, Stefan

Schuler, Steffen

Seemann, Gunnar

English

KITopen

Current Directions in Biomedical Engineering 2016; 2(1): 607–610Open AccessJoachim Greiner*, Stefan Pollnow, Steffen Schuler, Gustavo Lenis, Gunnar Seemannand Olaf DösselSimulation of intracardiac electrograms aroundacute ablation lesionsDOI 10.1515/cdbme-2016-0134Abstract: Radiofrequency ablation (RFA) is a widely usedclinical treatment for many types of cardiac arrhythmias.However, nontransmural lesions and gaps between linearlesions often lead to recurrence of the arrhythmia. Intrac-ardiac electrograms (IEGMs) provide real-time informa-tion regarding the state of the cardiac tissue surroundingthe catheter tip. Nevertheless, the formation and inter-pretation of IEGMs during the RFA procedure is complexand yet not fully understood. In this in-silico study, wepropose a computational model for acute ablation lesions.Our model consists of a necrotic scar core and a borderzone, describing irreversible and reversible temperatureinduced electrophysiological phenomena. These phenom-ena are modeled by varying the intra- and extracellularconductivity of the tissue as well as a regulating zonefactor. The computational model is evaluated regardingits feasibility and validity. Therefore, this model was com-pared to an existing one and to clinical measurements offive patients undergoing RFA. The results show that themodel can indeedbeused to recreate IEGMs.We computedIEGMs arising from complex ablation scars, such as scarswith gaps or two overlapping ellipsoid scars. For orthogo-nal catheter orientation, the presence of a second necroticcore in the near-field of a punctiform acute ablation lesionhadminor impact on the resulting signal morphology. Thepresented model can serve as a base for further researchon the formation and interpretation of IEGMs.Keywords: acute ablation lesions; in-silico modelling; in-tracardiac electrograms; radiofrequency ablation.*Corresponding author: Joachim Greiner, Institute of BiomedicalEngineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12,76128 Karlsruhe, Germany, E-mail: publications@ibt.kit.eduStefan Pollnow, Steffen Schuler, Gustavo Lenis and Olaf Dössel:Institute of Biomedical Engineering, Karlsruhe Institute ofTechnology (KIT), Kaiserstr. 12, 76128 Karlsruhe, GermanyGunnar Seemann: Institute for Experimental CardiovascularMedicine, University Heart Center Freiburg – Bad Krozingen,Medical Center – University of Freiburg, Germany; and MedicalDepartment, Albert-Ludwigs University, Freiburg, Germany,E-mail: gunnar.seemann@universitaets-herzzentrum.de1 IntroductionRadiofrequency ablation (RFA) is the gold standard pro-cedure for many forms of cardiac arrhythmias, includingatrial fibrillation, when drug therapy is not effective. A cor-ner stone for RFA success is the creation of transmural andcontinuous scars arising from elevated temperatures dueto radiofrequency currents. Unfortunately, the recurrencerate of arrhythmias is relatively high at 35–45% [1], whichmeans that patients often have to undergo a second pro-cedure. However, to date, there are no reliable criteria forevaluating acute ablation lesions andmonitoring the abla-tion process. Animal and clinical studies have shown thatelectrogram (EGM)-based criteria can be used to evaluatethe formation of single point lesions [2, 3]. In this in-silicostudy, we investigated the influence of ablation lesionsand catheter orientation on the morphology of intracar-diac electrograms (IEGMs).Weextendedanexistingmodelof an acute ablation lesion to cover more complex geome-tries; that is, those consisting of two point-shaped lesions.By evaluating the simulated IEGMs, new parameters canbe identified to assess complex ablation lesions.2 Methods2.1 Simulation setupFor our in-silico studies, we solved the finite element bido-main formulation using the cardiac simulation frameworkacCELLerate [4]. We adapted our computational model ofan acute ablation lesion from the model of Keller et al.[5]. The simulations were performed on an isotropic patchof myocardium with a size of 40mm× 24mm× 22mmand a spatial resolution of 0.2mm. An 8F (2.7mm)non-irrigated ablation catheter with an 8mm distalelectrode and a 1.2 mm proximal electrode was placedeither orthogonally or parallel to the myocardium. Thecatheter tip deforms the tissue surface by 1mm for theorthogonal orientation, whereas the parallel orientationresults in no tissue deformation. An intracellular stimulus© 2016 Joachim Greiner et al., licensee De Gruyter.This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License.608 | J. Greiner et al.: Simulation of intracardiac electrogramsTable 1: Overview of intra- and extracellular conductivities and CVfor several temperature ranges.Material σi (S/m) σe (S/m) CV (mm/s)Myocardium 0.40 0.264 800Blood 10−10 0.70 –Lesion (40–43°C) 0.47–0.53 0.264 844–873Lesion (43–46°C) 0.53–0.36 0.264 873–784Lesion (46–50°C) 0.36–0.01 0.264 784–160Lesion (necrotic) 10−10 0.10 –Electrode 10−10 7 · 103 –Isolation 10−10 10−10 –The intracellular conductivity in the border zones is continuous andis given as the range of values in this zone. We assumed amembrane capacity Cm of 0.1 µF/cm2 and surface to volume ratio βof 100mm−1.current was applied at the left tissue boundary to initiateexcitation propagation. The extracellular and intracellularconductivities of themyocardiumwere set to 0.264 S/m [6]and 0.40 S/m, respectively, which yielded a conductionvelocity (CV) of 800mm/s (see Table 1). According toClayton et al. [7], both of these values are within therange of experimental measurements. The myocardiumis completely surrounded by blood with an extracellularconductivity of 0.7 S/m and no intracellular conductivity[8]. The conductivity of the electrode was defined to be10,000 times higher than the conductivity of blood, whichresulted in a near-equipotential volume inside the metal.An isolating material was placed between the distal andproximal electrode. The boundaries of the intra- andextracellular domains were implemented with no-fluxNeumann conditions and the following constraint wasused to fix the extracellular potential ϕe:∫︁Ωϕe dΩ = 0. (1)2.2 Ablation modelsKeller et al. [5] modeled an acute ablation lesion with anecrotic core and a border zone consisting of six differentlayers with properties of heated myocardium. We ex-panded this model to a continuous temperature distribu-tion,wherewe calculated electrophysiological parametersuniquely for each voxel in the border zone. The actionpotential (AP) changes were modeled in a ten Tusschercell model, which was adapted for ischemia phase 1aby Wilhelms et al. [5, 9]. The lesion model assumes amaximum scar width of 10mm for orthogonal orientation(for parallel orientation: 12.8mm) and a scar width toFigure 1: Exemplary simulation setup for orthogonal catheterorientation. The whole setup is surrounded by blood (not shown inthis figure). Legend: Acute border zone (50°C40°C), necrotic scar core ( ), myocardium ( ), electrode ( ),isolation ( ).depth ratio of 1.25 for orthogonal catheter orientation (forparallel orientation: 1.6) [5]. The intracellular conductivityof the necrotic scar core was set to zero and the extra-cellular conductivity was set to 0.1 S/m. We estimated theintracellular conductivity for each temperature using thefindings of the hyperthermia experiments from Simmerset al. [10]. Subsequently, small patches of myocardium(120× 30× 65 voxel elements with a spatial resolution of0.2mm) were used to determine intracellular conductiv-ities for different temperatures, while the correspondingextracellular conductivity remained constant. We calcu-lated the CV by using the steepest point in the AP up-stroke as reference point for cellular activation. CV overintracellular conductivity σi was then interpolated withcubic splines to obtain a function for CV (see Table 1). Thesecond part of this study deals with a simplified in-silicomodel for complex ablation lesions. As a first approach,we assumed that the border zones of the first ablationlesion were completely cooled down by blood flow andheat loss due to local tissue perfusion. Inflammation aswell as edemas were excluded in this model. The secondablation lesionwas set along the x-axis of the first ablationscar. The distance between the center of both lesions wasvaried between 6.6 and 12.6mmwith a step-width of 2mm,representing all important scar configurations frommostlyoverlapping scar cores to ablation lesions with gaps. If thepassive necrotic core overlapped the border zones of thesecond lesion, necrotic tissue was assigned to this region.2.3 Simulation and analysis of IEGMsWe referenced the simulated extracellular potentials withthe tissue-distant top voxel layer of our computationalsetup. The bipolar EGM was obtained by subtracting theJ. Greiner et al.: Simulation of intracardiac electrograms | 609Table 2: Relative changes during ablation in measured and simulated IEGMs characteristics (mean value and standard deviation).Clinical IEGMs Simulated IEGMsOrientation Rel. maximum Rel. minimum Rel. derivative Rel. maximum Rel. minimum Rel. derivativeOrthogonal, distal −0.17 ± 0.27 −0.94 ± 0.10 −0.80 ± 0.09 −0.13 ± 0.00 −0.81 ± 0.00 −0.78 ± 0.00Orthogonal, proximal – – – −0.04 ± 0.00 −0.64 ± 0.00 −0.49 ± 0.00Orthogonal, bipolar −0.95 ± 0.17 −0.20 ± 0.33 −0.79 ± 0.10 −0.87 ± 0.00 −0.33 ± 0.00 −0.81 ± 0.00Parallel – DP, distal −0.27 ± 0.53 −1.04 ± 0.14 −0.81 ± 0.05 −0.18 ± 0.00 −0.73 ± 0.00 −0.31 ± 0.00Parallel – DP, proximal −0.15 ± 0.23 −0.09 ± 0.53 0.09 ± 0.41 −0.52 ± 0.00 −0.23 ± 0.00 0.06 ± 0.00Parallel – DP, bipolar −1.06 ± 0.37 −0.14 ± 0.54 −0.24 ± 0.82 −0.73 ± 0.00 −0.27 ± 0.00 −0.04 ± 0.00Parallel – PD, distal −0.31 ± 0.21 −1.00 ± 0.12 −0.88 ± 0.10 −0.31 ± 0.00 −0.73 ± 0.00 −0.53 ± 0.00Parallel – PD, proximal −0.22 ± 0.21 −0.46 ± 0.12 −0.45 ± 0.19 0.09 ± 0.00 −0.20 ± 0.00 0.17 ± 0.00Parallel – PD, bipolar −0.59 ± 0.17 −0.39 ± 0.03 −0.46 ± 0.13 −0.80 ± 0.00 −0.25 ± 0.00 −0.25 ± 0.00For parallel catheter orientation, we distinguished between distal to proximal (DP) and proximal to distal (PD) wave propagation.proximal EGM from the distal EGM. In order to compareclinical measurements with simulated IEGMs, a transferfunction with a first order butterworth highpass at 30Hzand a first order butterworth lowpass at 250Hz was used[5]. For comparison, we automatically annotated the fol-lowing markers in the simulated data as well as from fivepatients undergoing RFA procedure in the right atrium:positive peak amplitude, negative peak amplitude andthe maximum absolute first derivative between positiveand negative peak. The deviation of computed IEGMs andclinical IEGMs was quantified by the deviation factor QV:QV =∑︁∀i1(σclinical,i)2·(︀Eclinical,i − Ecomp,i)︀2 , (2)where σclinical is the standard deviation and E is the meanvalue of each charactistic.3 ResultsExtracted quantified signal statistics are shown in Table 2.For orthogonal catheter orientation, extracted mean val-ues are in high accordance with the distribution extractedfromclinical IEGMs, resulting in relatively a smallQV valueof 2. For parallel catheter orientation with distal to prox-imal wave propagation, all mean values are in line withthe clinical IEGMs, except the upstroke reduction in thedistal IEGM. Therefore, QV results in a rather high value of107. Proximal to distal wave propagation lead to a similarpattern, but with less total deviation, resulting in a QVvalue of 59.InFigure 2, someof the simulated IEGMsarepresentedin their filtered state together with the clinical signals fororthogonal catheter orientation. For all cases only a slightchange was determined in the proximal IEGM. Therefore,the changes in bipolar IEGMswere dominated by the distal–2–101–1010 20 40 60 0 20 40 60 0 20 40 600 20 40 60 0 20 40 60 0 20 40 600 20 40 60 0 20 40 60 0 20 40 60–1012–6–4–202–202–2024–6–4–202–202–2024Amplitude (mV) Complex (late)Amplitude (mV)Amplitude (mV)SimulationClinicalDistal IEGM Proximal IEGM Bipolar IEGMTime (ms) Time (ms) Time (ms)Figure 2: Comparison of filtered simulated IEGMs for point-shapedand complex ablation lesions, as well as clinical filtered IEGMs,before and after RFA, for orthogonal catheter orientation. ScarLegend: Point-shaped scars ( ), distance of complex scars(12.6mm 6.6mm, 12.6mm 6.6mm). Time Legend:Before ablation ( ), after ablation ( ).IEGM. This relation was also present for parallel catheterorientation. For complex scars, the visualization goal isnot to observe individual changes, but rather to grasp theoverall minor changes in signal morphology. Neither themorphology of the IEGM, nor the extracted signal statisticshowed major changes.4 DiscussionIn this computational study, we investigated themorphological changes of IEGMs shortly after the RFA610 | J. Greiner et al.: Simulation of intracardiac electrogramsprocedure. For this purpose, an existingmodel of an acuteablation lesion was developed further and subsequently,a first model of a complex ablation lesion was introduced.The in-silico experiments were performed with an 8F non-irrigated ablation catheter, which is often used in clinicalablation procedures. Additionally, the orientation of theablation catheter and the distances between two ablationlesions were varied on the three-dimensional myocardialpatch. This computational setup offered the possibilityto evaluate the distal, proximal and bipolar IEGMs forvarying lesion geometry and transmurality.We compared the expanded model with the compu-tational model from Keller et al. [5] and clinical signalsduring RFA procedures. Compared to the original modelfrom [5], the deviation factor QV decreased from 513 to167. Hereby, a better statistical agreement between oursimulated IEGMs and the clinical IEGMs was found withthe newablation lesionmodel. The simulation setup of thecomplex ablation lesionswas afirst step to reproducemorerealistic clinical ablation patterns consisting of severalpoint-shaped lesions. With our simplified necrotic core-model it is not feasible to detect gaps between two ablationscars nor to identify correspondent markers in the IEGMs,especially in retrospect with the natural deviation rangeseen in the clinical data set. To the best of our knowledge,no computationalmodel exist to investigatemore complexacute ablation lesions and cardiac electrophysiologyaround these areas simultaneously. In a next step, we willimplement further modular expansions to the ablationprocess. We can computationally substantiate manypresumptions, such as heat distribution and bio-heattransfer, blood flow and applied contact-force. Moreover,non-irrigated or irrigated ablation catheters of differentgeometries could be investigated. Simulated IEGMs of thisnew model have to be compared with clinical signals ofmore complex ablation lesions, such as linear lesions.Hereby, we will evaluate the changes of the IEGMs toidentify robust criteria for describing lesion geometry aswell as transmurality.Finally, we want to highlight that clinical filter param-eters are usually overlooked when talking about IEGMs.Signal morphology is strongly dependent on the order aswell as on the chosen cut-off frequencies of the filter andtherefore, these parameters have to be carefully consid-ered when comparing IEGMs.Acknowledgment: The authors would like to thankMatthias Werber for his support in the in-silicoexperiments.Author’s StatementResearch funding: Stefan Pollnow is supported by a schol-arship of the Karlsruhe School of Optics and Photonics(KSOP). Conflict of interest: The authors declare that theyhave no competing interests. Informed consent: Informedconsent is not applicable. Ethical approval: The conductedresearch is not related to either human or animal use.References[1] Shurrab M, Biase LD, Briceno DF, Kaoutskaia A, Haj-YahiaS, Newman D, et al. Impact of contact force technology onatrial fibrillation ablation: a meta-analysis. J Am Heart Assoc.2015;4:e002476.[2] Otomo K, Uno K, Fujiwara H, Isobe M, Iesaka Y. Localunipolar and bipolar electrogram criteria for evaluating thetransmurality of atrial ablation lesions at different catheterorientations relative to the endocardial surface. Heart Rhythm.2010;7:1291–300.[3] Bortone A, Appetiti A, Bouzeman A, Maupas E, Ciobotaru V,Boulenc JM, et al. Unipolar signal modification as a guidefor lesion creation during radiofrequency application in theleft atrium: a prospective study in humans in the setting ofparoxysmal atrial fibrillation catheter ablation. Circ ArrhythmiaElectrophysiol. 2013;6:1095–102.[4] Seemann G, Sachse FB, Karl M, Weiss DL, Heuveline V, DösselO. Framework for modular, flexible and eflcient solving thecardiac bidomain equation using PETSc. Progr Ind Math.2010;15:363–9.[5] Keller MW, Schuler S, Wilhelms M, Lenis G, SeemannG, Schmitt C, et al. Characterization of radiofrequencyablation lesion development based on simulated andmeasured intracardiac electrograms. IEEE Trans Biomed Eng.2014;61:2467–78.[6] Schwab BC, Seemann G, Lasher RA, Torres NS, Wulfers EM,Arp M, et al. Quantitative analysis of cardiac tissue includingfibroblasts using three-dimensional confocal microscopyand image reconstruction: towards a basis for electro-physiological modeling. IEEE Trans Med Imaging. 2013;32:862–72.[7] Clayton RH, Bernus O, Cherry EM, Dierckx H, Fenton FH,Mirabella L, et al. Models of cardiac tissue electrophysiology:progress, challenges and open questions. Prog Biophys MolBiol. 2011;104:22–48.[8] Gabriel S, Lau RW, Gabriel C. The dielectric properties ofbiological tissues: III. parametric models for the dielectricspectrum of tissues. Phys Med Bio. 1996;41:2271.[9] Wilhelms M, Dössel O, Seemann G. In silico investi-gation of electrically silent acute cardiac ischemia inthe human ventricles. IEEE Trans Biomed Eng. 2011;58:2961–4.[10] Simmers TA, De Bakker JM, Wittkampf FH, Hauer RN.Effects of heating on impulse propagation in super-fused canine myocardium. J Amer Coll Cardiol. 1995;25:1457–64.

Simulation of intracardiac electrograms around acute ablation lesions

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Simulation of intracardiac electrograms around acute ablation lesions

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