General Commentary ARTICLE
Commentary: Virtual In-Silico Modeling Guided Catheter Ablation Predicts Effective Linear Ablation Lesion Set for Longstanding Persistent Atrial Fibrillation: Multicenter Prospective Randomized Study
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
A commentary on
Virtual In-Silico Modeling Guided Catheter Ablation Predicts Effective Linear Ablation Lesion Set for Longstanding Persistent Atrial Fibrillation: Multicenter Prospective Randomized Study
by Shim, J., Hwang, M., Song, J.-S., Lim, B., Kim, T.-H., Joung, B., et al. (2017). Front. Physiol. 8:792. doi: 10.3389/fphys.2017.00792
Shim et al. (2017) presented a prospective study on ablation therapy guided by computational modeling results in the current “Frontiers in Physiology” research topic “Clinical Application of Physiome Models.” In their study, patients with persistent atrial fibrillation (AF) scheduled for ablation therapy were randomized to have either the physician or a computational model determine the best ablation strategy out of a predefined set of five strategies. They derived patient-specific anatomical surface models from left atrial CT images and induced atrial fibrillation (AF) in the model by applying a specific rapid pacing protocol. A human operator applied point-wise virtual ablation to the models by clicking in a graphical user interface according to each of the five strategies: circumferential pulmonary vein isolation (CPVI), CPVI + posterior box ablation ± anterior line, CPVI + roof line + left lateral isthmus line, CPVI + CFAE ablation based on the electrograms derived from the model. The ablation strategy that led to earliest termination of AF in the simulation was deemed optimal and applied in the patients.
Prospectively guiding catheter ablation for AF by means of personalized computational models is a long desired (Krueger et al., 2013b; Boyle et al., 2016; Jacquemet, 2016), giant leap in the field of computational cardiology that we want to congratulate on. Succeeding to do so in a multicenter study in particular is a great accomplishment in terms of logistical, procedural, and presumably, traditionalistic challenges. Shim et al. report a duration of <6 h from the time the CT was available to the time the model suggested the optimal ablation strategy and all manual work was performed during core working hours. Their results show that it is feasible to leverage computational modeling in a clinical time scale and under clinical constraints. Besides feasibility, Shim et al. proved that computationally guided ablation is not inferior in terms of procedure duration and complication rate. These are great findings that will hopefully fuel translational efforts to exploit computational models in the field of cardiac electrophysiology (Boyle et al., 2017). Despite these promising results, we want to highlight that the results regarding efficacy of the ablation therapy (comparable recurrence rates as for empirical ablation) should be considered specific for the rather simplistic approach employed to choose a particular ablation pattern using the computational model.
The models used by Shim et al. were individualized only in terms of the anatomy of the endocardial wall. Differences in myocardial wall thickness have been reported to influence AF dynamics (Biktasheva et al., 2015; Whitaker et al., 2016). These, as well as potential dissociation between layers in the atrial wall (Verheule et al., 2014) can per definition not be considered in a surface model of excitation spread. Moreover, the properties of the atrial substrate play a crucial role for virtually all mechanisms discussed to be potentially involved in AF perpetuation. According to the circus movement reentry concept (Schotten et al., 2011), the wavelength defined as the product of the conduction velocity (CV) and the duration of the effective refractory period (ERP) plays a crucial role. The wavelength in relation to the atrial surface determines to a large share how many reentrant activities can be sustained on a given anatomical model (Deng et al., 2017). Thus, reduction of wavelength (due to CV or ERP decrease) could compensate for a large share of the effects of an enlarged LA (Qu, 2006). Intracardiac electrograms can be used to derive patient-specific information regarding the CV (Weber et al., 2011; Cantwell et al., 2015), the ERP (Corrado et al., 2017), zones of slow conduction or block (Trächtler et al., 2015), and low voltage areas as a surrogate for fibrotic regions (Jadidi et al., 2016). However, time constraints cause the spatial resolution of these measurements to be rather coarse and they can only be derived during the procedure. Thus, either the time window for the computational evaluation would shrink significantly or the measurements would need to be acquired during an extra procedure. Substrate information that can be obtained non-invasively is late gadolinium-enhanced MRI as a surrogate for fibrotic tissue. Particularly the spatial distribution of fibrotic tissue has been shown to crucially impact AF dynamics in computational models (McDowell et al., 2015; Zahid et al., 2016a). Other aspects that have been shown to affect AF dynamics and can be considered in models as population-level a-priori knowledge is gross myocyte orientation (Wachter et al., 2015) and regional electrophysiological heterogeneity (Colman et al., 2013; Krueger et al., 2013a). A non-homogeneous atrial substrate is a prerequisite when simulating signals to compute meaningful CFAE maps (Ashihara et al., 2012; Keller et al., 2013). Known gene mutations (Loewe et al., 2014) and e.g., blood electrolyte levels (Krueger et al., 2011) give room for further personalization.
In addition to the possible improvements regarding model fidelity and personalization, the virtual ablation method and assessment of its success give opportunities for further optimization. The set of ablation strategies to choose from was rather limited in the study by Shim et al. and other approaches could be required to obtain optimal results (Bayer et al., 2016; Zahid et al., 2016b). Probably even more important, termination of the specific AF episode that was induced in the model as a sole success criterion might not be sufficient to predict long-term success. Other aspects to consider are reinducability of (potentially different) AF episodes and vulnerability to subsequently develop atrial flutter, which is a common clinical complication.
In conclusion, we would like to point out that the work by Shim et al. is an outstanding example of translating computational modeling to the clinical environment and encourage everyone to follow down this road. On the other hand, we want to highlight that the results on efficacy of computationally guided ablation should be considered a lower bound rather than a representative example of what value personalized electrophysiological modeling can potentially add. We hope that the work by Shim et al. will fuel the development and facilitate the use of more sophisticated models under clinical constraints to leverage the full power of computational modeling approaches in the near future.
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) through CRC 1173 and DO 637/22-2.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Ashihara, T., Haraguchi, R., Nakazawa, K., Namba, T., Ikeda, T., Nakazawa, Y., et al. (2012). The role of fibroblasts in complex fractionated electrograms during persistent/permanent atrial fibrillation: implications for electrogram-based catheter ablation. Circ. Res. 110, 275–284. doi: 10.1161/CIRCRESAHA.111.255026
Bayer, J. D., Roney, C. H., Pashaei, A., Jais, P., and Vigmond, E. J. (2016). Novel radiofrequency ablation strategies for terminating atrial fibrillation in the left atrium: A simulation study. Front. Physiol. 7:108. doi: 10.3389/fphys.2016.00108
Biktasheva, I. V., Dierckx, H., and Biktashev, V. N. (2015). Drift of scroll waves in thin layers caused by thickness features: asymptotic theory and numerical simulations. Phys. Rev. Lett. 114:68302. doi: 10.1103/PhysRevLett.114.068302
Boyle, P. M., Zahid, S., and Trayanova, N. A. (2016). Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. Europace 18, iv136–iv145. doi: 10.1093/europace/euw358
Boyle, P. M., Zahid, S., and Trayanova, N. A. (2017). Using personalized computer models to custom-tailor ablation procedures for atrial fibrillation patients: are we there yet? Expert Rev. Cardiovasc. Ther. 15, 339–341. doi: 10.1080/14779072.2017.1317593
Cantwell, C. D., Roney, C. H., Ng, F. S., Siggers, J. H., Sherwin, S. J., and Peters, N. S. (2015). Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping. Comput. Biol. Med. 65, 229–242. doi: 10.1016/j.compbiomed.2015.04.027
Colman, M. A., Aslanidi, O. V., Kharche, S., Boyett, M. R., Garratt, C., Hancox, J. C., et al. (2013). Pro-arrhythmogenic effects of atrial fibrillation-induced electrical remodelling: insights from the three-dimensional virtual human atria. J. Physiol. 591, 4249–4272. doi: 10.1113/jphysiol.2013.254987
Corrado, C., Whitaker, J., Chubb, H., Williams, S., Wright, M., Gill, J., et al. (2017). Personalized models of human atrial electrophysiology derived from endocardial electrograms. IEEE Trans. Biomed. Eng. 64, 735–742. doi: 10.1109/TBME.2016.2574619
Deng, D., Murphy, M. J., Hakim, J. B., Franceschi, W. H., Zahid, S., Pashakhanloo, F., et al. (2017). Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate. Chaos Interdiscip. J. Nonlinear Sci. 27:93932. doi: 10.1063/1.5003340
Jadidi, A. S., Lehrmann, H., Keyl, C., Sorrel, J., Markstein, V., Minners, J., et al. (2016). Ablation of persistent atrial fibrillation targeting low-voltage areas with selective activation characteristics. Circ. Arrhythm. Electrophysiol. 9:e002962. doi: 10.1161/CIRCEP.115.002962
Keller, M. W., Luik, A., Abady, M., Seemann, G., Schmitt, C., and Dössel, O. (2013). Influence of three-dimensional fibrotic patterns on simulated intracardiac electrogram morphology. Comput. Cardiol. 40, 923–926.
Krueger, M. W., Dorn, A., Keller, D. U. J., Holmqvist, F., Carlson, J., Platonov, P. G., et al. (2013a). In-silico modeling of atrial repolarization in normal and atrial fibrillation remodeled state. Med. Biol. Eng. Comput. 51, 1105–1119. doi: 10.1007/s11517-013-1090-1
Krueger, M. W., Seemann, G., Rhode, K., Keller, D. U. J., Schilling, C., Arujuna, A., et al. (2013b). Personalization of atrial anatomy and elelectophysiology as a basis for clinical modeling of radio-frequency-ablation of atrial fibrillation. IEEE Trans. Med. Imaging 32, 73–84. doi: 10.1109/TMI.2012.2201948
Krueger, M. W., Severi, S., Rhode, K., Genovesi, S., Weber, F. M., Vincenti, A., et al. (2011). Alterations of atrial electrophysiology related to hemodialysis session: insights from a multiscale computer model. J. Electrocardiol. 44, 176–183. doi: 10.1016/j.jelectrocard.2010.11.016
Loewe, A., Wilhelms, M., Fischer, F., Scholz, E. P., Dössel, O., and Seemann, G. (2014). Arrhythmic potency of human ether-à-go-go-related gene mutations L532P and N588K in a computational model of human atrial myocytes. Europace 16, 435–443. doi: 10.1093/europace/eut375
McDowell, K. S., Zahid, S., Vadakkumpadan, F., Blauer, J., MacLeod, R. S., and Trayanova, N. A. (2015). Virtual electrophysiological study of atrial fibrillation in fibrotic remodeling. PLoS ONE 10:e0117110. doi: 10.1371/journal.pone.0117110
Qu, Z. (2006). Critical mass hypothesis revisited: role of dynamical wave stability in spontaneous termination of cardiac fibrillation. Am. J. Physiol. Heart Circ. Physiol. 290, H255–H263. doi: 10.1152/ajpheart.00668.2005
Schotten, U., Verheule, S., Kirchhof, P., and Goette, A. (2011). Pathophysiological mechanisms of atrial fibrillation: a translational appraisal. Physiol. Rev. 91, 265–325. doi: 10.1152/physrev.00031.2009
Shim, J., Hwang, M., Song, J.-S., Lim, B., Kim, T.-H., Joung, B., et al. (2017). Virtual in-silico modeling guided catheter ablation predicts effective linear ablation lesion set for longstanding persistent atrial fibrillation: multicenter prospective randomized study. Front. Physiol. 8:792. doi: 10.3389/fphys.2017.00792
Trächtler, J., Oesterlein, T. G., Loewe, A., and Dössel, O. (2015). Virtualizing clinical cases of atrial flutter in a fast marching simulation including conduction velocity and ablation scars. Curr. Dir. Biomed. Eng. 1, 405–408. doi: 10.1515/cdbme-2015-0098
Verheule, S., Eckstein, J., Linz, D., Maesen, B., Bidar, E., Gharaviri, A., et al. (2014). Role of endo-epicardial dissociation of electrical activity and transmural conduction in the development of persistent atrial fibrillation. Prog. Biophys. Mol. Biol. 115, 173–185. doi: 10.1016/j.pbiomolbio.2014.07.007
Wachter, A., Loewe, A., Krueger, M. W., Dössel, O., and Seemann, G. (2015). Mesh structure-independent modeling of patient-specific atrial fiber orientation. CDBME. 1, 409–412. doi: 10.1515/cdbme-2015-0099
Weber, F. M., Luik, A., Schilling, C., Seemann, G., Krueger, M. W., Lorenz, C., et al. (2011). Conduction velocity restitution of the human atrium - An efficient measurement protocol for clinical electrophysiological studies. IEEE Trans. Biomed. Eng. 58, 2648–2655. doi: 10.1109/TBME.2011.2160453
Whitaker, J., Rajani, R., Chubb, H., Gabrawi, M., Varela, M., Wright, M., et al. (2016). The role of myocardial wall thickness in atrial arrhythmogenesis. Europace 18, 1758–1772. doi: 10.1093/europace/euw014
Zahid, S., Cochet, H., Boyle, P. M., Schwarz, E. L., Whyte, K. N., Vigmond, E. J., et al. (2016a). Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. Cardiovasc. Res. 110, 443–454. doi: 10.1093/cvr/cvw073
Zahid, S., Whyte, K. N., Schwarz, E. L., Blake, R. C., Boyle, P. M., Chrispin, J., et al. (2016b). Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter. Hear. Rhythm 13, 1687–1698. doi: 10.1016/j.hrthm.2016.04.009
Keywords: atrial fibrillation, catheter ablation, virtual ablation, computational model, in-silico modeling
Citation: Loewe A and Dössel O (2017) Commentary: Virtual In-Silico Modeling Guided Catheter Ablation Predicts Effective Linear Ablation Lesion Set for Longstanding Persistent Atrial Fibrillation: Multicenter Prospective Randomized Study. Front. Physiol. 8:1113. doi: 10.3389/fphys.2017.01113
Received: 15 November 2017; Accepted: 15 December 2017;
Published: 22 December 2017.
Edited by:Natalia A. Trayanova, Johns Hopkins University, United States
Reviewed by:Henggui Zhang, University of Manchester, United Kingdom
Copyright © 2017 Loewe and Dössel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Axel Loewe, email@example.com