Electrical activation during atrial fibrillation (AF) appears chaotic and disorganised, which impedes characterisation of the underlying substrate and treatment planning. While globally chaotic, there may be local preferential activation pathways that represent potential ablation targets. This study aimed to identify preferential activation pathways during AF and predict the acute ablation response when these are targeted by pulmonary vein isolation (PVI). In patients with persistent AF (n = 14), simultaneous biatrial contact mapping with basket catheters was performed pre-ablation and following each ablation strategy (PVI, roof, and mitral lines). Unipolar wavefront activation directions were averaged over 10 s to identify preferential activation pathways. Clinical cases were classified as responders or non-responders to PVI during the procedure. Clinical data were augmented with a virtual cohort of 100 models. In AF pre-ablation, pathways originated from the pulmonary vein (PV) antra in PVI responders (7/7) but not in PVI non-responders (6/6). We proposed a novel index that measured activation waves from the PV antra into the atrial body. This index was significantly higher in PVI responders than non-responders (clinical: 16.3 vs. 3.7%, p = 0.04; simulated: 21.1 vs. 14.1%, p = 0.02). Overall, this novel technique and proof of concept study demonstrated that preferential activation pathways exist during AF. Targeting patient-specific activation pathways that flowed from the PV antra to the left atrial body using PVI resulted in AF termination during the procedure. These PV activation flow pathways may correspond to the presence of drivers in the PV regions.

Boston Scientific Corp St Paul MN United States

Department of Cardiology Guy's and St Thomas' Hospital London United Kingdom

Department of Cardiology Na Holmolce Hospital Prague Czechia

INSIGNEO Institute for In Silico Medicine and Department of Computer Science University of Sheffield Sheffield United Kingdom

Institute of Cardiovascular Science University College London London United Kingdom

School of Biomedical Engineering and Imaging Sciences King's College London London United Kingdom

Zobrazit více v PubMed

Ali R. L., Hakim J. B., Boyle P. M., Zahid S., Sivasambu B., Marine J. E., et al. . (2019). Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models. Cardiovasc. Res. 115, 1757–1765. 10.1093/cvr/cvz083 PubMed DOI PMC

Azzolin L., Schuler S., Dössel O., Loewe A. (2021). A reproducible protocol to assess arrhythmia vulnerability in silico: pacing at the end of the effective refractory period. Front. Physiol. 12:656411. 10.3389/fphys.2021.656411 PubMed DOI PMC

Bayer J. D., Roney C. H., Pashaei A., Jaïs P., Vigmond E. J. (2016). Novel Radiofrequency Ablation Strategies for Terminating Atrial Fibrillation in the Left Atrium: A Simulation Study. Front. Physiol. 7:108. 10.3389/fphys.2016.00108 PubMed DOI PMC

Bellmann B., Lin T., Ruppersberg P., Zettwitz M., Guttmann S., Tscholl V., et al. . (2018). Identification of active atrial fibrillation sources and their discrimination from passive rotors using electrographical flow mapping. Clin. Res. Cardiol. 107, 1021–1032. 10.1007/s00392-018-1274-7 PubMed DOI

Boyle P. M., Hakim J. B., Zahid S., Franceschi W. H., Murphy M. J., Vigmond E. J., et al. . (2018). Comparing reentrant drivers predicted by image-based computational modeling and mapped by electrocardiographic imaging in persistent atrial fibrillation. Front. Physiol. 9:414. 10.3389/fphys.2018.00414 PubMed DOI PMC

Calkins H., Hindricks G., Cappato R., Kim Y. H., Saad E. B., Aguinaga L., et al. . (2017). 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation. Europace 20, e1–e160. 10.1093/europace/eux274 PubMed DOI PMC

Child N., Clayton R. H., Roney C. H., Laughner J. I., Shuros A., Neuzil P., et al. . (2018). Unraveling the underlying arrhythmia mechanism in persistent atrial fibrillation: results from the STARLIGHT study. Circ. Arrhythmia Electrophysiol. 11:e005897. 10.1161/CIRCEP.117.005897 PubMed DOI

Courtemanche M., Ramirez R. J., Nattel S. (1998). Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. Am. J. Physiol. 275, H301–H321. 10.1152/ajpheart.1998.275.1.H301 PubMed DOI

Courtemanche M., Ramirez R. J., Nattel S. (1999). Ionic targets for drug therapy and atrial fibrillation-induced electrical remodeling: insights from a mathematical model. Cardiovasc. Res. 42, 477–489. 10.1016/s0008-6363(99)00034-6 PubMed DOI

Csepe T. A., Hansen B. J., Fedorov V. V. (2017). Atrial fibrillation driver mechanisms: insight from the isolated human heart. Trends Cardiovasc. Med. 27, 1–11. 10.1016/j.tcm.2016.05.008 PubMed DOI PMC

Dapogny C., Dobrzynski C., Frey P. (2014). Three-dimensional adaptive domain remeshing, implicit domain meshing, and applications to free and moving boundary problems. J. Comput. Phys. 262, 358–378. 10.1016/j.jcp.2014.01.005 DOI

Dharmaprani D., Schopp M., Kuklik P., Chapman D., Lahiri A., Dykes L., et al. . (2019). Renewal theory provides a universal quantitative framework to characterise the continuous regeneration of rotational events in cardiac fibrillation. Circ Arrhythm Electrophysiol. 12:e007569. 10.1161/CIRCEP.119.007569 PubMed DOI

Gerstenfeld E. P., Sahakian A. V., Swiryn S. (1992). Evidence for transient linking of atrial excitation during atrial fibrillation in humans. Circulation 86, 375–382. 10.1161/01.CIR.86.2.375 PubMed DOI

Hansen B. J., Zhao J., Csepe T. A., Moore B. T., Li N., Jayne L. A., et al. . (2015). Atrial fibrillation driven by micro-anatomic intramural re-entry revealed by simultaneous sub-epicardial and sub-endocardial optical mapping in explanted human hearts. Eur. Heart J. 36, 2390–2401. 10.1093/eurheartj/ehv233 PubMed DOI PMC

Hocini M., Ho S. Y., Kawara T., Linnenbank A. C., Potse M., Shah D., et al. . (2002). Electrical conduction in canine pulmonary veins: electrophysiological and anatomic correlation. Circulation 105, 2442–2448. 10.1161/01.CIR.0000016062.80020.11 PubMed DOI

Horn B. K., Schunck B. G. (1981). Determining optical flow. Artif. Intell. 17, 185–203.

Johner N., Namdar M., Shah D. C. (2019). Individualised approaches for Catheter ablation of AF: patient selection and procedural endpoints. Arrhythmia Electrophysiol. Rev. 8, 184–190. 10.15420/aer.2019.33.2 PubMed DOI PMC

Kay M. W., Gray R. A. (2005). Measuring curvature and velocity vector fields for waves of cardiac excitation in 2-D media. IEEE Trans. Biomed. Eng. 52, 50–63. 10.1109/TBME.2004.839798 PubMed DOI

Kochhäuser S., Jiang C. Y., Betts T. R., Chen J., Deisenhofer I., Mantovan R., et al. . (2017). Impact of acute atrial fibrillation termination and prolongation of atrial fibrillation cycle length on the outcome of ablation of persistent atrial fibrillation: a substudy of the STAR AF II trial. Heart Rhythm 14, 476–483. 10.1016/j.hrthm.2016.12.033 PubMed DOI

Krueger M. W., Rhode K. S., O'Neill M. D., Rinaldi C. A., Gill J., Razavi R., et al. . (2014). Patient-specific modeling of atrial fibrosis increases the accuracy of sinus rhythm simulations and may explain maintenance of atrial fibrillation. J. Electrocardiol. 47, 324–328. 10.1016/j.jelectrocard.2013.11.003 PubMed DOI

Labarthe S., Bayer J., Coudière Y., Henry J., Cochet H., Jaïs P., et al. . (2014). A bilayer model of human atria: mathematical background, construction, and assessment. Europace 16(Suppl 4), iv21–iv29. 10.1093/europace/euu256 PubMed DOI

Laughner J., Shome S., Child N., Shuros A., Neuzil P., Gill J., et al. . (2016). Practical considerations of mapping persistent atrial fibrillation with whole-chamber basket catheters. JACC Clin. Electrophysiol. 2, 55–65. 10.1016/j.jacep.2015.09.017 PubMed DOI

Lee G., Kumar S., Teh A., Madry A., Spence S., Larobina M., et al. . (2014). Epicardial wave mapping in human long-lasting persistent atrial fibrillation: transient rotational circuits, complex wavefronts, and disorganized activity. Eur. Heart J. 35, 86–97. 10.1093/eurheartj/eht267 PubMed DOI

Lim H. S., Derval N., Komatsu Y., Zellerhoff S., Denis A., Shah A. J., et al. . (2015). Is ablation to termination the best strategy for ablation of persistent atrial fibrillation. Circ. Arrhythmia Electrophysiol. 8, 963–970. 10.1161/CIRCEP.114.001721 PubMed DOI

Narayan S. M., Patel J., Mulpuru S., Krummen D. E. (2012). Focal impulse and rotor modulation ablation of sustaining rotors abruptly terminates persistent atrial fibrillation to sinus rhythm with elimination on follow-up: a video case study. Heart Rhythm 9, 1436–1439. 10.1016/j.hrthm.2012.03.055 PubMed DOI PMC

Navara R., Leef G., Shenasa F., Kowalewski C., Rogers A. J., Meckler G., et al. . (2018). Independent mapping methods reveal rotational activation near pulmonary veins where atrial fibrillation terminates before pulmonary vein isolation. J. Cardiovasc. Electrophysiol. 29, 687–695. 10.1111/jce.13446 PubMed DOI PMC

Ng J., Kadish A. H., Goldberger J. J. (2007). Technical considerations for dominant frequency analysis. J. Cardiovasc. Electrophysiol. 18, 757–764. 10.1111/j.1540-8167.2007.00810.x PubMed DOI

Pashakhanloo F., Herzka D. A., Ashikaga H., Mori S., Gai N., Bluemke D. A., et al. . (2016). Myofiber architecture of the human atria as revealed by submillimeter diffusion tensor imaging. Circ. Arrhythmia Electrophysiol. 9, 1–9. 10.1161/CIRCEP.116.004133 PubMed DOI PMC

Potse M. (2018). Scalable and accurate ECG simulation for reaction-diffusion models of the human heart. Front. Physiol. 9:370. 10.3389/fphys.2018.00370 PubMed DOI PMC

Razeghi O., Solís-Lemus J. A., Lee A. W. C., Karim R., Corrado C., Roney C. H., et al. . (2020). CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research. SoftwareX, 12:100570. 10.1016/j.softx.2020.100570 PubMed DOI PMC

Romero J., Di Biase L., Mohanty S., Trivedi C., Patel K., Parides M., et al. . (2020). Long-term outcomes of left atrial appendage electrical isolation in patients with nonparoxysmal atrial fibrillation: a propensity score-matched analysis. Circ. Arrhythmia Electrophysiol. 13:e008390. 10.1161/CIRCEP.120.008390 PubMed DOI

Roney C. H., Bayer J. D., Cochet H., Meo M., Dubois R., Jaïs P., et al. . (2018). Variability in pulmonary vein electrophysiology and fibrosis determines arrhythmia susceptibility and dynamics. PLoS Comput. Biol. 14:e1006166. 10.1371/journal.pcbi.1006166 PubMed DOI PMC

Roney C. H., Bayer J. D., Zahid S., Meo M., Boyle P. M., Trayanova N. A., et al. . (2016). Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms. Europace. 18, iv146–iv155. 10.1093/europace/euw365 PubMed DOI PMC

Roney C. H., Beach M. L., Mehta A. M., Sim I., Corrado C., Bendikas R., et al. . (2020a). In silico comparison of left atrial ablation techniques that target the anatomical, structural, and electrical substrates of atrial fibrillation. Front. Physiol. 11:572874. 10.3389/fphys.2020.572874 PubMed DOI PMC

Roney C. H., Bendikas R., Pashakhanloo F., Corrado C., Vigmond E. J., McVeigh E. R., et al. . (2020b). Constructing a human atrial fibre atlas. Ann. Biomed. Eng. 49, 233–250. 10.1007/s10439-020-02525-w PubMed DOI PMC

Roney C. H., Cantwell C. D., Bayer J. D., Qureshi N. A., Lim P. B., Tweedy J. H., et al. . (2017a). Spatial resolution requirements for accurate identification of drivers of atrial fibrillation. Circ. Arrhythmia Electrophysiol. 10:e004899. 10.1161/CIRCEP.116.004899 PubMed DOI PMC

Roney C. H., Cantwell C. D., Qureshi N. A., Chowdhury R. A., Dupont E., Lim P. B., et al. . (2017b). Rotor tracking using phase of electrograms recorded during atrial fibrillation. Ann. Biomed. Eng. 45, 910–923. 10.1007/s10439-016-1766-4 PubMed DOI PMC

Roney C. H., Pashaei A., Meo M., Dubois R., Boyle P. M., Trayanova N. A., et al. . (2019). Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes. Med. Image Anal. 55, 65–75. 10.1016/j.media.2019.04.004 PubMed DOI PMC

Roy A., Varela M., Aslanidi O. (2018). Image-based computational evaluation of the effects of atrial wall thickness and fibrosis on re-entrant drivers for atrial fibrillation. Front. Physiol. 9:1352. 10.3389/fphys.2018.01352 PubMed DOI PMC

Saliani A., Tsikhanovich A., Jacquemet V. (2019). Visualization of interpolated atrial fiber orientation using evenly-spaced streamlines. Comput. Biol. Med. 111:103349. 10.1016/j.compbiomed.2019.103349 PubMed DOI

Shkurovich S., Sahakian A. V., Swiryn S. (1998). Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique. IEEE Tran. Biomed. Eng. 45, 229–234. 10.1109/10.661270 PubMed DOI

Sim I., Razeghi O., Karim R., Chubb H., Whitaker J., O'Neill L., et al. . (2019). Reproducibility of atrial fibrosis assessment using CMR imaging and an open source platform. JACC Cardiovasc. Imaging 12, 2076–2077. 10.1016/j.jcmg.2019.03.027 PubMed DOI

Singh S. M., D'Avila A., Kim Y. H., Aryana A., Mangrum J. M., Michaud G. F., et al. . (2017). Termination of persistent atrial fibrillation during pulmonary vein isolation: insight from the MAGIC-AF trial. Europace 19, 1657–1663. 10.1093/europace/euw266 PubMed DOI

Van Nieuwenhuyse E., Strisciuglio T., Lorenzo G., El Haddad M., Goedgebeur J., Van Cleemput N., et al. . (2021). Evaluation of directed graph-mapping in complex atrial tachycardias. JACC Clin. Electrophysiol. 936–949. 10.1016/j.jacep.2020.12.013 PubMed DOI

Vandersickel N., Van Nieuwenhuyse E., Van Cleemput N., Goedgebeur J., El Haddad M., De Neve J., et al. . (2019). Directed networks as a novel way to describe and analyze cardiac excitation: directed graph mapping. Front. Physiol. 10:1138. 10.3389/fphys.2019.01138 PubMed DOI PMC

Verma A., Jiang C.-Y., Betts T. R., Chen J., Deisenhofer I., Mantovan R., et al. . (2015). Approaches to catheter ablation for persistent atrial fibrillation. N. Engl. J. Med. 372, 1812–1822. 10.1056/NEJMoa1408288 PubMed DOI

Vigmond E. J., Hughes M., Plank G., Leon L. J. (2003). Computational tools for modeling electrical activity in cardiac tissue. J. Electrocardiol. 36(Suppl):69– 74. 10.1016/j.jelectrocard.2003.09.017 PubMed DOI

Zahid S., Cochet H., Boyle P. M., Schwarz E. L., Whyte K. N., Vigmond E. J., et al. . (2016). Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. Cardiovasc. Res. 110, 443–454. 10.1093/cvr/cvw073 PubMed DOI PMC

Zhao J., Hansen B. J., Wang Y., Csepe T. A., Sul L. V., Tang A., et al. . (2017). Three-dimensional integrated functional, structural, and computational mapping to define the structural “fingerprints” of heart-specific atrial fibrillation drivers in human heart ex vivo. J. Am. Heart Assoc. 6:e005922. 10.1161/JAHA.117.005922 PubMed DOI PMC