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On-line Access: 2024-08-27
Received: 2023-10-17
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Citations: Bibtex RefMan EndNote GB/T7714
Zhenghong WU, Yunlong LIU, Lv TONG, Diandian DONG, Dongdong DENG, Ling XIA. Current progress of computational modeling for guiding clinical atrial fibrillation ablation[J]. Journal of Zhejiang University Science B, 2021, 22(10): 805-817.
@article{title="Current progress of computational modeling for guiding clinical atrial fibrillation ablation",
author="Zhenghong WU, Yunlong LIU, Lv TONG, Diandian DONG, Dongdong DENG, Ling XIA",
journal="Journal of Zhejiang University Science B",
volume="22",
number="10",
pages="805-817",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2000727"
}
%0 Journal Article
%T Current progress of computational modeling for guiding clinical atrial fibrillation ablation
%A Zhenghong WU
%A Yunlong LIU
%A Lv TONG
%A Diandian DONG
%A Dongdong DENG
%A Ling XIA
%J Journal of Zhejiang University SCIENCE B
%V 22
%N 10
%P 805-817
%@ 1673-1581
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2000727
TY - JOUR
T1 - Current progress of computational modeling for guiding clinical atrial fibrillation ablation
A1 - Zhenghong WU
A1 - Yunlong LIU
A1 - Lv TONG
A1 - Diandian DONG
A1 - Dongdong DENG
A1 - Ling XIA
J0 - Journal of Zhejiang University Science B
VL - 22
IS - 10
SP - 805
EP - 817
%@ 1673-1581
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2000727
Abstract: atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.
[1]AhmedA, UllahW, HussainI, et al., 2019. Atrial fibrillation: a leading cause of heart failure-related hospitalizations; a dual epidemic. Am J Cardiovasc Dis, 9(5):109-115.
[2]AlessandriniM, ValinotiM, UngerL, et al., 2018. A computational framework to benchmark basket catheter guided ablation in atrial fibrillation. Front Physiol, 9:1251.
[3]AliRL, HakimJB, BoylePM, 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(12):1757-1765.
[4]BayerJD, RoneyCH, PashaeiA, et al., 2016. Novel radiofrequency ablation strategies for terminating atrial fibrillation in the left atrium: a simulation study. Front Physiol, 7:108.
[5]BayerJD, BoukensBJ, KrulSPJ, et al., 2019. Acetylcholine delays atrial activation to facilitate atrial fibrillation. Front Physiol, 10:1105.
[6]BenjaminEJ, BlahaMJ, ChiuveSE, et al., 2017. Heart Disease and Stroke Statistics-2017 Update: a report from the American Heart Association. Circulation, 135(10):e146-e603.
[7]BhattiA, OakeshottP, DhinojaM, et al., 2019. Ablation therapy in atrial fibrillation. BMJ, 367:l6428.
[8]BoylePM, HakimJB, ZahidS, et al., 2018a. Comparing reentrant drivers predicted by image-based computational modeling and mapped by electrocardiographic imaging in persistent atrial fibrillation. Front Physiol, 9:414.
[9]BoylePM, HakimJB, ZahidS, et al., 2018b. The fibrotic substrate in persistent atrial fibrillation patients: comparison between predictions from computational modeling and measurements from focal impulse and rotor mapping. Front Physiol, 9:1151.
[10]BoylePM, ZghaibT, ZahidS, et al., 2019. Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat Biomed Eng, 3(11):870-879.
[11]CantwellCD, MohamiedY, TzortzisKN, et al., 2019. Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling. Comput Biol Med, 104:339-351.
[12]ChrispinJ, Gucuk IpekE, ZahidS, et al., 2016. Lack of regional association between atrial late gadolinium enhancement on cardiac magnetic resonance and atrial fibrillation rotors. Heart Rhythm, 13(3):654-660.
[13]CochetH, DuboisR, YamashitaS, et al., 2018. Relationship between fibrosis detected on late gadolinium-enhanced cardiac magnetic resonance and re-entrant activity assessed with electrocardiographic imaging in human persistent atrial fibrillation. JACC Clin Electrophysiol, 4(1):17-29.
[14]ContiS, WeerasooriyaR, NovakP, et al., 2018. Contact force sensing for ablation of persistent atrial fibrillation: a randomized, multicenter trial. Heart Rhythm, 15(2):201-208.
[15]CourtemancheM, RamirezRJ, NattelS, 1998. Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. Am J Physiol, 275(1):H301-H321.
[16]CoxJL, SchuesslerRB, D'AgostinoHJJr, et al., 1991. The surgical treatment of atrial fibrillation. III. Development of a definitive surgical procedure. J Thorac Cardiovasc Surg, 101(4):569-583.
[17]DangL, ViragN, IharaZ, et al., 2005. Evaluation of ablation patterns using a biophysical model of atrial fibrillation. Ann Biomed Eng, 33(4):465-474.
[18]DengDD, JiaoPF, YeXS, et al., 2012. An image-based model of the whole human heart with detailed anatomical structure and fiber orientation. Comput Math Methods Med, 2012:891070.
[19]DengDD, MurphyMJ, HakimJB, 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, 27(9):093932.
[20]DewireJ, CalkinsH, 2013. Update on atrial fibrillation catheter ablation technologies and techniques. Nat Rev Cardiol, 10(10):599-612.
[21]FochlerF, YamaguchiT, KheirkahanM, et al., 2019. Late gadolinium enhancement magnetic resonance imaging guided treatment of post-atrial fibrillation ablation recurrent arrhythmia. Circ Arrhythm Electrophysiol, 12(8):e007174.
[22]GanesanAN, KuklikP, LauDH, et al., 2013. Bipolar electrogram Shannon entropy at sites of rotational activation: implications for ablation of atrial fibrillation. Circ Arrhythm Electrophysiol, 6(1):48-57.
[23]GharaviriA, PezzutoS, PotseM, et al., 2021. Left atrial appendage electrical isolation reduces atrial fibrillation recurrences: a simulation study. Circ Arrhythm Electrophysiol, 14(1):e009230.
[24]Giffard-RoisinS, JacksonT, FovargueL, et al., 2017. Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping. IEEE Trans Bio-Med Eng, 64(9):2206-2218.
[25]GongYF, XieFG, SteinKM, et al., 2007. Mechanism underlying initiation of paroxysmal atrial flutter/atrial fibrillation by ectopic foci: a simulation study. Circulation, 115(16):2094-2102.
[26]GongYL, GaoY, LuZH, et al., 2015. Preliminary simulation study of atrial fibrillation treatment procedure based on a detailed human atrial model. J Clin Trial Cardiol, 2(4):1-9.
[27]HaACT, WijeysunderaHC, BirnieDH, et al., 2017. Real-world outcomes, complications, and cost of catheter-based ablation for atrial fibrillation: an update. Curr Opin Cardiol, 32(1):47-52.
[28]HaïssaguerreM, JaïsP, ShahDC, et al., 1998. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med, 339(10):659-666.
[29]HaissaguerreM, ShahAJ, CochetH, et al., 2016. Intermittent drivers anchoring to structural heterogeneities as a major pathophysiological mechanism of human persistent atrial fibrillation. J Physiol, 594(9):2387-2398.
[30]HakalahtiA, BiancariF, NielsenJC, et al., 2015. Radiofrequency ablation vs. antiarrhythmic drug therapy as first line treatment of symptomatic atrial fibrillation: systematic review and meta-analysis. Europace, 17(3):370-378.
[31]HakimJB, MurphyMJ, TrayanovaNA, et al., 2018. Arrhythmia dynamics in computational models of the atria following virtual ablation of re-entrant drivers. Europace, 20(S3):iii45-iii54.
[32]HeijmanJ, AlgalarrondoV, VoigtN, et al., 2016. The value of basic research insights into atrial fibrillation mechanisms as a guide to therapeutic innovation: a critical analysis. Cardiovasc Res, 109(4):467-479.
[33]HoSY, Sánchez-QuintanaD, 2009. The importance of atrial structure and fibers. Clin Anat, 22(1):52-63.
[34]HwangM, KwonSS, WiJ, et al., 2014. Virtual ablation for atrial fibrillation in personalized in-silico three-dimensional left atrial modeling: comparison with clinical catheter ablation. Prog Biophys Mol Biol, 116(1):40-47.
[35]HwangM, SongJS, LeeYS, et al., 2016. Electrophysiological rotor ablation in in-silico modeling of atrial fibrillation: comparisons with dominant frequency, Shannon entropy, and phase singularity. PLoS ONE, 11(2):e0149695.
[36]KaboudianA, CherryEM, FentonFH, 2019. Real-time interactive simulations of large-scale systems on personal computers and cell phones: toward patient-specific heart modeling and other applications. Sci Adv, 5(3):eaav6019.
[37]KimIS, LimB, ShimJ, et al., 2019. Clinical usefulness of computational modeling-guided persistent atrial fibrillation ablation: updated outcome of multicenter randomized study. Front Physiol, 10:1512.
[38]KimTH, UhmJS, KimJY, et al., 2017. Does additional electrogram-guided ablation after linear ablation reduce recurrence after catheter ablation for longstanding persistent atrial fibrillation? A prospective randomized study. J Am Heart Assoc, 6(2):e004811.
[39]LatchamsettyR, MoradyF, 2018. Atrial fibrillation ablation. Annu Rev Med, 69:53-63.
[40]LiY, WuYF, ChenKP, et al., 2013. Prevalence of atrial fibrillation in China and its risk factors. Biomed Environ Sci, 26(9):709-716.
[41]LimB, HwangM, SongJS, et al., 2017. Effectiveness of atrial fibrillation rotor ablation is dependent on conduction velocity: an in-silico 3-dimensional modeling study. PLoS ONE, 12(12):e0190398.
[42]LimB, ParkJW, HwangM, et al., 2020a. Electrophysiological significance of the interatrial conduction including cavo-tricuspid isthmus during atrial fibrillation. J Physiol, 598(17):3597-3612.
[43]LimB, KimJ, HwangM, et al., 2020b. In situ procedure for high-efficiency computational modeling of atrial fibrillation reflecting personal anatomy, fiber orientation, fibrosis, and electrophysiology. Sci Rep, 10:2417.
[44]LuoCH, RudyY, 1991. A model of the ventricular cardiac action potential. Depolarization, repolarization, and their interaction. Circ Res, 68(6):1501-1526.
[45]MărgulescuAD, Nuñez-GarciaM, AlarcónF, et al., 2019. Reproducibility and accuracy of late gadolinium enhancement cardiac magnetic resonance measurements for the detection of left atrial fibrosis in patients undergoing atrial fibrillation ablation procedures. Europace, 21(5):724-731.
[46]McDowellKS, ZahidS, VadakkumpadanF, et al., 2015. Virtual electrophysiological study of atrial fibrillation in fibrotic remodeling. PLoS ONE, 10(2):e0117110.
[47]MillerCAS, MaronMS, EstesNAM III, et al., 2019. Safety, side effects and relative efficacy of medications for rhythm control of atrial fibrillation in hypertrophic cardiomyopathy. Am J Cardiol, 123(11):1859-1862.
[48]MorganR, ColmanMA, ChubbH, et al., 2016. Slow conduction in the border zones of patchy fibrosis stabilizes the drivers for atrial fibrillation: insights from multi-scale human atrial modeling. Front Physiol, 7:474.
[49]NademaneeK, McKenzieJ, KosarE, et al., 2004. A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate. J Am Coll Cardiol, 43(11):2044-2053.
[50]NarayanSM, KrummenDE, ShivkumarK, et al., 2012. Treatment of atrial fibrillation by the ablation of localized sources: CONFIRM (Conventional Ablation for Atrial Fibrillation With or Without Focal Impulse and Rotor Modulation) trial. J Am Coll Cardiol, 60(7):628-636.
[51]NattelS, HaradaM, 2014. Atrial remodeling and atrial fibrillation: recent advances and translational perspectives. J Am Coll Cardiol, 63(22):2335-2345.
[52]NattelS, HeijmanJ, ZhouLP, et al., 2020. Molecular basis of atrial fibrillation pathophysiology and therapy: a translational perspective. Circ Res, 127(1):51-72.
[53]NguyenTP, QuZL, WeissJN, 2014. Cardiac fibrosis and arrhythmogenesis: the road to repair is paved with perils. J Mol Cell Cardiol, 70:83-91.
[54]NishidaK, NattelS, 2014. Atrial fibrillation compendium: historical context and detailed translational perspective on an important clinical problem. Circ Res, 114(9):1447-1452.
[55]PallisgaardJL, GislasonGH, HansenJ, et al., 2018. Temporal trends in atrial fibrillation recurrence rates after ablation between 2005 and 2014: a nationwide Danish cohort study. Eur Heart J, 39(6):442-449.
[56]PashakhanlooF, HerzkaDA, AshikagaH, et al., 2016. Myofiber architecture of the human atria as revealed by submillimeter diffusion tensor imaging. Circ Arrhythm Electrophysiol, 9(4):e004133.
[57]PatelNJ, AttiV, MitraniRD, et al., 2018. Global rising trends of atrial fibrillation: a major public health concern. Heart, 104(24):1989-1990.
[58]PedrottyDM, KlingerRY, KirktonRD, et al., 2009. Cardiac fibroblast paracrine factors alter impulse conduction and ion channel expression of neonatal rat cardiomyocytes. Cardiovasc Res, 83(4):688-697.
[59]PontecorboliG, Figueras i VenturaRM, CarlosenaA, et al., 2017. Use of delayed-enhancement magnetic resonance imaging for fibrosis detection in the atria: a review. Europace, 19(2):180-189.
[60]RahmanF, KwanGF, BenjaminEJ, 2014. Global epidemiology of atrial fibrillation. Nat Rev Cardiol, 11(11):639-654.
[61]ReumannM, BohnertJ, OsswaldB, et al., 2007. Multiple wavelets, rotors, and snakes in atrial fibrillation—a computer simulation study. J Electrocardiol, 40(4):328-334.
[62]ReumannM, BohnertJ, SeemannG, et al., 2008. Preventive ablation strategies in a biophysical model of atrial fibrillation based on realistic anatomical data. IEEE Trans Biomed Eng, 55(2):399-406.
[63]RolfS, KircherS, AryaA, et al., 2014. Tailored atrial substrate modification based on low-voltage areas in catheter ablation of atrial fibrillation. Circ Arrhythm Electrophysiol, 7(5):825-833.
[64]RoneyCH, WilliamsSE, CochetH, et al., 2018. Patient-specific simulations predict efficacy of ablation of interatrial connections for treatment of persistent atrial fibrillation. Europace, 20(S3):iii55-iii68.
[65]RoneyCH, BeachML, MehtaAM, et al., 2020. In silico comparison of left atrial ablation techniques that target the anatomical, structural, and electrical substrates of atrial fibrillation. Front Physiol, 11:1145.
[66]RoneyCH, BendikasR, PashakhanlooF, et al., 2021. Constructing a human atrial fibre atlas. Ann Biomed Eng, 49(1):233-250.
[67]RotterM, DangL, JacquemetV, et al., 2007. Impact of varying ablation patterns in a simulation model of persistent atrial fibrillation. Pace-Pacing Clin Electrophysiol, 30(3):314-321.
[68]RoyA, VarelaM, ChubbH, et al., 2020. Identifying locations of re-entrant drivers from patient-specific distribution of fibrosis in the left atrium. PLoS Comput Biol, 16(9):e1008086.
[69]RuchatP, ViragN, DangL, et al., 2007a. A biophysical model of atrial fibrillation ablation: what can a surgeon learn from a computer model? Europace, 9(S6):vi71-vi76.
[70]RuchatP, DangL, ViragN, et al., 2007b. A biophysical model of atrial fibrillation to define the appropriate ablation pattern in modified maze. Eur J Cardio-Thorac Surg, 31(1):65-69.
[71]RuchatP, DangL, SchlaepferJ, et al., 2007c. Use of a biophysical model of atrial fibrillation in the interpretation of the outcome of surgical ablation procedures. Eur J Cardio-Thorac Surg, 32(1):90-95.
[72]SahaM, RoneyCH, BayerJD, et al., 2018. Wavelength and fibrosis affect phase singularity locations during atrial fibrillation. Front Physiol, 9:1207.
[73]SandersP, BerenfeldO, HociniM, et al., 2005. Spectral analysis identifies sites of high-frequency activity maintaining atrial fibrillation in humans. Circulation, 112(6):789-797.
[74]SchadeA, NentwichK, Costello-BoerrigterLC, et al., 2016. Spatial relationship of focal impulses, rotors and low voltage zones in patients with persistent atrial fibrillation. J Cardiovasc Electrophysiol, 27(5):507-514.
[75]SeemannG, HöperC, SachseFB, et al., 2006. Heterogeneous three-dimensional anatomical and electrophysiological model of human atria. Philos Trans Roy Soc A-Math Phys Eng Sci, 364(1843):1465-1481.
[76]SeitzJ, HorvilleurJ, LacotteJ, et al., 2011. Correlation between AF substrate ablation difficulty and left atrial fibrosis quantified by delayed-enhancement cardiac magnetic resonance. Pacing Clin Electrophysiol, 34(10):1267-1277.
[77]SeitzJ, BarsC, ThéodoreG, et al., 2017. AF ablation guided by spatiotemporal electrogram dispersion without pulmonary vein isolation: a wholly patient-tailored approach. J Am Coll Cardiol, 69(3):303-321.
[78]ShadeJK, AliRL, BasileD, et al., 2020. Preprocedure application of machine learning and mechanistic simulations predicts likelihood of paroxysmal atrial fibrillation recurrence following pulmonary vein isolation. Circ Arrhythm Electrophysiol, 13(7):e008213.
[79]ShimJ, HwangM, SongJS, 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.
[80]SimI, RazeghiO, KarimR, et al., 2019. Reproducibility of atrial fibrosis assessment using CMR imaging and an open source platform. JACC Cardiovasc Imaging, 12(10):2076-2077.
[81]SławutaA, JacekP, MazurG, et al., 2020. Quality of life and frailty syndrome in patients with atrial fibrillation. Clin Interv Aging, 15:783-795.
[82]SohnsC, LemesC, MetznerA, et al., 2017. First-in-man analysis of the relationship between electrical rotors from noninvasive panoramic mapping and atrial fibrosis from magnetic resonance imaging in patients with persistent atrial fibrillation. Circ Arrhythm Electrophysiol, 10(8):e004419.
[83]SteinbeckG, SinnerMF, LutzM, et al., 2018. Incidence of complications related to catheter ablation of atrial fibrillation and atrial flutter: a nationwide in-hospital analysis of administrative data for Germany in 2014. Eur Heart J, 39(45):4020-4029.
[84]TakahashiY, O'NeillMD, HociniM, et al., 2008. Characterization of electrograms associated with termination of chronic atrial fibrillation by catheter ablation. J Am Coll Cardiol, 51(10):1003-1010.
[85]TrayanovaNA, PopescuDM, ShadeJK, 2021. Machine learning in arrhythmia and electrophysiology. Circ Res, 128(4):544-566.
[86]VandersickelN, van NieuwenhuyseE, van CleemputN, et al., 2019. Directed networks as a novel way to describe and analyze cardiac excitation: directed graph mapping. Front Physiol, 10:1138.
[87]VermaA, JiangCY, BettsTR, et al., 2015. Approaches to catheter ablation for persistent atrial fibrillation. N Engl J Med, 372(19):1812-1822.
[88]ViragN, JacquemetV, HenriquezCS, et al., 2002. Study of atrial arrhythmias in a computer model based on magnetic resonance images of human atria. Chaos, 12(3):754-763.
[89]WeimarT, SchenaS, BaileyMS, et al., 2012. The Cox-Maze procedure for lone atrial fibrillation: a single-center experience over 2 decades. Circ Arrhythm Electrophysiol, 5(1):8-14.
[90]WoodsCE, OlginJ, 2014. Atrial fibrillation therapy now and in the future: drugs, biologicals, and ablation. Circ Res, 114(9):1532-1546.
[91]ZahidS, WhyteKN, SchwarzEL, et al., 2016. Feasibility of using patient-specific models and the "minimum cut" algorithm to predict optimal ablation targets for left atrial flutter. Heart Rhythm, 13(8):1687-1698.
[92]ZhaoJC, HansenBJ, WangYF, 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(8):e005922.
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