Patient-Centered Computer Model Comparing Excitation Conduction in Normal and Long-Term Persistence of Atrial Fibrillation

Lippi G., Sanchis-Gomar F., Cervellin G. 2021. Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. Intern. J. Stroke. 16 (2), 217–221. https://doi.org/10.1177/1747493019897870

Article  Google Scholar 

Yamaguchi T., Tsuchiya, T., Nakahara S., Fukui A., Nagamoto Y., Murotani K., et al. 2016. Efficacy of left atrial voltage-based catheter ablation of persistent atrial fibrillation. J. Cardiovasc. Electrophysiol. 27 (9), 1055–1063. https://doi.org/10.1111/jce.13019

Article  PubMed  Google Scholar 

Kirchhof P., Calkins H. 2017. Catheter ablation in patients with persistent atrial fibrillation. European Heart J. 38 (1), 20–26. https://doi.org/10.1093/eurheartj/ehw260

Article  Google Scholar 

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. New Engl. J. Med. 372 (19), 1812–1822. https://doi.org/10.1056/NEJMoa1408288

Article  PubMed  Google Scholar 

Yu H.T., Kim I.S., Kim T.H., Uhm J.S., Kim J.Y., Joung B., et al. 2020. Persistent atrial fibrillation over 3 years is associated with higher recurrence after catheter ablation. J. Cardiovasc. Electrophysiol. 31 (2), 457–464. https://doi.org/10.1111/jce.14345

Article  PubMed  PubMed Central  Google Scholar 

Burstein B., Nattel S. 2008. Atrial fibrosis: Mechanisms and clinical relevance in atrial fibrillation. J. Amer. Coll. Cardiol. 51 (8), 802–809. https://doi.org/10.1016/j.jacc.2007.09.064

Article  CAS  Google Scholar 

Boyle P.M., Zghaib T., Zahid S., Ali R.L., Deng D., Franceschi W.H., Trayanova, N. A. 2019. Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nature Biomed. Engin. 3 (11), 870–879. https://doi.org/10.1038/s41551-019-0437-9

Article  Google Scholar 

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 (12), 1757–1765. https://doi.org/10.1093/cvr/cvz083

Article  CAS  PubMed  PubMed Central  Google Scholar 

Honarbakhsh S., Hunter R.J., Dhillon G., Ullah W., Keating E., Providencia R., et al. 2018. Validation of a novel mapping system and utility for mapping complex atrial tachycardias. J. Cardiovasc. Electrophysiol. 29 (3), 395–403. https://doi.org/10.1111/jce.13437

Article  CAS  PubMed  Google Scholar 

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 (2), 477–489. https://doi.org/10.1016/s0008-6363(99)00034-6

Article  CAS  PubMed  Google Scholar 

Karim R., Housden R.J., Balasubramaniam M., Chen Z., Perry D., Uddin A., et al. 2013. Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: An open-access grand challenge. J. Cardiovasc. Magn. Resonance. 15 (1), 105. https://doi.org/10.1186/1532-429X-15-105

Article  Google Scholar 

Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J.-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. 2012. 3D slicer as an image computing platform for the quantitative imaging network. Magn. Resonance Imag. 30 (9), 1323–1341. https://doi.org/10.1016/j.mri.2012.05.001

Article  Google Scholar 

Huang Z., Zhang T., Heng W., Shi B., Zhou S. 2022. Real-time intermediate flow estimation for video frame interpolation. Eur. Confer. Computer Vision. Cham: Springer Nature. 13674. 624–642. https://doi.org/10.1007/978-3-031-19781-9_36

Article  Google Scholar 

Plank G., Loewe A., Neic A., Augustin C., Huang Y.L., Gsell M.A., et al. 2021. The openCARP simulation environment for cardiac electrophysiology. Comp. Meth. Progr. Biomed. 208. 106223. https://doi.org/10.1016/j.cmpb.2021.106223

Article  Google Scholar 

Azzolin L., Eichenlaub M., Nagel C., Nairn D., Sánchez J., Unger L., et al. 2023. AugmentA: Patient-specific augmented atrial model generation tool. Computerized Med. Imag. Graph. 108. 102265. https://doi.org/10.1016/j.compmedimag.2023.102265

Article  Google Scholar 

Courtemanche M., Ramirez R.J., Nattel S. 1998. Ionic mechanisms underlying human atrial action potential properties: Insights from a mathematical model. Amer. J. Physiol., Heart Circ. Physiol. 275 (1), H301–H321. https://doi.org/10.1152/ajpheart.1998.275.1.H301

Article  CAS  Google Scholar 

Gao Z., Lau C.P., Chiu S.W., Li G.R. 2004. Inhibition of ultra-rapid delayed rectifier K+ current by verapamil in human atrial myocytes. J. Mol. Cell. Cardiol. 36 (2), 257–263. https://doi.org/10.1016/j.yjmcc.2003.11.003

Article  CAS  PubMed  Google Scholar 

Britton O.J., Abi-Gerges N., Page G., Ghetti A., Miller P.E., Rodriguez B. 2017. Quantitative comparison of effects of dofetilide, sotalol, quinidine, and verapamil between human ex vivo trabeculae and in silico ventricular models incorporating inter-individual action potential variability. Front. Physiol. 8, 597. https://doi.org/10.3389/fphys.2017.00597

Article  PubMed  PubMed Central  Google Scholar 

Majumder R., Pandit R., Panfilov A.V. 2014. Turbulent electrical activity at sharp-edged inexcitable obstacles in a model for human cardiac tissue. Amer. J. Physiol., Heart Circ. Physiol. 307 (7), H1024–H1035. https://doi.org/10.1152/ajpheart.00593.2013

Article  CAS  Google Scholar 

Marrouche N.F. 2014. Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: The DECAAF study. J. Amer. Med. Associat. 312 (17), 1805–1805. https://doi.org/10.1001/jama.2014.3

Article  CAS  Google Scholar 

Himmel H.M., Bussek A., Hoffmann M., Beckmann R., Lohmann H., Schmidt M., Wettwer E. 2012. Field and action potential recordings in heart slices: Correlation with established in vitro and in vivo models. Brit. J. Pharmacol. 166 (1), 276–296. https://doi.org/10.1111/j.1476-5381.2011.01775.x

Article  CAS  Google Scholar 

Duytschaever M.F., Garratt C.J., Allessie M.A. 2000. Profibrillatory effects of verapamil but not of digoxin in the goat model of atrial fibrillation. J. Cardiovasc. Electrophysiol. 11 (12), 1375–1385. https://doi.org/10.1046/j.1540-8167.2000.01375.x

Article  CAS  PubMed  Google Scholar 

Zhou P., Zhang S.M., Wang Q.L., Wu Q., Chen M., Pei J.M. 2013. Anti-arrhythmic effect of verapamil is accompanied by preservation of Cx43 protein in rat heart. PLoS One. 8 (8), e71567. https://doi.org/10.1371/journal.pone.0071567

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stern E.H., Pitchon R., King B.D., Guerrero J., Schneider R.R., Wiener I. 1982. Clinical use of oral verapamil in chronic and paroxysmal atrial fibrillation. Chest. 81 (3), 308–311. https://doi.org/10.1378/chest.81.3.308

Article  CAS  PubMed  Google Scholar 

den Uijl D.W., Cabanelas N., Benito E.M., Figueras R., Alarcon F., Borras R., et al. 2018. Impact of left atrial volume, sphericity, and fibrosis on the outcome of catheter ablation for atrial fibrillation. J. Cardiovasc. Electrophysiol. 29 (5), 740–746. https://doi.org/10.1111/jce.13482

Article  PubMed  Google Scholar 

Kudryashova N., Nizamieva A., Tsvelaya V., Panfilov A.V., et al. 2019. Self-organization of conducting pathways explains electrical wave propagation in cardiac tissues with high fraction of non-conducting cells. PLoS Comput. Biol. 15 (3), e1006597. https://doi.org/10.1371/journal.pcbi.1006597

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kudryashova N., Tsvelaya V., Agladze K., Panfilov A. 2017. Virtual cardiac monolayers for electrical wave propagation. Sci. Rep. 7 (1), 7887. https://doi.org/10.1038/s41598-017-07653-3

Article  CAS  PubMed  PubMed Central  Google Scholar 

Comments (0)

No login
gif