The Right Atrium Affects in silico Arrhythmia Vulnerability in Both Atria
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Abstract
Introduction The role of the right atrium (RA) in atrial fibrillation (AF) has long been overlooked. Computer models of the atria can aid in assessing how the RA influences arrhythmia vulnerability and in studying the role of RA drivers in the induction of AF, both aspects challenging to assess in living patients. It remains unclear if incorporating the RA influences the reentry inducibility of the model. As personalized ablation strategies rely on non-inducibility criteria, the adequacy of left atrium (LA)-only models for developing such ablation tools is uncertain. Aim To evaluate the effect of incorporating the RA in 3D patient-specific computer models on arrhythmia vulnerability. Methods Imaging data from 8 subjects were obtained to generate patient-specific computer models. We created 2 models for each subject: a monoatrial with only the LA and a biatrial with both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courte-manche et al. cellular model was modified from control conditions to a setup representing AF-induced remodeling with 0 %, 50 %, and 100 % changes for H, M, and S, respectively. Conduction velocity was set to 1.2, 1.0, and 0.8 m/s for each remodeling state. Fibrosis extent corresponded to Utah 2 (5-20 %) and Utah 4 ( > 35 %) stages for M and S, while the H state was modeled without fibrosis. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm using openCARP. A point was classified as inducing arrhythmia if reentry was maintained for at least 1 s. The vulnerability ratio was defined as the number of inducing points divided by the number of stimulation points. The mean tachycardia cycle length (TCL) was assessed at the stimulation site. We compared LA vulnerability ratios in monoatrial and biatrial models. Results Incorporating the RA increased the mean LA vulnerability ratio by 115.8 % (0.19 ± 0.13 to 0.41 ± 0.22, p = 0.033) in state M and 29.0 % in state S (0.31 ± 0.14 to 0.40 ± 0.15, p = 0.219). No arrhythmia was induced in the H models. RA inclusion increased the TCL of LA reentries by 5.5 % (186.9 ± 13.3 ms to 197.2 ± 18.3 ms, p = 0.006) in scenario M and decreased it by 7.2 % (224.3 ± 27.6 ms to 208.2 ± 34.8 ms , p = 0.010) in scenario S. RA inclusion increased LA inducibility revealing 5.5 ± 3.0 new points per patient in the LA for the biatrial model, which did not induce reentry in the monoatrial model. Conclusions LA reentry vulnerability in a biatrial model is higher than in a monoatrial model. Incorporating the RA in patient-specific computational models unmasked potential inducing points in the LA. The RA had a substrate-dependent effect on reentry dynamics, altering the TCL of LA-induced reentries. Our results provide evidence for an important role of the RA in the maintenance and induction of arrhythmia in patient-specific computational models, thus suggesting the use of biatrial models.
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References (52)
- doi:10.1016/j.jacc.2021.04.012 via crossref
- doi:10.1093/eurheartj/ehz786 via crossref
- doi:10.1093/europace/euw161 via crossref
- doi:10.1093/europace/euw014 via crossref
- doi:10.1161/circulationaha.105.546648 via crossref
- doi:10.1161/circresaha.114.303211 via crossref
- doi:10.1146/annurev-physiol-031720085307 via crossref
- doi:10.1093/ehjci/jeac011 via crossref
- doi:10.1016/j.acvd.2019.06.010 via crossref
- doi:10.1111/jce.13297 via crossref
- doi:10.1111/j.1540-8167.2007.00941.x via crossref
- doi:10.1093/ehjci/jeac152 via crossref
- doi:10.1016/j.hrthm.2015.09.030 via crossref
- doi:10.1111/j.1540-8167.1996.tb00492.x via crossref
- doi:10.1161/circep.108.772392 via crossref
- doi:10.1007/s10840-016-0198-2 via crossref
- doi:10.3389/fcvm.2022.997998 via crossref
- doi:10.1111/jce.12187 via crossref
- doi:10.1038/s41551-019-0437-9 via crossref
- doi:10.1093/europace/euac116 via crossref
- doi:10.1038/s41598-020-59372-x via crossref
- doi:10.1161/circep.121.010253 via crossref
- doi:10.1109/tmi.2012.2201948 via crossref
- doi:10.1016/j.compmedimag.2023.102265 via crossref
- doi:10.1093/europace/euy231 via crossref
- doi:10.1515/cdbme-2021-2035 via crossref
- doi:10.1093/europace/euu256 via crossref
- doi:10.1152/ajpheart.1998.275.1.h301 via crossref
- doi:10.35097/1027 via crossref
- doi:10.1016/j.cmpb.2021.106223 via crossref
- doi:10.35097/1830 via crossref
- doi:10.1515/bmt-2014-5012 via crossref
- doi:10.3389/fphys.2012.00487/abstract via crossref
- doi:10.1007/978-3-319-20309-6 via crossref
- doi:10.3390/jcm10081797 via crossref
- doi:10.1093/europace/euy095 via crossref
- doi:10.1016/j.jacep.2017.07.016 via crossref
- doi:10.1161/jaha.122.026028 via crossref
- doi:10.1093/europace/euad278 via crossref
- doi:10.1001/jama.2014.3 via crossref
- doi:10.1111/j.1540-8167.2011.02140.x via crossref
- doi:10.1016/j.jelectrocard.2012.08.005 via crossref
- doi:10.1093/europace/euw365 via crossref
- doi:10.3389/fphys.2021.656411 via crossref
- doi:10.1111/j.1540-8167.1999.tb00708.x via crossref
- doi:10.1093/eurheartj/ehaa612 via crossref
- doi:10.1161/cir.0000000000000665 via crossref
- doi:10.1161/circresaha.111.300158 via crossref
- doi:10.1093/europace/7.s1.83 via crossref
- doi:10.1093/cvr/cvw073 via crossref
- doi:10.1371/journal.pcbi.1008086 via crossref
- doi:10.3390/cells10112852 via crossref
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