The right atrium affects in silico arrhythmia vulnerability in both atria

Abstract

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 Courtemanche cellular model was modified from control conditions to a setup representing AF-induced remodeling [1] with 0%, 50%, and 100% changes for H, M, and S, respectively. Conduction velocity was set to 1.2, 1.0, and 0.8m/s for each remodeling state. Fibrosis extent corresponded to Utah 2 (5-20%) and Utah 4 (>35%) stages [2] 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 [3]. A point was classified as inducing arrhythmia if reentry was maintained for at least 1s. 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) as illustrated in Figure 1. No arrhythmia was induced in the H models. RA inclusion increased the TCL of LA reentries by 5.5% (186.9±13.3ms to 197.2±18.3ms, p=0.006) in scenario M and decreased it by 7.2% (224.3±27.6ms to 208.2±34.8ms, 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 arrhythmia vulnerability in a biatrial model differs from that 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.