1Karlsruher Institut für Technologie (KIT) Institut für Biomedizinische Technik Karlsruhe, Deutschland; 2Universitat Politècnica de València (UPV) Institute of Information and Communication Technologies (ITACA) Valencia, Spanien; 3Universitätsklinikum Institute for Experimental Cardiovascular Medicine Freiburg, Deutschland
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 until now. It remains unclear whether the incorporation of the RA influences the propensity of the model to reentry induction. 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. For each subject, we created 2 models: one monoatrial consisting only of the LA, and one biatrial model consisting of both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courtemanche 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 along the myocyte preferential direction was set to 1.2, 1.0, and 0.8m/s for each remodeling level. To incorporate fibrotic substrate, we manually placed six seeds on each biatrial model, 3 in the LA and 3 in the RA, corresponding to regions with the most frequent enhancement (IIR>1.2) in LGE-MRI. The extent of the fibrotic substrate corresponded to the Utah 2 (5-20%) and Utah 4 (>35%) stages, for M and S respectively, while the H state was modeled without fibrosis. Electrical propagation in the atria was modeled using the monodomain equation solved with openCARP. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm. A point was classified as inducing arrhythmia if reentry was initiated and 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) of the induced arrhythmia was assessed at the stimulation site. We compared the vulnerability ratio of the LA in monoatrial and biatrial configurations.
Results
The incorporation of the RA increased the mean LA vulnerability ratio by 115.79% (0.19±0.13 to 0.41±0.22, p=0.033) in state M, and 29.03% 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 5.51% (186.9±13.3ms to 197.2±18.3ms, p=0.006) in M scenario, and decreased it by 7.17% (224.3±27.6ms to 208.2±34.8ms, p=0.010) in scenario S. RA inclusion resulted in an elevated LA inducibility, revealing 4.9±3.3 additional points per patient in the LA for the biatrial model that did not induce reentry in the monoatrial model.
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 until now. It remains unclear whether the incorporation of the RA influences the propensity of the model to reentry induction. 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. For each subject, we created 2 models: one monoatrial consisting only of the LA, and one biatrial model consisting of both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courtemanche 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 along the myocyte preferential direction was set to 1.2, 1.0, and 0.8m/s for each remodeling level. To incorporate fibrotic substrate, we manually placed six seeds on each biatrial model, 3 in the LA and 3 in the RA, corresponding to regions with the most frequent enhancement (IIR>1.2) in LGE-MRI. The extent of the fibrotic substrate corresponded to the Utah 2 (5-20%) and Utah 4 (>35%) stages, for M and S respectively, while the H state was modeled without fibrosis. Electrical propagation in the atria was modeled using the monodomain equation solved with openCARP. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm. A point was classified as inducing arrhythmia if reentry was initiated and 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) of the induced arrhythmia was assessed at the stimulation site. We compared the vulnerability ratio of the LA in monoatrial and biatrial configurations.
Results
The incorporation of the RA increased the mean LA vulnerability ratio by 115.79% (0.19±0.13 to 0.41±0.22, p=0.033) in state M, and 29.03% 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 5.51% (186.9±13.3ms to 197.2±18.3ms, p=0.006) in M scenario, and decreased it by 7.17% (224.3±27.6ms to 208.2±34.8ms, p=0.010) in scenario S. RA inclusion resulted in an elevated LA inducibility, revealing 4.9±3.3 additional points per patient in the LA for the biatrial model that did not induce reentry in the monoatrial model.
Conclusions
The LA vulnerability in a biatrial model differs from the LA vulnerability 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.
The LA vulnerability in a biatrial model differs from the LA vulnerability 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.