The specific representation of fibrosis in computer models, such as percolation and fibroblast coupling, significantly affects rotor dynamics and must be carefully considered to match clinical data.
How do different computational modelling methodologies of atrial fibrosis affect rotor dynamics and electrograms compared to clinically observed reentrant behaviour?
The specific computational representation of atrial fibrosis significantly impacts simulated rotor dynamics, highlighting the need for careful methodology selection in patient-specific AF modelling.
AIMS: Catheter ablation is an effective technique for terminating atrial arrhythmia. However, given a high atrial fibrillation (AF) recurrence rate, optimal ablation strategies have yet to be defined. Computer modelling can be a powerful aid but modelling of fibrosis, a major factor associated with AF, is an open question. Several groups have proposed methodologies based on imaging data, but no comparison to determine which methodology best corroborates clinically observed reentrant behaviour has been performed. We examined several methodologies to determine the best method for capturing fibrillation dynamics. METHODS AND RESULTS: Patient late gadolinium-enhanced magnetic resonance imaging data were transferred onto a bilayer atrial computer model and used to assign fibrosis distributions. Fibrosis was modelled as conduction disturbances (lower conductivity, edge splitting, or percolation), transforming growth factor-β1 ionic channel effects, myocyte-fibroblast coupling, and combinations of the preceding. Reentry was induced through pulmonary vein ectopy and the ensuing rotor dynamics characterized. Non-invasive electrocardiographic imaging data of the patients in AF was used for comparison. Electrograms were computed and the fractionation durations measured over the surface. Edge splitting produced more phase singularities from wavebreaks than the other representations. The number of phase singularities seen with percolation was closer to the clinical values. Addition of fibroblast coupling had an organizing effect on rotor dynamics. Simple tissue conductivity changes with ionic changes localized rotors over fibrosis which was not observed with clinical data. CONCLUSION: The specific representation of fibrosis has a large effect on rotor dynamics and needs to be carefully considered for patient specific modelling.
Roney et al. (2016) studied Atrial fibrillation. Fibrosis modelling methodologies (conduction disturbances, ionic channel effects, myocyte-fibroblast coupling) vs. Clinical non-invasive electrocardiographic imaging data was evaluated on Rotor dynamics and electrogram fractionation (number of phase singularities). The specific representation of fibrosis in computer models, such as percolation and fibroblast coupling, significantly affects rotor dynamics and must be carefully considered to match clinical data.