Performance of atrial conduction velocity algorithms: a comparative in-silico study - Gharaviri, A; Bodagh, N; Kotaida, I; Baptiste, T; Corrado, C; Sim, I; Lemus, J A S; Niederer, S; Whitaker, J; O Neill, M; Williams, S E (Europace (London, England), 2023)
Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Background Atrial conduction velocity is a key determinant of re-entry. Measurement of conduction velocity from electroanatomic mapping data is challenging due to sparsity, distribution, and uncertainty of measurements. Several mathematical methods have been proposed to address these challenges but their relative sensitivity to the typical uncertainty of clinical data is unknown. Furthermore, prior clinical studies utilising these algorithms provide conflicting results. Purpose To assess the performance of conduction velocity methods (triangulation, planar fitting, and radial basis function interpolation) for the calculation of atrial conduction velocity. Method Atrial activation was simulated with known conduction velocity using the monodomain model, Courtemanche cell model and atrial geometry derived from cardiac magnetic resonance imaging with mapped fibre orientations. The model was paced from the pulmonary veins, the mid coronary sinus, and the insertion of Bachmann's bundle. Reconstructed electroanatomic maps were used for conduction velocity calculation in OpenEP[1]. To test the robustness of algorithms to electrogram sampling density, simulated sampling densities were reduced from 16.45 to 8.43 points/cm2 (1950-1000 points/map). To test the robustness to uncertainty in local activation time assignment, random uniform noises with the maximum amplitude of ± 20ms was added to the local activation times from simulations. Mean squared velocity error was calculated using the differences between the ground truth and estimated conduction velocities. Result In electroanatomic maps recreated with high electrogram sampling density (16.45 points/cm2), planar fitting showed a slightly higher error compared to triangulation and the radial basis function methods (0.16 ± 0.03 m/s, 0.11 ± 0.01 m/s and 0.12 ± 0.03 m/s, respectively, P < 0.05). As mapping density was decreased, the relative performance of the planar fitting method increased and in low-density maps (13.5 to 8.43 points/cm2) the planar fitting method resulted in the lowest error. When local activation times were accurately assigned, all methods performed well. However, when uncertainty in local activation time assignment increased, conduction velocities calculated using planar fitting had a significantly lower error compared to those calculated with the triangulation and radial basis function methods (0.19 ± 0.03 m/s, 0.36 ± 0.04 and 0.29 ± 0.04 m/s, respectively, P < 0.05). Conclusion This study highlights the importance of spatial and temporal accuracy for the assessment of conduction velocity. At high electroanatomic mapping sampling densities (>16.9 points/cm2), all methods performed well. Below 13.5 points/cm2 (~1600 points per map), the planar fitting method outperformed the triangulation and radial basis function methods. The differential effects of clinical measurement uncertainty on the accuracy of each method may explain the conflicting results of prior studies.