INTRODUCTION: Manual, point-by-point electroanatomical mapping requires the operator to directly evaluate each point during map construction. Consequently, point collection can be a slow process. An automated 3D mapping system was developed with the goal of improving key mapping metrics, including map completion time and point density. METHODS: Automated 3D mapping software that includes morphology and cycle length discrimination functions for surface and intracardiac electrograms was developed. In five swine, electroanatomical maps (EAMs) of all four cardiac chambers were generated in sinus rhythm. Four catheters were used: two different four-pole ablation catheters, a 20-pole circular catheter, and a 64-pole basket catheter. Automated and manual 3D mapping were compared for 12 different catheter-chamber combinations (paired sets of 10 maps for most combinations, for a total of 156 maps). RESULTS: Automated 3D mapping produced more than twofold increase in the number of points per map, as compared with manual 3D mapping (P ≤0.007 for all catheter-chamber combinations tested). Automated 3D mapping also reduced map completion time by an average of 29% (P < 0.05 for all comparisons). The amount of manual editing of the maps acquired with automated 3D mapping was minimal. CONCLUSION: Automated 3D mapping with the open-platform mapping software described in this study is significantly faster than manual, point-by-point 3D mapping. This resulted in shorter mapping time and higher point density. The morphology discrimination functions effectively excluded ectopic beats during mapping in sinus rhythm and allowed for rapid mapping of intermittent ventricular ectopic beats.
INTRODUCTION: Manual, point-by-point electroanatomical mapping requires the operator to directly evaluate each point during map construction. Consequently, point collection can be a slow process. An automated 3D mapping system was developed with the goal of improving key mapping metrics, including map completion time and point density. METHODS: Automated 3D mapping software that includes morphology and cycle length discrimination functions for surface and intracardiac electrograms was developed. In five swine, electroanatomical maps (EAMs) of all four cardiac chambers were generated in sinus rhythm. Four catheters were used: two different four-pole ablation catheters, a 20-pole circular catheter, and a 64-pole basket catheter. Automated and manual 3D mapping were compared for 12 different catheter-chamber combinations (paired sets of 10 maps for most combinations, for a total of 156 maps). RESULTS: Automated 3D mapping produced more than twofold increase in the number of points per map, as compared with manual 3D mapping (P ≤0.007 for all catheter-chamber combinations tested). Automated 3D mapping also reduced map completion time by an average of 29% (P < 0.05 for all comparisons). The amount of manual editing of the maps acquired with automated 3D mapping was minimal. CONCLUSION: Automated 3D mapping with the open-platform mapping software described in this study is significantly faster than manual, point-by-point 3D mapping. This resulted in shorter mapping time and higher point density. The morphology discrimination functions effectively excluded ectopic beats during mapping in sinus rhythm and allowed for rapid mapping of intermittent ventricular ectopic beats.
Authors: Jonathan C Hsu; Douglas Darden; Benedict M Glover; B Judson Colley; Christian Steinberg; Bernard Thibault; Coty Jewell; Michael Bernard; Paul B Tabereaux; Usman Siddiqui; Jingyun Li; Eric E Horvath; Daniel Cooper; David Lin Journal: J Interv Card Electrophysiol Date: 2022-05-10 Impact factor: 1.759