OBJECTIVES: The aim of this study was to separate isthmus-dependent atrial flutter (IDAFL) from non-isthmus-dependent atrial flutter (NIDAFL) from the electrocardiogram (ECG) based on functional differences. BACKGROUND: The ECG analyses of F-wave shape suboptimally separate NIDAFL from IDAFL. The authors hypothesized that anatomic and functional differences may result in greater wavefront variability in NIDAFL than IDAFL, allowing their separation. The authors tested this hypothesis in patients undergoing ablation for atrial flutter using a novel ECG algorithm to detect subtle F-wave variability, validated by intracardiac measurements. METHODS: In 62 patients (23 NIDAFL, 39 IDAFL) ECG atrial wavefronts were represented as correlations of an F-wave template to the ECG over time. Correlations in orthogonal ECG lead-pairs were plotted at each time point to yield loops reflecting temporal and spatial regularity in each plane. The ECG analyses were compared with intracardiac standard deviations of: 1) atrial electrograms (temporal variability), and 2) bi-atrial activation time differences (spatial variability). RESULTS: Atrial ECG temporospatial loops were reproducible in IDAFL, but varied in NIDAFL (p < 0.01) suggesting greater variability that correctly classified IDAFL (39 of 39 cases) from NIDAFL (22 of 23 cases; p < 0.001). Intra-atrial mapping confirmed greater temporal variability for NIDAFL versus IDAFL, in lateral (p < 0.01) and septal (p = 0.03) right atrium, and proximal (p = 0.02) and distal (p < 0.01) coronary sinus. Spatial variability was greater in NIDAFL than IDAFL (p = 0.02). CONCLUSIONS: Greater cycle-to-cycle atrial wavefront variability separates NIDAFL from IDAFL and is detectable from the ECG using temporospatial analyses. These results have implications for guiding ablation and support the concept that IDAFL and NIDAFL lie along a spectrum of intracardiac organization.
OBJECTIVES: The aim of this study was to separate isthmus-dependent atrial flutter (IDAFL) from non-isthmus-dependent atrial flutter (NIDAFL) from the electrocardiogram (ECG) based on functional differences. BACKGROUND: The ECG analyses of F-wave shape suboptimally separate NIDAFL from IDAFL. The authors hypothesized that anatomic and functional differences may result in greater wavefront variability in NIDAFL than IDAFL, allowing their separation. The authors tested this hypothesis in patients undergoing ablation for atrial flutter using a novel ECG algorithm to detect subtle F-wave variability, validated by intracardiac measurements. METHODS: In 62 patients (23 NIDAFL, 39 IDAFL) ECG atrial wavefronts were represented as correlations of an F-wave template to the ECG over time. Correlations in orthogonal ECG lead-pairs were plotted at each time point to yield loops reflecting temporal and spatial regularity in each plane. The ECG analyses were compared with intracardiac standard deviations of: 1) atrial electrograms (temporal variability), and 2) bi-atrial activation time differences (spatial variability). RESULTS: Atrial ECG temporospatial loops were reproducible in IDAFL, but varied in NIDAFL (p < 0.01) suggesting greater variability that correctly classified IDAFL (39 of 39 cases) from NIDAFL (22 of 23 cases; p < 0.001). Intra-atrial mapping confirmed greater temporal variability for NIDAFL versus IDAFL, in lateral (p < 0.01) and septal (p = 0.03) right atrium, and proximal (p = 0.02) and distal (p < 0.01) coronary sinus. Spatial variability was greater in NIDAFL than IDAFL (p = 0.02). CONCLUSIONS: Greater cycle-to-cycle atrial wavefront variability separates NIDAFL from IDAFL and is detectable from the ECG using temporospatial analyses. These results have implications for guiding ablation and support the concept that IDAFL and NIDAFL lie along a spectrum of intracardiac organization.
Authors: David E Krummen; Mitul Patel; Hong Nguyen; Gordon Ho; Dhruv S Kazi; Paul Clopton; Marian C Holland; Scott L Greenberg; Gregory K Feld; Mitchell N Faddis; Sanjiv M Narayan Journal: J Cardiovasc Electrophysiol Date: 2010-11
Authors: Andrei Forclaz; Sanjiv M Narayan; Daniel Scherr; Nick Linton; Amir S Jadidi; Isabelle Nault; Lena Rivard; Shinsuke Miyazaki; Laurent Uldry; Matthew Wright; Ashok J Shah; Xingpeng Liu; Olivier Xhaet; Nicolas Derval; Sébastien Knecht; Frédéric Sacher; Pierre Jaïs; Mélèze Hocini; Michel Haïssaguerre Journal: Heart Rhythm Date: 2011-05-14 Impact factor: 6.343
Authors: Tina Baykaner; Rishi Trikha; Junaid A B Zaman; David E Krummen; Paul J Wang; Sanjiv M Narayan Journal: J Electrocardiol Date: 2017-01-15 Impact factor: 1.438