Alex Baher1,2, Benjamin Buck3, Manuel Fanarjian3, J Paul Mounsey4, Anil Gehi3, Eugene Chung3, Fadi G Akar5, Charles L Webber6, Joseph G Akar2, James P Hummel7. 1. Section of Cardiovascular Medicine, Department of Medicine, University of Utah, Salt Lake City, Utah. 2. Section of Cardiovascular Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut. 3. Division of Cardiology, University of North Carolina, Chapel Hill, North Carolina. 4. Department of Cardiovascular Sciences, Brody School of Medicine, East Carolina University, Greenville, North Carolina. 5. Cardiovascular Research Center, Icahn School of Medicine at Mount Sinai, New York, New York. 6. Department of Cell and Molecular Physiology, Loyola University, Maywood, Illinois. 7. Division of Cardiovascular Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
Abstract
OBJECTIVES: To differentiate electrograms representing sites of active atrial fibrillation (AF) drivers from passive ones. BACKGROUND: Ablation of complex-fractionated atrial electrograms (CFAEs) is controversial due to difficulty in distinguishing CFAEs representing sites of active AF drivers from passive mechanisms. We hypothesized that active CFAE sites exhibit repetitive wavefront directionality, thereby inscribing an electrogram conformation (Egm-C) that is more recurrent compared with passive CFAE sites; and that can be differentiated from passive CFAEs using nonlinear recurrence quantification analysis (RQA). METHODS: We developed multiple computer models of active CFAE mechanisms (ie, rotors) and passive CFAE mechanisms (ie, wavebreak, slow conduction, and double potentials). CFAE signals were converted into discrete time-series representing Egm-C. The RQA algorithm was used to compare signals derived from active CFAE sites to those from passive CFAEs sites. The RQA algorithm was then applied to human CFAE signals collected during AF ablation (n = 17 patients). RESULTS: RQA was performed in silico on simulated bipolar CFAEs within active (n = 45) and passive (n = 60) areas. Recurrence of Egm-C was significantly higher in active compared with passive CFAE sites (31.8% ± 19.6% vs 0.3% ± 0.5%, respectively, P < .0001) despite no difference in mean cycle length (CL). Similarly, for human AF (n = 39 signals), Egm-C recurrence was higher in active vs passive CFAE areas despite similar CLs (%recurrence 13.6% ± 15.5% vs 0.1% ± 0.3%, P < .002; mean CL 102.5 ± 14.3 vs 106.6 ± 14.4, P = NS). CONCLUSION: Active CFAEs critical to AF maintenance exhibit higher Egm-C recurrence and can be differentiated from passive bystander CFAE sites using RQA.
OBJECTIVES: To differentiate electrograms representing sites of active atrial fibrillation (AF) drivers from passive ones. BACKGROUND: Ablation of complex-fractionated atrial electrograms (CFAEs) is controversial due to difficulty in distinguishing CFAEs representing sites of active AF drivers from passive mechanisms. We hypothesized that active CFAE sites exhibit repetitive wavefront directionality, thereby inscribing an electrogram conformation (Egm-C) that is more recurrent compared with passive CFAE sites; and that can be differentiated from passive CFAEs using nonlinear recurrence quantification analysis (RQA). METHODS: We developed multiple computer models of active CFAE mechanisms (ie, rotors) and passive CFAE mechanisms (ie, wavebreak, slow conduction, and double potentials). CFAE signals were converted into discrete time-series representing Egm-C. The RQA algorithm was used to compare signals derived from active CFAE sites to those from passive CFAEs sites. The RQA algorithm was then applied to human CFAE signals collected during AF ablation (n = 17 patients). RESULTS: RQA was performed in silico on simulated bipolar CFAEs within active (n = 45) and passive (n = 60) areas. Recurrence of Egm-C was significantly higher in active compared with passive CFAE sites (31.8% ± 19.6% vs 0.3% ± 0.5%, respectively, P < .0001) despite no difference in mean cycle length (CL). Similarly, for humanAF (n = 39 signals), Egm-C recurrence was higher in active vs passive CFAE areas despite similar CLs (%recurrence 13.6% ± 15.5% vs 0.1% ± 0.3%, P < .002; mean CL 102.5 ± 14.3 vs 106.6 ± 14.4, P = NS). CONCLUSION: Active CFAEs critical to AF maintenance exhibit higher Egm-C recurrence and can be differentiated from passive bystander CFAE sites using RQA.
Authors: Christopher O'Shea; James Winter; Andrew P Holmes; Daniel M Johnson; Joao N Correia; Paulus Kirchhof; Larissa Fabritz; Kashif Rajpoot; Davor Pavlovic Journal: Prog Biophys Mol Biol Date: 2019-12-30 Impact factor: 3.667