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Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation

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journal contribution
posted on 25.01.2016, 11:05 by Tiago P. Almeida, Gavin S. Chu, J. L. Salinet, Frederique J. Vanheusden, Xin Li, Jiun H. Tuan, Peter J. Stafford, G. André Ng, Fernando S. Schlindwein
Ablation of persistent atrial fibrillation (persAF) targeting complex fractionated atrial electrograms (CFAEs) detected by automated algorithms has produced conflicting outcomes in previous electrophysiological studies. We hypothesize that the differences in these algorithms could lead to discordant CFAE classifications by the available mapping systems, giving rise to potential disparities in CFAE-guided ablation. This study reports the results of a head-to-head comparison of CFAE detection performed by NavX (St. Jude Medical) versus CARTO (Biosense Webster) on the same bipolar electrogram data (797 electrograms) from 18 persAF patients. We propose revised thresholds for both primary and complementary indices to minimize the differences in CFAE classification performed by either system. Using the default thresholds [NavX: CFE-Mean ≤ 120 ms; CARTO: ICL ≥ 7], NavX classified 70 % of the electrograms as CFAEs, while CARTO detected 36 % (Cohen’s kappa κ ≈ 0.3, P < 0.0001). Using revised thresholds found using receiver operating characteristic curves [NavX: CFE-Mean ≤ 84 ms, CFE-SD ≤ 47 ms; CARTO: ICL ≥ 4, ACI ≤ 82 ms, SCI ≤ 58 ms], NavX classified 45 %, while CARTO detected 42 % (κ ≈ 0.5, P < 0.0001). Our results show that CFAE target identification is dependent on the system and thresholds used by the electrophysiological study. The thresholds found in this work counterbalance the differences in automated CFAE classification performed by each system. This could facilitate comparisons of CFAE ablation outcomes guided by either NavX or CARTO in future works.

History

Citation

Medical & Biological Engineering & Computing, 2016, doi: 10.1007/s11517-016-1456-2

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

VoR (Version of Record)

Published in

Medical & Biological Engineering & Computing

issn

0140-0118

eissn

1741-0444

Acceptance date

19/12/2015

Copyright date

2016

Available date

08/03/2016

Publisher version

http://link.springer.com/article/10.1007/s11517-016-1456-2

Language

en