Abstract
Experimental models have demonstrated that atrial fibrillation (AF) may be due to one or more rapid drivers (source) producing AF. These drivers may be characterized by rapid and regular cycle lengths (CLs), producing fibrillatory conduction to the rest of the atria. The ability to reliably identify such drivers would be invaluable. The purpose of this study was to develop and validate a CL variability detection (CLVD) analysis capable of accurately determining beat-to-beat CLs of atrial electrograms (AEGs) during AF, and then to compare this analysis with dominant frequency (DF) analysis. We analyzed 6 episodes of AF in 6 dogs (sterile pericarditis model) due either to a single, stable left atrial reentrant circuit, or unstable reentrant circuits causing fibrillatory conduction to the rest of the atria. During AF, AEGs were recorded simultaneously from 400 to 420 electrodes on both atria. CLs from over 20,000 AEGs were manually measured, and compared to CLs detected using both the CLVD and DF analyses. There was significant correlation between (1) CLs measured manually and the CLVD analysis (mean CL: correlation coefficient [CC]= 0.96, standard deviation [SD]: CC = 0.89); and (2) mean CL measured manually and the DF analysis (CC = 0.84). However, there was poor correlation between SD of CLs measured manually and the organization index (OI) by DF analysis (CC =-0.59). The CLVD analysis was validated as being accurate for detecting both rate and degree of regularity of AEGs during AF, and more accurate than DF analysis.
Published Version
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