Autonomic Nervous System Activity before Atrial Fibrillation Onset as Assessed by Heart Rate Variability.

Abstract

Background: Neuromodulation has been shown to increase the efficacy of atrial fibrillation (AF) ablation procedures. However, despite its ability to influence the autonomic nervous system (ANS), the exact mechanism of action remains unclear. The activity of the ANS via the intracardiac nervous system (ICNS) can be inferred from heart rate variability (HRV). Therefore, this study aims to investigate the significance of changes in the ICNS prior to the onset of AF by analyzing the evolution of HRV in a large new cohort of patients.

Methods: We selected and annotated recordings with AF and atrial flutter from our database of 95,871 Holter recordings. Each recording included both sinus rhythm and one or more AF episodes. We computed parameters estimating parasympathetic activity (root mean square of successive RR interval differences (RMSSD) and percentage of successive RR intervals that differ by more than 50 ms (pNN50)), as well as HRV frequential parameters a few minutes before AF onset. To allow a minute-by-minute assessment of the parameter changes, we computed their values over 5-minute sliding windows, starting at 35 minutes before AF onset.

Results: The mean age of the whole group of patients was 71.1 ± 11.3 years (range 35-99), the total number of episodes was 1319 on 623 recordings from 570 patients, with an average of 2.1 ± 2.2 episodes per recording (range 1-17) and 2.3 ± 2.6 episodes per patient (range 1-21). The proportion of premature atrial contractions (PACs) increased from 4.8 ± 0.3%, 35 minutes before the onset of AF to 8.3 ± 0.4%, 5 minutes before the AF episode. We measured a statistically significant increase in very-low-frequency (VLF), low-frequency (LF), high-frequency (HF), RMSSD and pNN50 between 35 minutes and 5 minutes before AF onset.

Conclusions: Our data suggest that a significant short-term increase in vagal activity precedes most AF events. Dynamic changes in HRV parameters could be considered when determining the optimal neuromodulation strategies.