Dynamics of AV coupling during human atrial fibrillation: role of atrial rate

Abstract

The causal relationship between atrial and ventricular activities during human atrial fibrillation (AF) is poorly understood. This study analyzed the effects of an increase in atrial rate on the link between atrial and ventricular activities during AF. Atrial and ventricular time series were determined in 14 patients during the spontaneous acceleration of the atrial rhythm at AF onset. The dynamic relationship between atrial and ventricular activities was quantified in terms of atrioventricular (AV) coupling by AV synchrogram analysis. The technique identified n:m coupling patterns (n atrial beats in m ventricular cycles), quantifying their percentage, maximal length, and conduction ratio (= m/n). Simulations with a difference-equation AV model were performed to correlate the observed dynamics to specific atrial/nodal properties. The atrial rate increase significantly affected AV coupling and ventricular response during AF. The shortening of atrial intervals from 185 ± 32 to 165 ± 24 ms (P < 0.001) determined transitions toward AV patterns with progressively decreasing m/n ratios (from conduction ratio = 0.34 ± 0.09 to 0.29 ± 0.08, P < 0.01), lower occurrence (from percentage of coupled beats = 27.1 ± 8.0 to 21.8 ± 6.9%, P < 0.05), and higher instability (from maximal length = 3.9 ± 1.5 to 2.8 ± 0.7 s, P < 0.01). Advanced levels of AV block and coupling instability at higher atrial rates were associated with increased ventricular interval variability (from 123 ± 52 to 133 ± 55 ms, P < 0.05). AV pattern transitions and coupling instability in patients were predicted, assuming the filtering of high-rate irregular atrial beats by the slow recovery of nodal excitability. These results support the role of atrial rate in determining AV coupling and ventricular response and may have implications for rate control in AF.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series

“…First is a short time scale clustering of RR intervals, embedded in the overall map. These additional clusters correspond to alternation of RR intervals which has been noted before in some cases of AF [ 25 , 39 , 42 ], but remains to be mechanistically explained and clinically investigated further. One possible explanation is dual-pathway AV conduction [ 25 , 39 ]; another is a Wenckebach variant of conduction block in the AV node.…”

Computational Simulation of Cardiac Function and Blood Flow in the Circulatory System under Continuous Flow Left Ventricular Assist Device Support during Atrial Fibrillation

“…The cardiovascular system model utilises patient-derived series to simulate AF. Incorporating dynamical models that simulate electrocardiogram signals or dual pathway atrioventricular nodal conduction [47][48][49][50] with the existing cardiovascular system model may allow to simulate the dependence of the cardiovascular response on the rate and regularity of ventricular activation in AF [51]. Elaborating the cardiovascular system model to simulate atrioventricular nodal dynamics and the evaluation of CF-LVAD support and validation of the results will be future work.…”

A Divergence-Based Approach for the Identification of Atrial Fibrillation Focal Drivers From Multipolar Mapping: A Computational Study

“…Electrograms with inadequate signal-to-noise ratio were excluded from subsequent analysis. Activation time series were automatically extracted from each bipolar electrogram as previously reported (Faes et al, 2002;Masè et al, 2015). Briefly, electrograms were pre-processed to remove ventricular interference, local atrial activation waves were identified by signal filtering and adaptive threshold crossing (Faes et al, 2002;Masè et al, 2015) and atrial activation times were estimated by measuring the barycenter of local activation waves (Faes et al, 2002).…”

“…Indeed, although the use of an interpolation approach allows flexibility for pattern reconstruction, interpolation is more sensitive to activation time misdetections and noise effects, with respect to techniques based on model fitting (Bayly et al, 1998;Fitzgerald et al, 2003;Weber et al, 2010;Alcaine et al, 2014) or approaches that do not require activation time detection (Richter et al, 2011;Rodrigo et al, 2016;Alcaine et al, 2017;Luengo et al, 2019;Handa et al, 2020). In order to reduce inaccuracy in activation time estimations, activation waveforms from patient data were automatically identified by a well-established technique (Botteron and Smith, 1995;Faes et al, 2002), and activation times were set at the waveform barycenter (Faes et al, 2002;Masè et al, 2005Masè et al, , 2015. As suggested in several works (Holm et al, 1996;Faes et al, 2002;El Haddad et al, 2013;, the use of a morphology-based activation detection, such as the barycenter method, improves estimation accuracy in the presence of fragmented electrograms.…”