Changes in RR Series Characteristics During Atrial Fibrillation: An AV Node Simulation Study

Plappert, Felix; Wallman, Mikael; Platonov, Pyotr G.; Sandberg, Frida

doi:10.23919/cinc53138.2021.9662939

Cited by 1 publication

(1 citation statement)

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“…Previous studies have shown that the RR irregularity during AF change in response to changes in autonomic tone induced by drugs , Cygankiewicz et al 2015 and tilt-test (Patel et al 2018). However, it should be noted that increased RR irregularity may also be a direct effect of changes in atrial electrical activity as quantified by an increased AFR or variations in the atrioventricular node conduction (Plappert et al 2021). Out of the three HRV parameters considered, only SampEn was correlated with average AFR and had significant effect on AFR when considering the ME model.…”

Section: Discussionmentioning

confidence: 98%

Heart rate characteristic based modelling of atrial fibrillatory rate using implanted cardiac monitor data

et al. 2023

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Objective: The objective of the present study is to investigate the feasibility of using heart rate characteristics to estimate atrial fibrillatory rate (AFR) in a cohort of atrial fibrillation (AF) patients continuously monitored with an implantable cardiac monitor (ICM). We will use a mixed model approach to investigate population effect and patient specific effects of heart rate characteristics on AFR, and will correct for the effect of previous ablations, episode duration, and onset date and time. Approach: The f-wave signals, from which AFR is estimated, were extracted using a QRST cancellation process of the AF episodes in a cohort of 99 patients (67% male; 57±12 years) monitored for 9.2(0.2-24.3) months as median(min-max). The AFR from 2453 f-wave signals included in the analysis was estimated using a model-based approach. The association between AFR and heart rate characteristics, prior ablations, and episode-related features were modelled using fixed-effect and mixed-effect modelling approaches. Main Results: The mixed-effect models had a better fit to the data than fixed-effect models showing higher coefficients of determination (R2=0.49 vs R2=0.04) when relating the variations of AFR to the heart rate features. However, when correcting for the other factors, the mixed-effect model showed the best fit (R2=0.56). AFR was found to be significantly affected by previous catheter ablations (p<0.05), episode duration (p<0.05), and irregularity of the RR interval series (p<0.05). Significance: Mixed-effect models are more suitable for AFR modelling. AFR was shown to be faster in episodes with longer duration, less organized RR intervals and after several ablation procedures.

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Section: Discussionmentioning

confidence: 98%