The effect of head‐up tilt upon markers of heart rate variability in patients with atrial fibrillation

Patel, Hitesh; Hayward, Carl; Wardle, Andrew J; Middleton, Lee J; Lyon, Alexander R.; Mario, Carlo Di; Salukhe, Tushar V.; Sutton, Richard; Rosen, Stuart D.

doi:10.1111/anec.12511

Cited by 5 publications

(8 citation statements)

References 34 publications

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“…Access to data from patients with AF during other tilt-inclinations could potentially be used to refine the model to take the degree on inclination into account. The tilt-induced changes in RR series irregularity observed in the present study are in line with the results in Patel et al (2018) , where a decrease in RR sample entropy in response to HUT in patients with AF was reported. The tilt-induced changes in RR series irregularity observed in the present study are also in line with the changes reported for patients in normal sinus rhythm during HUT ( Porta et al, 2007 ).…”

Section: Discussionsupporting

confidence: 93%

“…While ANS modulation has been extensively studied during normal sinus rhythm ( Porta et al, 2007 ; Porta et al, 2015 ; Sassi et al, 2015 ; Patel et al, 2018 ), no attempts have been made towards the estimation of ANS modulation during persistent AF. The present study is a first step towards developing a model of the AV node that will ultimately be used to quantify ANS modulation on a patient specific basis by fitting to RR interval series and information on atrial electrical activity obtained from clinical ECG recordings.…”

Section: Discussionmentioning

confidence: 99%

“…The effect of the ANS-induced activity was investigated with respect to its ability to mimic the population-averaged changes observed during tilt test. The RMSSD and sample entropy were used to quantify RR series variability and irregularity, respectively, since these statistical measures have been used in previous studies to assess changes in RR series characteristics during AF in response to drugs ( Corino et al, 2015 ) and tilt-test ( Patel et al, 2018 ). Population-averaged trends were chosen over the trends of individual patients to reduce the uncertainty in the estimation of the clinical RR V and RR I trends.…”

Section: Discussionmentioning

confidence: 99%

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An atrioventricular node model incorporating autonomic tone

et al. 2022

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The response to atrial fibrillation (AF) treatment is differing widely among patients, and a better understanding of the factors that contribute to these differences is needed. One important factor may be differences in the autonomic nervous system (ANS) activity. The atrioventricular (AV) node plays an important role during AF in modulating heart rate. To study the effect of the ANS-induced activity on the AV nodal function in AF, mathematical modelling is a valuable tool. In this study, we present an extended AV node model that incorporates changes in autonomic tone. The extension was guided by a distribution-based sensitivity analysis and incorporates the ANS-induced changes in the refractoriness and conduction delay. Simulated RR series from the extended model driven by atrial impulse series obtained from clinical tilt test data were qualitatively evaluated against clinical RR series in terms of heart rate, RR series variability and RR series irregularity. The changes to the RR series characteristics during head-down tilt were replicated by a 10% decrease in conduction delay, while the changes during head-up tilt were replicated by a 5% decrease in the refractory period and a 10% decrease in the conduction delay. We demonstrate that the model extension is needed to replicate ANS-induced changes during tilt, indicating that the changes in RR series characteristics could not be explained by changes in atrial activity alone.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An atrioventricular node model incorporating autonomic tone

et al. 2022

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“…In our study, the effect the regularity of the RR series has in modelling AFR was further evaluated in the ¢ ME model showing a significant effect (b ¢ 3 = 0.105, p < 0.05). 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).…”

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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“…[ 17 18 ] An increase in LF was seen in their studies which were a result mechanism reverse of above. [ 19 ]…”

Section: Discussionmentioning

confidence: 99%

Effect of head down tilt on heart rate variability

Malhotra

Thakare

Hulke

et al. 2021

Journal of Family Medicine and Primary Care

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Context: Sirshasan is supposed to increase blood flow to the brain and considered to be beneficial for intellectual function, however mastering these techniques may be difficult. Aims: To see the effect of headstand using a tilt table on heart rate variability (HRV). Settings and Design: A cross-sectional study that was done on 26 healthy volunteers. Methods and Material: HRV was assessed in the supine position and 30° head tilt position for 5 min. HRV recording was done on the power lab (AD Instruments P Ltd, Castle Hill Australia). The tilt table used was Medica Podium, New Delhi, HLT-200. Statistical analysis used: Paired t -test. Results: All the HRV parameters showed non-significant change except low-frequency parameters which showed significant change during head tilt. Conclusions: Headstand to a 30° using tilt table cause a decrease in the autonomic activity which is mainly because of decrease in sympathetic activity.

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The effect of head‐up tilt upon markers of heart rate variability in patients with atrial fibrillation

Cited by 5 publications

References 34 publications

An atrioventricular node model incorporating autonomic tone

An atrioventricular node model incorporating autonomic tone

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

Effect of head down tilt on heart rate variability

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