Heartbeat dynamics in adrenergic blocker treated conscious beagle dogs

Abstract

Introduction Adrenergic blockade as a treatment for chronic heart failure (CHF) has proved effective, but its pharmacological mechanism on CHF remains unclear. In the past two decades, studies on heart rate variability (HRV) have reported that CHF patients generally have a reduced temporal complexity in heart rate variability. On the other hand, adrenergic blockers have been shown to restore such complexity. Fractal analysis is a novel and efficient tool to explore the adrenergic blockade effect on HRV. This paper applies the detrended fluctuation analysis (DFA) and multifractal DFA (MF-DFA) methods in an attempt to understand the effect of adrenergic blockade on cardiac dynamics in conscious beagle dogs. Methods DFA and MF-DFA analysis are conducted on RR interval data generated from telemetry instrumented dogs receiving a combination of 15 mg/kg nadolol and 5 mg/kg phenoxybenzamine orally administered at the 22nd and 34th hour in a parallel design ( n = 12). All dogs had approximately 48 h of beat-to-beat heart rate measurements recorded in the left ventricle. Complexity measures for heartbeat series are compared between the blocker and vehicle group. We also compute traditional statistics for HRV and spectral parameters and examine their correlation with fractal analysis. Results When compared to the vehicle group, the adrenergic blocker group had: 1) longer RR intervals ( p = 0.02) and lower beat-to-beat variability ( p = 0.04); 2) decreased low frequency (LF) and high frequency (HF) power ( p = 0.03), and higher LF-to-HF ratio; 3) larger middle-range scaling exponents ( p < 0.01); 4) broader multifractal spectra ( p = 0.03) with higher dominant singularity indices ( p = 0.02). Discussion Our results show that 1) adrenergic blockade alters the sympathovagal balance; 2) adrenergic blockers enhance the complexity of the cardiac dynamics; 3) the adrenergic blockade effect on cardiac dynamics is primarily the attenuation of small fluctuations in RR intervals. Fractal analysis also has the potential to be applied to early QT diagnosis.