Comparison of Heart Autonomic Control between Hemodynamically Stable and Unstable Patients during Hemodialysis Sessions: A Bayesian Approach

Abstract

Intradialytic hypotension is a common complication during hemodialysis sessions. The analysis of successive RR interval variability using nonlinear methods represents a promising tool for evaluating the cardiovascular response to acute volemic changes. Thus, the present study aims to compare the variability of successive RR intervals between hemodynamically stable (HS) and unstable (HU) patients during a hemodialysis session, through linear and nonlinear methods. Forty-six chronic kidney disease patients volunteered in this study. Successive RR intervals and blood pressures were recorded throughout the hemodialysis session. Hemodynamic stability was defined based on the delta of systolic blood pressure (higher SBP-lower SBP). The cutoff for hemodynamic stability was defined as 30 mm Hg, and patients were stratified as: HS ([n = 21]: ≤29.9 mm Hg) or HU ([n = 25]: ≥30 mm Hg). Linear methods (low-frequency [LFnu] and high-frequency [HFnu] spectra) and nonlinear methods (multiscale entropy [MSE] for Scales 1–20, and fuzzy entropy) were applied. The area under the MSE curve at Scales 1–5 (MSE1–5), 6–20 (MSE6–20), and 1–20 (MSE1–20) were also used as nonlinear parameters. Frequentist and Bayesian inferences were applied to compare HS and HU patients. The HS patients exhibited a significantly higher LFnu and lower HFnu. For MSE parameters, Scales 3–20 were significantly higher, as well as MSE1–5, MSE6–20, and MSE1–20 in HS, when compared to HU patients (p < 0.05). Regarding Bayesian inference, the spectral parameters demonstrated an anecdotal (65.9%) posterior probability favoring the alternative hypothesis, while MSE exhibited moderate to very strong probability (79.4 to 96.3%) at Scales 3–20, and MSE1–5, MSE6–20, and MSE1–20. HS patients exhibited a higher heart-rate complexity than HU patients. In addition, the MSE demonstrated a greater potential than spectral methods to differentiate variability patterns in successive RR intervals.