Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability

Abstract

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.

Keywords: ARI index; cerebral blood flow; cerebral hemodynamics; transcranial Doppler; transfer function analysis.

FIGURE 1

(A) Gain LF results of TFA-like methods for repeated measurements. Top row: physiological data, bottom row: surrogate data. For each method group (TFA, Laguerre, Wavelet, IR-filter, and ARX) the results of similar methods are combined (Table 1). TFA: black dots are 10 methods (cm/s/mmHg), gray dots are 3 methods (%/% or %/mmHg); Laguerre: 4 methods (cm/s/mmHg); Wavelet: 1 method (cm/s/mmHg); IR-filter: 2 methods (%/%); ARX: 2 methods (cm/s/mmHg). See Supplementary Figures S1–S3 for Phase VLF/LF and Gain VLF. (B) ARI-like results of different methods for repeated measurements. Top row: physiological data, bottom row: surrogate data. For each method group (ARI/ARMA, ARX, IR-filter, and correlation) the results of similar methods are combined (Table 1). ARI: black dots are three methods (ARI 0–9 arbitrary units); gray dots are two methods (ARMA-ARI 0–9 arbitrary units); ARX: one method (ARX coefficient); IR-filter: one method (arbitrary units); correlation: two methods.

FIGURE 2

Bland–Altman plot of TFA-like parameters: gain VLF (top left), gain LF (top right), phase VLF (middle left), and phase LF (middle right); ARI-like parameters (bottom left); correlation-like parameters (bottom right). Units are similar to Figure 1A,B.

FIGURE 3

ICC values for methods using TFA or similar approaches with gain VLF and LF (top), phase VLF or LF (middle), and ARI or correlation-like methods (bottom). Results are shown per method (Table 1). ICC values <0.40: poor, between 0.40 and 0.59: fair, between 0.60 and 0.74: good, and between 0.75 and 1.00: excellent (Cicchetti, 1994).