Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study

Abstract

Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings, have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index , ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way Intraclass Correlation Coefficient analysis (ICC). Main results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p=0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p <0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods , ICC was 0.21 (0.21; [0.056-0.35]). Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability?

“…This procedure resulted in N = 55 left sides, N = 71 right sides, and N = 22 artificial sides. The results for the artificial and physiological data have been presented elsewhere, and will not be reported here [21]. Since there were no significant differences between left and right sided values for all DCA variables examined in this study, left and right sides were averaged in case of bilateral measurement, or were used singly in case of unilateral missing data.…”

“…The main focus of this study was to evaluate any differences in reproducibility between the different analysis strategies that have been developed. Initially, surrogate data were used to isolate the separate effects of modelling method from physiological variability [21]. In the second analysis, physiological data were used to quantify reproducibility of several DCA analysis methods on physiological data [22].…”

Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability

“…This limitation was addressed in the current multi-center study. An initial report (Sanders et al, 2018) described the influence of different methods of analysis on the reproducibility of synthetic data, where surrogate time-series of CBF velocity (CBFv) were generated based on real measurements of BP, coupled with a realistic signal-to-noise ratio. These generated CBFv data were based on a linear model.…”

Can we use short recordings for assessment of dynamic cerebral autoregulation? A sensitivity analysis study in acute ischaemic stroke and healthy subjects

“…Finally, it is important to emphasize that the results of this study are limited to the use of TFA within the set of parameters described above and cannot be extended to other approaches for modelling the BP-CBFV dynamic relationship such as time-domain techniques (Czosnyka et al, 2008;Mahdi et al, 2017;Nogueira et al, 2013;Panerai et al, 2001;Panerai et al, 2016b;Sanders et al, 2018;Tzeng et al, 2012) or closed-loop models as proposed by Marmarelis et al (2013).…”