Lack of linear correlation between dynamic and steady‐state cerebral autoregulation

KEY POINTS: For correct application and interpretation of cerebral autoregulation (CA) measurements in research and in clinical care, it is essential to understand differences and similarities between dynamic and steady‐state CA. The present study found no correlation between dynamic and steady‐state CA indices in healthy older adults. There was variability between individuals in all (steady‐state and dynamic) autoregulatory indices, ranging from low (almost absent) to highly efficient CA in this healthy population. These findings challenge the assumption that assessment of a single CA parameter or a single set of parameters can be generalized to overall CA functioning. Therefore, depending on specific research purposes, the choice for either steady‐state or dynamic measures or both should be weighed carefully. ABSTRACT: The present study aimed to investigate the relationship between dynamic (dCA) and steady‐state cerebral autoregulation (sCA). In 28 healthy older adults, sCA was quantified by a linear regression slope of proportionate (%) changes in cerebrovascular resistance (CVR) in response to proportionate (%) changes in mean blood pressure (BP) induced by stepwise sodium nitroprusside (SNP) and phenylephrine (PhE) infusion. Cerebral blood flow (CBF) was measured at the internal carotid artery (ICA) and vertebral artery (VA) and CBF velocity at the middle cerebral artery (MCA). With CVR = BP/CBF, Slope‐CVR(ICA), Slope‐CVR(VA) and Slope‐CVRi(MCA) were derived. dCA was assessed (i) in supine rest, analysed with transfer function analysis (gain and phase) and autoregulatory index (ARI) fit from spontaneous oscillations (ARI(Baseline)), and (ii) with transient changes in BP using a bolus injection of SNP (ARI(SNP)) and PhE (ARI(PhE)). Comparison of sCA and dCA parameters (using Pearson's r for continuous and Spearman's ρ for ordinal parameters) demonstrated a lack of linear correlations between sCA and dCA measures. However, comparisons of parameters within dCA and within sCA were correlated. For sCA slope‐CVR(VA) with Slope‐CVRi(MCA) (r = 0.45, P < 0.03); for dCA ARI(SNP) with ARI(PhE) (ρ = 0.50, P = 0.03), ARI(Baseline) (ρ = 0.57, P = 0.03) and Phase(LF) (ρ = 0.48, P = 0.03); and for Gain(VLF) with Gain(LF) (r = 0.51, P = 0.01). By contrast to the commonly held assumption based on an earlier study, there were no linear correlations between sCA and dCA. As an additional observation, there was strong inter‐individual variability, both in dCA and sCA, in this healthy group of elderly, in a range from low to high CA efficiency.