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

Daan L.K. de Jong, Takashi Tarumi, Jie Liu, Rong Zhang, Jurgen A.H.R. Claassen

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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-CVRICA, Slope-CVRVA and Slope-CVRiMCA 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 (ARIBaseline), and (ii) with transient changes in BP using a bolus injection of SNP (ARISNP) and PhE (ARIPhE). 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-CVRVA with Slope-CVRiMCA (r = 0.45, P < 0.03); for dCA ARISNP with ARIPhE (ρ = 0.50, P = 0.03), ARIBaseline (ρ = 0.57, P = 0.03) and PhaseLF (ρ = 0.48, P = 0.03); and for GainVLF with GainLF (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.

Original languageEnglish (US)
Pages (from-to)5623-5636
Number of pages14
JournalJournal of Physiology
Volume595
Issue number16
DOIs
StatePublished - Aug 15 2017

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Homeostasis
Cerebrovascular Circulation
Nitroprusside
Phenylephrine
Blood Pressure
Vertebral Artery
Blood Flow Velocity
Middle Cerebral Artery
Internal Carotid Artery
Research
Linear Models
Observation
Injections

Keywords

  • cerebral blood flow
  • cerebral circulation
  • dynamic cerebral autoregulation
  • physiological variability
  • static cerebral autoregulation

ASJC Scopus subject areas

  • Physiology

Cite this

Lack of linear correlation between dynamic and steady-state cerebral autoregulation. / de Jong, Daan L.K.; Tarumi, Takashi; Liu, Jie; Zhang, Rong; Claassen, Jurgen A.H.R.

In: Journal of Physiology, Vol. 595, No. 16, 15.08.2017, p. 5623-5636.

Research output: Contribution to journalArticle

de Jong, Daan L.K. ; Tarumi, Takashi ; Liu, Jie ; Zhang, Rong ; Claassen, Jurgen A.H.R. / Lack of linear correlation between dynamic and steady-state cerebral autoregulation. In: Journal of Physiology. 2017 ; Vol. 595, No. 16. pp. 5623-5636.
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N2 - 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-CVRICA, Slope-CVRVA and Slope-CVRiMCA 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 (ARIBaseline), and (ii) with transient changes in BP using a bolus injection of SNP (ARISNP) and PhE (ARIPhE). 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-CVRVA with Slope-CVRiMCA (r = 0.45, P < 0.03); for dCA ARISNP with ARIPhE (ρ = 0.50, P = 0.03), ARIBaseline (ρ = 0.57, P = 0.03) and PhaseLF (ρ = 0.48, P = 0.03); and for GainVLF with GainLF (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.

AB - 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-CVRICA, Slope-CVRVA and Slope-CVRiMCA 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 (ARIBaseline), and (ii) with transient changes in BP using a bolus injection of SNP (ARISNP) and PhE (ARIPhE). 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-CVRVA with Slope-CVRiMCA (r = 0.45, P < 0.03); for dCA ARISNP with ARIPhE (ρ = 0.50, P = 0.03), ARIBaseline (ρ = 0.57, P = 0.03) and PhaseLF (ρ = 0.48, P = 0.03); and for GainVLF with GainLF (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.

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