Tracking time-varying cerebral autoregulation in response to changes in respiratory PaCO2

Liu, Jia, Simpson, M. David, Yan, Jingyu and Allen, R. (2010) Tracking time-varying cerebral autoregulation in response to changes in respiratory PaCO2. Physiological Measurement, 31 (10), 1291-1307. (doi:10.1088/0967-3334/31/10/001). (PMID:20720290)

Abstract

Cerebral autoregulation has been studied by linear filter systems, with arterial blood pressure (ABP) as the input and cerebral blood flow velocity (CBFV— from transcranial Doppler Ultrasound) as the output. The current work extends this by using adaptive filters to investigate the dynamics of time-varying cerebral autoregulation during step-wise changes in arterial PaCO2. Cerebral autoregulation was transiently impaired in 11 normal adult volunteers, by switching inspiratory air to a CO2/air mixture (5% CO2, 30% O2 and 65% N2) for approximately 2 min and then back to the ambient air, causing stepwise changes in end-tidal CO2 (EtCO2). Simultaneously, ABP and CBFV were recorded continuously. Simulated data corresponding to the same protocol were also generated using an established physiological model, in order to refine the signal analysis methods. Autoregulation was quantified by the timevarying phase lead, estimated from the adaptive filter model. The adaptive filter was able to follow rapid changes in autoregulation, as was confirmed in the simulated data. In the recorded signals, there was a slow decrease in autoregulatory function following the step-wise increase in PaCO2 (but this did not reach a steady state within approximately 2 min of recording), with a more rapid change in autoregulation on return to normocapnia. Adaptive filter modelling was thus able to demonstrate time-varying autoregulation. It was further noted that impairment and recovery of autoregulation during transient increases in EtCO2 occur in an asymmetric manner, which should be taken into account when designing experimental protocols for the study of autoregulation.

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Contributors

Author: M. David Simpson

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