Impact of impaired cerebral blood flow autoregulation on electroencephalogram signals in adults undergoing propofol anaesthesia: a pilot study

Abstract

Background: Cerebral autoregulation actively maintains cerebral blood flow over a range of MAPs. During general anaesthesia, this mechanism may not compensate for reductions in MAP leading to brain hypoperfusion. Cerebral autoregulation can be assessed using the mean flow index derived from Doppler measurements of average blood velocity in the middle cerebral artery, but this is impractical for routine monitoring within the operating room. Here, we investigate the possibility of using the EEG as a proxy measure for a loss of cerebral autoregulation, determined by the mean flow index.

Methods: Thirty-six patients (57.5 [44.25; 66.5] yr; 38.9% women, non-emergency neuroradiology surgery) anaesthetised using propofol were prospectively studied. Continuous recordings of MAP, average blood velocity in the middle cerebral artery, EEG, and regional cerebral oxygen saturation were made. Poor cerebral autoregulation was defined as a mean flow index greater than 0.3.

Results: Eighteen patients had preserved cerebral autoregulation, and 18 had altered cerebral autoregulation. The two groups had similar ages, MAPs, and average blood velocities in the middle cerebral artery. Patients with altered cerebral autoregulation exhibited a significantly slower alpha peak frequency (9.4 [9.0, 9.9] Hz vs 10.5 [10.1, 10.9] Hz, P<0.001), which persisted after adjusting for age, norepinephrine infusion rate, and ASA class (odds ratio=0.038 [confidence interval, 0.004, 0.409]; P=0.007).

Conclusion: In this pilot study, we found that loss of cerebral autoregulation was associated with a slower alpha peak frequency, independent of age. This work suggests that impaired cerebral autoregulation could be monitored in the operating room using the existing EEG setup.

Clinical trial registration: NCT03769142.

Keywords: EEG; anaesthesia; brain monitoring; cerebral autoregulation; propofol; transcranial Doppler ultrasonography.

© 2022 The Author(s).

Figures

Fig 1

Patient selection, data acquisition. (a) Flowchart. (b) Schematic representation of the monitoring setup used to collect blood flow velocity (Vm) in the cerebral middle artery (Doppler), estimate perfusion (NIRS), frontal EEG traces and MAP from digital plethysmography. CA, cerebral autoregulation; Mxa, mean flow index; NIRS, near-infrared spectroscopy; Vm, mean velocity.

Fig 2

Mxa-based cerebral autoregulation. MAP (green diamonds) and Vm (grey squares) averaged over 10-s-long non-overlapping epochs for a patient with an impaired cerebral autoregulation CA– (upper panel, Mxa >0.3) and one with a preserved perfusion CA+ (lower panel, Mxa ≤0.3). Variables were scaled for sake of illustration. CA, cerebral autoregulation; Mxa, mean flow index; Vm, mean velocity. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig 3

Box-plot. (a, b) Box-plot distributions between CA+ (blue) and CA– (purple) patients for age and norepinephrine. (c, d) Box-plot distributions between CA+ (blue) and CA– (purple) patients for α-PF, and α-BP. A significant difference between the two groups was found only for the α-PF. α-BP, alpha band power; α-PF, alpha peak frequency; CA, cerebral autoregulation; Mxa, mean flow index; Vm, mean velocity. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig 4

Alpha peak frequency slowing for patient with impaired autoregulation. (a) Averaged power spectral densities computed from EEG signals from CA+ (blue) and CA– (purple) patients. The ‘bump’ in the spectrum, located around 10 Hz, is characteristic of propofol-based general anaesthesia. The frequency corresponding to the maximal amplitude of this bump is the α-PF. (b) Scatterplot showing relationship between Mxa scores and the α-PF, exhibiting a significant negative correlation (red curve, r=–0.556, P<0.001). α-PF, alpha peak frequency; CA, cerebral autoregulation; Mxa, mean flow index. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig 5

Predicting AC using α-PF and confounding factors. Receiver operating characteristic AUC computed from logistic model classifiers for the α-PF alone (AUC=0.88, OR=0.069 [CI 0.013, 0.359], P=0.002; lavander), Age + ASA score + NAD (AUC=0.76; blue), and all of the above (AUC=0.93, α-PF OR=0.038 [CI 0.004, 0.409], P=0.007; red). Adding α-PF to Age + ASA + NAD variables significantly improved the prediction (P=0.017, Delong test). α-PF, alpha peak frequency; AUC, area under the curve; CI, confidence interval; NAD, norepinephrine; OR, odds ratio. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)