Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

Abstract

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

Keywords: Cerebral hemodynamics; consensus guidelines; reference values; transfer function analysis.

Figure 1.

Typical set of recordings in a clinical setting. See new Recommendation 20 for details about particular aspects affecting studies of dCA in critically ill patients. TCD: transcranial Doppler ultrasound device; ECG: electrocardiograph; ABP: either non-invasive continuous arterial blood pressure (finger photoplethysmograph sensor combined with arterial volume clamping), or invasive intra-radial catheter sampling in critical care settings.

Figure 2.

Critical values for coherence estimates at the 99%, 95% and 90% significance level for 3–25 windows. Solid lines: Monte Carlo simulation from 10,000 pairs of independent white Gaussian noise using Hanning windows with 50% overlap and spectral smoothing (see Table 2). The dotted lines give the critical values without spectral smoothing and with non-overlapping windows, calculated from theory. The line-thickness denotes the significance level.

Figure 3.

Representative spontaneous fluctuations in CBv, BP and ICP with corresponding CBv step responses from two patients with severe head injury. Patient A did not show a recovery of the step response following the sudden change in BP, with a corresponding value of ARI = 0 (absent dCA). Patient B had a working dCA, with ARI = 6. Reproduced with permission from.

Figure 4.

On the left side of the figure are the typical trace for blood pressure (BP), middle cerebral artery blood velocity (MCAv) and end-tidal CO₂ (P_ETCO₂) during: spontaneous upright (a), 0.05 Hz (b) and 0.10 Hz (c) repeated squat-stand maneuvers (rSSM), spontaneous supine (d), 0.05 Hz (e) and 0.10 Hz (f) oscillatory lower body negative pressure (OLBNP) maneuvers in a young healthy adult male. On the right side of the figure are the absolute values of the power spectrum densities (PSD) for the mean arterial pressure (MAP) and cerebral blood velocity (CBv) under spontaneous (grey), 0.05 Hz (dashed) and 0.10 Hz (black) conditions. The PSD during the OLBNP pressure maneuvers are represented on the left side (g–j) and rSSM are on the right side (k–n). Note the substantial increase in PSD power during the forced oscillations, with the greatest augmentation (approximately 20x higher peak PSD than OLBNP) clearly delineated for both MAP and CBv occurring during the rSSM maneuvers presented in the far-right panel. Adapted from Smirl et al.