Prolonged monitoring of cerebral blood flow and autoregulation with diffuse correlation spectroscopy in neurocritical care patients

Abstract

Monitoring of cerebral blood flow (CBF) and autoregulation are essential components of neurocritical care, but continuous noninvasive methods for CBF monitoring are lacking. Diffuse correlation spectroscopy (DCS) is a noninvasive diffuse optical modality that measures a CBF index ( ${\mathrm{CBF}}_i$ ) in the cortex microvasculature by monitoring the rapid fluctuations of near-infrared light diffusing through moving red blood cells. We tested the feasibility of monitoring ${\mathrm{CBF}}_i$ with DCS in at-risk patients in the Neurosciences Intensive Care Unit. DCS data were acquired continuously for up to 20 h in six patients with aneurysmal subarachnoid hemorrhage, as permitted by clinical care. Mean arterial blood pressure was recorded synchronously, allowing us to derive autoregulation curves and to compute an autoregulation index. The autoregulation curves suggest disrupted cerebral autoregulation in most patients, with the severity of disruption and the limits of preserved autoregulation varying between subjects. Our findings suggest the potential of the DCS modality for noninvasive, long-term monitoring of cerebral perfusion, and autoregulation.

Keywords: cerebral autoregulation; cerebral blood flow; diffuse correlation spectroscopy; near-infrared spectroscopy; neurocritical care; neuromonitoring; subarachnoid hemorrhage.

Fig. 1

DCS probes. (a) First probe version. The probe is made of two parts so that the separation can be optimized based on the signal quality (b). Second DCS-only probe version, with multiple fibers at each detector location. (c) Schematic of the illumination prism with a diffuser enlarging the beam diameter (patent⁶³). (d) First probe placed on a patient and attached with gauze and collodion. (e) Second DCS-only probe attached on a patient. (S, source; D, detector).

Fig. 2

Scatter plots of the DCS-derived relative CBF index versus white matter perfusion measured with the invasive diffusion sensor in two patients. In patient 2, the data points corresponding to the last 4 h of recording are plotted in black.

Fig. 3

Autoregulatory curves for patient 1. The first panel displays the CPP, ICP, and ${\mathrm{rCBF}}_{\mathrm{i}}$ time traces over 6 h of recording. The second panel displays the ${\mathrm{rCBF}}_{\mathrm{i}}$ versus MAP curve for that recording. Panel 3 displays the association of the blood flow autoregulation index BFAx with CPP. Panel 4 displays the association of the reactivity index PRx with CPP. In panels 2, 3, and 4, the thick line represents the median value in that MAP (CPP) segment of ${\mathrm{rCBF}}_{\mathrm{i}}$, BFAx, and PRx, respectively, and the colored area shows the interquartile range.

Fig. 4

Time series, autoregulatory curves, and autoregulation indices for patient 2. The two rows correspond to the 2 days of recordings. The panels are the same as for Fig. 3.

Fig. 5

Time series, autoregulatory curves, and autoregulation indices for patient 3. The panels are the same as for Fig. 3.

Fig. 6

Time series, autoregulatory curves, and autoregulation indices for patient 4. The panels are the same as for Fig. 3.

Fig. 7

Time series, autoregulatory curves, and autoregulation indices for patient 5. The panels are the same as for Fig. 3.

Fig. 8

Time series, autoregulatory curves, and autoregulation indices for patient 6. The panels are the same as for Fig. 3.