RESUMEN
This is a comparative study of two novel noninvasive cerebrovascular autoregulation (CA) monitoring methods based on intracranial blood volume (IBV) changes in the human brain. We investigated the clinical applicability of the new volumetric reactivity index (VRx2), reflected by intracranial ultrasonic attenuation dynamics for noninvasive CA monitoring. The CA was determined noninvasively on 43 healthy participants by calculating the volumetric reactivity index (VRx1 from time-of-flight of ultrasound, VRx2 from attenuation of ultrasound). The VRx was calculated as a moving correlation coefficient between the arterial blood pressure and noninvasively measured IBV slow waves. Linear regression between VRx1 and VRx2 (averaged per participants) showed a significant correlation (r = 0.731, p < 0.0001, 95% confidence interval [0.501-0.895]) in data filtered by bandpass filtering. On the other hand, FIR filtering demonstrated a slightly better correlation (r = 0.769, p < 0.0001, 95% confidence interval [0.611-0.909]). The standard deviation of the difference by bandpass filtering was 0.1647 and bias -0.3444; and by FIR filtering 0.1382 and bias -0.3669. This comparative study showed a significant coincidence of the VRx2 index compared to that of VRx1. Hence, VRx2 could be used as an alternative, cost-effective noninvasive cerebrovascular autoregulation index in the same way as VRx1 values are used.
RESUMEN
Background and Objectives: The aim of this study was to explore the association between the cerebral autoregulation (CA) index, the pressure reactivity index (PRx), the patient's clinical outcome, and the quality of arterial blood pressure (ABP(t)) and intracranial blood pressure (ICP(t)) signals by comparing two filtering methods to derive the PRx. Materials and Methods: Data from 60 traumatic brain injury (TBI) patients were collected. Moving averaging and FIR (Finite Impulse Response) filtering were performed on the ABP(t) and ICP(t) signals, and the PRx was estimated from both filtered datasets. Sensitivity, specificity, and receiver-operating characteristic (ROC) curves with the area under the curves (AUCs) were determined using patient outcomes as a reference. The outcome chosen for comparison among the two filtering methods were mortality and survival. Results: The FIR filtering approach, compared with clinical outcome, had a sensitivity of 70%, a specificity of 81%, and a level of significance p = 0.001 with an area under the curve (AUC) of 0.78. The moving average filtering method compared with the clinical outcome had a sensitivity of 58%, a specificity of 72%, and a level of significance p = 0.054, with an area under the curve (AUC) of 0.66. Conclusions: The FIR (optimal) filtering approach was found to be more sensitive for discriminating between two clinical outcomes, namely intact (survival) and impaired (death) cerebral autoregulation for TBI treatment decision making.
