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Case Report: A 26-year-old woman presented a superior sagittal and transverse sinus thrombosis with venous infarction. Anticoagulation was started. Six months later headache and visual impairment developed, and intracranial hypertension was diagnosed-secondary pseudotumor cerebri. It was managed with a lumbo-peritoneal shunt (LPS) resulting in a positive initial evolution with initial symptoms resolution, but headache and visual impairment eventually reappeared. Magnetic Resonance Imaging revealed a Pseudo-Chiari malformation, leading to lumbo-peritoneal shunt removal (Friedman et al. Neurology 81:1159-1165, 2013; Moncho et al. Rev Neurol 56(12):623-634, 2013). As symptoms reappeared, a short period of continuous transcranial Doppler neuromonitoring, including a change of head of bed elevation, was performed. A sudden decrease in cerebral blood flow velocity with a dramatic increase in pulsatility index developed when head of bed was moved from 45° to horizontal position. Transcranial Doppler changes were compatible with a plateau wave of intracranial hypertension. A ventricle-peritoneal shunt was inserted, which resulted in symptomatology, imaging, and digital campimetry improvement.

Introduction: Cerebral critical closing pressure (CrCP) comprises intracranial pressure (ICP) and arteriolar wall tension (WT). It is the arterial blood pressure (ABP) at which small vessels close and circulation stops. We hypothesized that the increase in WT secondary to a systemic hypertensive challenge would lead to an increase in CrCP and that the "effective" cerebral perfusion pressure (CPPeff; calculated as ABP - CrCP) would give more complete information than the "conventional" cerebral perfusion pressure (CPP; calculated as ABP - ICP)., Objective: This study aimed to compare CrCP, CPP, and CPPeff changes during a hypertensive challenge in patients with a severe traumatic brain injury., Patients and Methods: Data on ABP, ICP, and cerebral blood flow velocity, measured by transcranial Doppler ultrasound, were acquired simultaneously for 30 min both basally and during a hypertensive challenge. An impedance-based CrCP model was used., Results: The following values are expressed as median (interquartile range). There were 11 patients, aged 29 (14) years. CPP increased from 73 (17) to 102 (26) mmHg (P ≤ 0.001). ICP did not change. CrCP changed from 23 (11) to 27 (10) mmHg (P ≤ 0.001). WT increased from 7 (5) to 11 (7) mmHg (P ˂ 0.005). CPPeff changed less than CPP., Conclusion: The CPP change was greater than the CPPeff change, mainly because CrCP increased simultaneously with the WT increase as a result of the autoregulatory response. CPPeff provides information about the real driving force generating blood movement.

The time constant of the cerebral arterial bed ("tau") estimates how fast the blood entering the brain fills the arterial vascular sector. Analogous to an electrical resistor-capacitor circuit, it is expressed as the product of arterial compliance (Ca) and cerebrovascular resistance (CVR). Hypocapnia increases the time constant in healthy volunteers and decreases arterial compliance in head trauma. How the combination of hyocapnia and trauma affects this parameter has yet to be studied. We hypothesized that in TBI patients the intense vasoconstrictive action of hypocapnia would dominate over the decrease in compliance seen after hyperventilation. The predominant vasoconstrictive response would maintain an incoming blood volume in the arterial circulation, thereby lengthening tau. We retrospectively analyzed recordings of intracranial pressure (ICP), arterial blood pressure (ABP), and blood flow velocity (FV) obtained from a cohort of 27 severe TBI patients [(39/30 years (median/IQR), 5 women; admission GCS 6/5 (median/IQR)] studied during a standard clinical CO 2 reactivity test. The reactivity test was performed by means of a 50-min increase in ventilation (20% increase in respiratory minute volume). CVR and Ca were estimated from these recordings, and their product calculated to find the time constant. CVR significantly increased [median CVR pre-hypocapnia/during hypocapnia: 1.05/1.35 mmHg/(cm 3 /s)]. Ca decreased (median Ca pre-hypocapnia/during hypocapnia: 0.130/0.124 arbitrary units) to statistical significance (p = 0.005). The product of these two parameters resulted in a significant prolongation of the time constant (median tau pre-hypocapnia/during hypocapnia: 0.136 s/0.152 s, p ˂ .001). Overall, the increase in CVR dominated over the decrease in compliance, hence tau was longer. We demonstrate a significant increase in the time constant of the cerebral circulation during hypocapnia after severe TBI, and attribute this to an increase in cerebrovascular resistance which outweighs the decrease in cerebral arterial bed compliance.

We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC<0.4) can improve to good (ICC > 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased., Competing Interests: The authors have declared that no competing interests exist.

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data ( p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.

Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques., Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC)., Main Results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p  =  0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p  <  0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35])., Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

Transfer function analysis (TFA) is a frequently used method to assess dynamic cerebral autoregulation (CA) using spontaneous oscillations in blood pressure (BP) and cerebral blood flow velocity (CBFV). However, controversies and variations exist in how research groups utilise TFA, causing high variability in interpretation. The objective of this study was to evaluate between-centre variability in TFA outcome metrics. 15 centres analysed the same 70 BP and CBFV datasets from healthy subjects (n=50 rest; n=20 during hypercapnia); 10 additional datasets were computer-generated. Each centre used their in-house TFA methods; however, certain parameters were specified to reduce a priori between-centre variability. Hypercapnia was used to assess discriminatory performance and synthetic data to evaluate effects of parameter settings. Results were analysed using the Mann-Whitney test and logistic regression. A large non-homogeneous variation was found in TFA outcome metrics between the centres. Logistic regression demonstrated that 11 centres were able to distinguish between normal and impaired CA with an AUC>0.85. Further analysis identified TFA settings that are associated with large variation in outcome measures. These results indicate the need for standardisation of TFA settings in order to reduce between-centre variability and to allow accurate comparison between studies. Suggestions on optimal signal processing methods are proposed., (Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.)

A low cost multimodal monitoring and signal processing platform is presented. A modular and flexible system was developed, aimed to continuous acquisition of several biological variables at patient bed-head and further processing with application specific algorithms. System hardware is made of a six-channel isolation and signal conditioning front-end along with a high resolution analog-to-digital converter board connected to a standard laptop. Whole system hardware is compact and light weight, which ensures portability and ease of use at intensive care units. System software is divided in three modules: Acquisition, Signal Processing and Patients Data Management. The first one allows configuring each acquisition channel parameters, depending on the biological variable connected to it, and to store up to several hours of continuous data. Signal processing module implements novel algorithms for research purposes like dynamic cerebral autoregulation, optimal perfusion pressure, critical closing pressure or pulsatility index. It is flexible enough to easily add new processing algorithms, export data to different formats and create graphical reports. Patients data management module organizes acquired records, which allows selecting cases for new studies based on different criteria like monitored variables or pathological information. In this work, whole system architecture is described and algorithms included into the cerebral hemodynamics toolbox are presented along with experimental results.