Accuracy of Intracranial Pressure Monitoring-Single Centre Observational Study and Literature Review.

Intracranial hypertension and adequacy of brain blood flow are primary concerns following traumatic brain injury. Intracranial pressure (ICP) monitoring is a critical diagnostic tool in neurocritical care. However, all ICP sensors, irrespective of design, are subject to systematic and random measurement inaccuracies that can affect patient care if overlooked or disregarded. The wide choice of sensors available to surgeons raises questions about performance and suitability for treatment. This observational study offers a critical review of the clinical and experimental assessment of ICP sensor accuracy and comments on the relationship between actual clinical performance, bench testing, and manufacturer specifications. Critically, on this basis, the study offers guidelines for the selection of ICP monitoring technologies, an important clinical decision. To complement this, a literature review on important ICP monitoring considerations was included. This study utilises illustrative clinical and laboratory material from 1200 TBI patients (collected from 1992 to 2019) to present several important points regarding the accuracy of in vivo implementation of contemporary ICP transducers. In addition, a thorough literature search was performed, with sources dating from 1960 to 2021. Sources considered to be relevant matched the keywords: "intraparenchymal ICP sensors", "fiberoptic ICP sensors", "piezoelectric strain gauge sensors", "external ventricular drains", "CSF reference pressure", "ICP zero drift", and "ICP measurement accuracy". Based on single centre observations and the 76 sources reviewed in this paper, this material reports an overall anticipated measurement accuracy for intraparenchymal transducers of around ± 6.0 mm Hg with an average zero drift of <2.0 mm Hg. Precise ICP monitoring is a key tenet of neurocritical care, and accounting for zero drift is vital. Intraparenchymal piezoelectric strain gauge sensors are commonly implanted to monitor ICP. Laboratory bench testing results can differ from in vivo observations, revealing the shortcomings of current ICP sensors.

Feasibility of non-invasive neuromonitoring in general intensive care patients using a multi-parameter transcranial Doppler approach.

PURPOSE: To assess the feasibility of Transcranial Doppler ultrasonography (TCD) neuromonitoring in a general intensive care environment, in the prognosis and outcome prediction of patients who are in coma due to a variety of critical conditions. METHODS: The prospective trial was performed between March 2017 and March 2019 Addenbrooke's Hospital, Cambridge, UK. Forty adult patients who failed to awake appropriately after resuscitation from cardiac arrest or were in coma due to conditions such as meningitis, seizures, sepsis, metabolic encephalopathies, overdose, multiorgan failure or transplant were eligible for inclusion. Gathered data included admission diagnosis, duration of ventilation, length of stay in the ICU, length of stay in hospital, discharge status using Cerebral Performance Categories (CPC). All patients received intermittent extended TCD monitoring following inclusion in the study. Parameters of interest included TCD-based indices of cerebral autoregulation, non-invasive intracranial pressure, autonomic system parameters (based on heart rate variability), critical closing pressure, the cerebrovascular time constant and indices describing the shape of the TCD pulse waveform. RESULTS: Thirty-seven patients were included in the final analysis, with 21 patients classified as good outcome (CPC 1-2) and 16 as poor neurological outcomes (CPC 3-5). Three patients were excluded due to inadequacies identified in the TCD acquisition. The results indicated that irrespective of the primary diagnosis, non-survivors had significantly disturbed cerebral autoregulation, a shorter cerebrovascular time constant and a more distorted TCD pulse waveform (all p<0.05). CONCLUSIONS: Preliminary results from the trial indicate that multi-parameter TCD neuromonitoring increases outcome-predictive power and TCD-based indices can be applied to general intensive care monitoring.

Usability of Noninvasive Counterparts of Traditional Autoregulation Indices in Traumatic Brain Injury.

The pressure reactivity index (PRx) and the pulse amplitude index (PAx) are invasively determined parameters that are commonly used to describe autoregulation following traumatic brain injury (TBI). Using a transcranial Doppler ultrasound (TCD) technique, it is possible to approximate cerebral arterial blood volume (CaBV) solely from cerebral blood flow velocities, and further, to calculate non-invasive markers of autoregulation. In this brief study, we aimed to investigate whether the estimation of relative CaBV with different models could describe the cerebrovascular reactivity of TBI patients. PRx, PAx and their non-invasive counterparts (nPRx and nPAx) were calculated retrospectively from data collected during the monitoring of TBI patients. CaBV, an essential parameter for the calculation of nPRx and nPAx, was determined with both a continuous flow forward (CFF) model-considering a non-pulsatile blood outflow from the brain-and a pulsatile flow forward (PFF) model, presuming a pulsatile outflow. We found that the estimated CaBV demonstrates good coherence with ICP and that nPRx and nPAx can describe cerebrovascular reactivity similarly to PRx and PAx. Continuous monitoring with TCD is difficult, so the usability of PRx and PAx is limited. However, they might become useful for clinicians in the near future owing to rapid advances in these technologies.

Errors and Consequences of Inaccurate Estimation of Mean Blood Flow Velocity in Cerebral Arteries.

Many transcranial Doppler ultrasonography devices estimate the mean flow velocity (FVm) by using the traditional formula (FVsystolic + 2 × FVdiastolic)/3 instead of a more accurate formula calculating it as the time integral of the current flow velocities divided by the integration period. We retrospectively analyzed flow velocity and intracranial pressure signals containing plateau waves (transient intracranial hypertension), which were collected from 14 patients with a traumatic brain injury. The differences in FVm and its derivative pulsatility index (PI) calculated with the two different methods were determined. We found that during plateau waves, when the intracranial pressure (ICP) rose, the error in FVm and PI increased significantly from the baseline to the plateau (from 4.6 ± 2.4 to 9.8 ± 4.9 cm/s, P < 0.05). Similarly, the error in PI also increased during plateau waves (from 0.11 ± 0.07 to 0.44 ± 0.24, P < 0.005). These effects were most likely due to changes in the pulse waveform during increased ICP, which alter the relationship between systolic, diastolic, and mean flow velocities. If a change in the mean ICP is expected, then calculation of FVm with the traditional formula is not recommended.

Validation of non-invasive cerebrovascular pressure reactivity and pulse amplitude reactivity indices in traumatic brain injury.

BACKGROUND: Two transcranial Doppler (TCD) estimators of cerebral arterial blood volume (CaBV) coexist: continuous outflow of arterial blood outside the cranium through a low-pulsatile venous system (continuous flow forward, CFF) and pulsatile outflow through regulating arterioles (pulsatile flow forward, PFF). We calculated non-invasive equivalents of the pressure reactivity index (PRx) and the pulse amplitude index PAx with slow waves of mean CaBV and its pulse amplitude. METHODS: About 273 individual TBI patients were retrospectively reviewed. PRx is the correlation coefficient between 30 samples of 10-second averages of ICP and mean ABP. PAx is the correlation coefficient between 30 samples of 10-second averages of the amplitude of ICP (AMP, derived from Fourier analysis of the raw full waveform ICP tracing) and mean ABP. nPRx is calculated with CaBV instead of ICP and nPAx with the pulse amplitude of CaBV instead of AMP (calculated using both the CFF and PFF models). All reactivity indices were additionally compared with Glasgow Outcome Score (GOS) to verify potential outcome-predictive strength. RESULTS: When correlated, slow waves of ICP demonstrated good coherence between slow waves in CaBV (>0.75); slow waves of AMP showed good coherence with slow waves of the pulse amplitude of CaBV (>0.67) in both the CFF and PFF models. nPRx was moderately correlated with PRx (R = 0.42 for CFF and R = 0.38 for PFF; p < 0.0001). nPAx correlated with PAx with slightly better strength (R = 0.56 for CFF and R = 0.41 for PFF; p < 0.0001). nPAx_CFF showed the strongest association with outcomes. CONCLUSIONS: Non-invasive estimators (nPRx and nPAx) are associated with their invasive counterparts and can provide meaningful associations with outcome after TBI. The CFF model is slightly superior to the PFF model.

Relationship Between Brain Pulsatility and Cerebral Perfusion Pressure: Replicated Validation Using Different Drivers of CPP Change.

BACKGROUND: Determination of relationships between transcranial Doppler (TCD)-based spectral pulsatility index (sPI) and pulse amplitude (AMP) of intracranial pressure (ICP) in 2 groups of severe traumatic brain injury (TBI) patients (a) displaying plateau waves and (b) with unstable mean arterial pressure (MAP). METHODS: We retrospectively reviewed patients with severe TBI and continuous TCD monitoring displaying either plateau waves or unstable MAP from 1992 to 1998. We utilized linear and nonlinear regression techniques to describe both cohorts: cerebral perfusion pressure (CPP) versus AMP, CPP versus sPI, mean ICP versus ICP AMP, mean ICP versus sPI, and AMP versus sPI. RESULTS: Nonlinear regression techniques were employed to analyze the relationships with CPP. In plateau wave and unstable MAP patients, CPP versus sPI displayed an inverse nonlinear relationship (R 2 = 0.820 vs. R 2 = 0.610, respectively), with the CPP versus sPI relationship best modeled by the following function in both cases: PI = a + (b/CPP). Similarly, in both groups, CPP versus AMP displayed an inverse nonlinear relationship (R 2 = 0.610 vs. R 2 = 0.360, respectively). Positive linear correlations were displayed in both the plateau wave and unstable MAP cohorts between: ICP versus AMP, ICP versus sPI, AMP versus sPI. CONCLUSIONS: There is an inverse relationship through nonlinear regression between CPP versus AMP and CPP versus sPI display. This provides evidence to support a previously-proposed model of TCD pulsatility index. ICP shows a positive linear correlation with AMP and sPI, which is also established between AMP and sPI.

Estimation of pulsatile cerebral arterial blood volume based on transcranial doppler signals.

OBJECTIVE: Mathematical modeling of cerebral hemodynamics by descriptive equations can estimate the underlying pulsatile component of cerebral arterial blood volume (CaBV). This way, clinical monitoring of changes in cerebral compartmental compliances becomes possible. Our aim is to validate the most adequate method of CaBV estimation in neurocritical care. APPROACH: We retrospectively reviewed patients with severe traumatic brain injury (TBI) [admitted from 1992-2012] and continuous transcranial Doppler (TCD) monitoring of cerebral blood flow velocity (FV) displaying either plateau waves of intracranial pressure (ICP), episodes of controlled, mild hypocapnia, or vasopressor-induced increases in arterial blood pressure (ABP). Each cohort was analyzed with continuous flow forward (CFF, pulsatile blood inflow and steady blood outflow) or pulsatile flow forward (PFF, both blood inflow and outflow are pulsatile) modeling approaches for estimating the pulse component of CaBV. Spectral pulsatility index (sPI, the first harmonic of the FV pulse/mean FV) can be estimated using the compliance of the vascular bed (Ca) and the cerebrovascular resistance (CVR - here, Ra). We compared three possible methods of assessing Ca (C1: the CFF model, C2 and C3: the PFF models based on ABP or cerebral perfusion pressure (CPP) pulsations, respectively) and combined them with three possible methods of assessing Ra (Ra1= ABP/FV, Ra2= the resistance area product, and Ra3= CPP/FV). Linear regression techniques were applied to describe the strength of each CaBV estimator (a combination of Ca and Ra) against sPI. MAIN RESULTS: The combination of C1 and Ra3 (PI_C1Ra3) was the superior descriptor of CaBV as approximated by sPI for both the plateau waves and the hypocapnia cohorts (r = 0.915 and r = 0.955, respectively). The combination of C1 and Ra1 (PI_C1Ra1) was nearly as robust in the vasopressors cohort (r = 0.938 and r = 0.931, respectively). SIGNIFICANCE: TCD-based estimation of CaBV pulsations seems to be feasible when employing the CFF modeling approach.

Cerebral autoregulation monitoring in acute traumatic brain injury: what's the evidence?

Cerebral autoregulation is conceptualized as a vascular self-regulatory mechanism within the brain. Controlled by elusive relationships between various biophysical processes, it functions to protect the brain against potential damages caused by sudden changes in cerebral perfusion pressures and flow. Following events such as traumatic brain injuries (TBI), autoregulation may be compromised, potentially leading to an unfavorable outcome. In spite of its complexity, autoregulation has been able to be quantified non-invasively within the neuro-critical care setting with the aid of transcranial Doppler. This information is interpreted particularly through calculated derived indices based on commonly-monitored input signals such as arterial blood pressure and intracranial pressure (i.e. Pressure Reactivity Index [PRx], Mean Flow Index, etc.). For example, PRx values that trend towards positive numbers are correlated with unfavorable outcome. These predictors are primarily surrogate markers of cerebral hemodynamic activity, although suggesting robust correlations between these indices and patient outcome. This review of the literature seeks to explain the methodology behind the calculations of various measures of autoregulation in adult patients suffering from traumatic brain injuries, and how they can interact with one another to both create larger effects on patient outcome and general outcome prediction models. Insight into the driving forces behind cerebral autoregulation is imperative for guiding both clinical decision-making and global treatment protocols for neuro-critically ill patients. The evidence that autoregulation-oriented therapy may improve outcome after TBI is still oscillating around Level III.