Model-based noninvasive estimation of intracranial pressure from cerebral blood flow velocity and arterial pressure

Open cranium model for the study of cerebrovascular dynamics in intracranial hypertension

BACKGROUND

Significant research has been devoted to developing noninvasive approaches to neuromonitoring. Clinical validation of such approaches is often limited, with minimal data available in the clinically relevant elevated ICP range.

NEW METHOD

To allow ultrasound-guided placement of an intraventricular catheter and to perform simultaneous long-duration ICP and ultrasound recordings of cerebral blood flow, we developed a large unilateral craniectomy in a swine model. We also used a microprocessor-controlled actuator for intraventricular saline infusion to reliably and reversibly manipulate ICP according to pre-determined profiles.

RESULTS

The model was reproducible, resulting in over 80 hours of high-fidelity, multi-parameter physiological waveform recordings in twelve animals, with ICP ranging from 2 to 78 mmHg. ICP elevations were reversible and reproducible according to two predetermined profiles: a stepwise elevation up to an ICP of 30-35 mmHg and return to normotension, and a clinically significant plateau wave. Finally, ICP was elevated to extreme levels of greater than 60 mmHg, simulating extreme clinical emergency.

COMPARISON WITH EXISTING METHODS

Existing methods for ICP monitoring in large animals typically relied on burr-hole approaches for catheter placement. Accurate catheter placement can be difficult in pigs, given the thickness of their skull. Additionally, ultrasound is significantly attenuated by the skull. The open cranium model overcomes these limitations.

CONCLUSIONS

The hemicraniectomy model allowed for verified placement of the intraventricular catheter, and reversible and reliable ICP manipulation over a wide range. The large dural window additionally allowed for long-duration recording of cerebral blood flow velocity from the middle cerebral artery.

Characterization of intracranial compliance in healthy subjects using a noninvasive method - results from a multicenter prospective observational study

PURPOSE

An FDA-approved non-invasive intracranial pressure (ICP) monitoring system enables the assessment of ICP waveforms by revealing and analyzing their morphological variations and parameters associated with intracranial compliance, such as the P2/P1 ratio and time-to-peak (TTP). The aim of this study is to characterize intracranial compliance in healthy volunteers across different age groups.

METHODS

Healthy participants, both sexes, aged from 9 to 74 years old were monitored for 5 min in the supine position at 0º. Age was stratified into 4 groups: children (≤ 7 years); young adults (18 ≤ age ≤ 44 years); middle-aged adults (45 ≤ age ≤ 64 years); older adults (≥ 65 years). The data obtained was the non-invasive ICP waveform, P2/P1 ratio and TTP.

RESULTS

From December 2020 to February 2023, 188 volunteers were assessed, of whom 104 were male, with a median (interquartile range) age of 41 (29-51), and a median (interquartile range) body mass index of 25.09 (22.57-28.04). Men exhibited lower values compared to women for both the P2/P1 ratio and TTP (p < 0.001). There was a relative rise in both P2/P1 and TTP as age increased (p < 0.001).

CONCLUSIONS

The study revealed that the P2/P1 ratio and TTP are influenced by age and sex in healthy individuals, with men displaying lower values than women, and both ratios increasing with age. These findings suggest potential avenues for further research with larger and more diverse samples to establish reference values for comparison in various health conditions.

TRIAL REGISTRATION

Brazilian Registry of Clinical Trials (RBR-9nv2h42), retrospectively registered 05/24/2022. UTN: U1111-1266-8006.

A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit

Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.

Non-Invasive Methods for Intracranial Pressure Monitoring in Traumatic Brain Injury Using Transcranial Doppler: A Scoping Review

Intracranial pressure (ICP) monitoring is necessary for managing patients with traumatic brain injury (TBI). Although gold-standard methods include intraventricular or intraparenchymal transducers, these systems cannot be used in patients with coagulopathies or in those who are at high risk of catheter-related infections, nor can they be used in resource-constrained settings. Therefore, a non-invasive modality that is more widely available, cost effective, and safe would have tremendous impact. Among such non-invasive choices, transcranial Doppler (TCD) provides indirect ICP estimates through waveform analysis of cerebral hemodynamic changes. The objective of this scoping review is to describe the existing evidence for the use of TCD-derived methods in estimating ICP in adult TBI patients as compared with gold-standard invasive methods. This review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews, with a main search of PubMed and Embase. The search was limited to studies conducted in adult TBI patients published in any language between 2012 and 2022. Twenty-two studies were included for analysis, with most being prospective studies conducted in high-income countries. TCD-derived non-invasive ICP (nICP) methods are either mathematical or non-mathematical, with the former having slightly better correlation with invasive methods, especially when using time-trending ICP dynamics over one-time estimated values. Nevertheless, mathematical methods are associated with greater cost and complexity in their application. Formula-based methods showed promise in excluding elevated ICP, exhibiting a high negative predictive value. Therefore, TCD-derived methods could be useful in assessing ICP changes instead of absolute ICP values for high-risk patients, especially in low-resource settings.

Noninvasive Neuromonitoring Modalities in Children Part I: Pupillometry, Near-Infrared Spectroscopy, and Transcranial Doppler Ultrasonography

BACKGROUND

Noninvasive neuromonitoring in critically ill children includes multiple modalities that all intend to improve our understanding of acute and ongoing brain injury.

METHODS

In this article, we review basic methods and devices, applications in clinical care and research, and explore potential future directions for three noninvasive neuromonitoring modalities in the pediatric intensive care unit: automated pupillometry, near-infrared spectroscopy, and transcranial Doppler ultrasonography.

RESULTS

All three technologies are noninvasive, portable, and easily repeatable to allow for serial measurements and trending of data over time. However, a paucity of high-quality data supporting the clinical utility of any of these technologies in critically ill children is currently a major limitation to their widespread application in the pediatric intensive care unit.

CONCLUSIONS

Future prospective multicenter work addressing major knowledge gaps is necessary to advance the field of pediatric noninvasive neuromonitoring.

Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management

Background

The protocols and therapeutic guidance established for treating traumatic brain injuries (TBI) in neurointensive care focus on managing cerebral blood flow (CBF) and brain tissue oxygenation based on pressure signals. The decision support process relies on assumed relationships between cerebral perfusion pressure (CPP) and blood flow, pressure-flow relationships (PFRs), and shares this framework of assumptions with mathematical intracranial hemodynamic models. These foundational assumptions are difficult to verify, and their violation can impact clinical decision-making and model validity.

Method

A hypothesis- and model-driven method for verifying and understanding the foundational intracranial hemodynamic PFRs is developed and applied to a novel multi-modality monitoring dataset.

Results

Model analysis of joint observations of CPP and CBF validates the standard PFR when autoregulatory processes are impaired as well as unmodelable cases dominated by autoregulation. However, it also identifies a dynamical regime -or behavior pattern- where the PFR assumptions are wrong in a precise, data-inferable way due to negative CPP-CBF coordination over long timescales. This regime is of both clinical and research interest: its dynamics are modelable under modified assumptions while its causal direction and mechanistic pathway remain unclear.

Conclusions

Motivated by the understanding of mathematical physiology, the validity of the standard PFR can be assessed a) directly by analyzing pressure reactivity and mean flow indices (PRx and Mx) or b) indirectly through the relationship between CBF and other clinical observables. This approach could potentially help personalize TBI care by considering intracranial pressure and CPP in relation to other data, particularly CBF. The analysis suggests a threshold using clinical indices of autoregulation jointly generalizes independently set indicators to assess CA functionality. These results support the use of increasingly data-rich environments to develop more robust hybrid physiological-machine learning models.

Evaluation of machine learning algorithms for noninvasive intracranial pressure estimation using near infrared spectroscopy as a covariate

BACKGROUND

Intracranial pressure (ICP) is a vital parameter that is continuously monitored in patients with severe brain injury and imminent intracranial hypertension.

OBJECTIVE

To estimate intracranial pressure without intracranial probes based on transcutaneous near infrared spectroscopy (NIRS).

METHODS

We developed machine learning based approaches for noninvasive intracranial pressure (ICP) estimation using signals from transcutaneous near infrared spectroscopy (NIRS) as well as other cardiovascular and artificial ventilation parameters.

RESULTS

In a patient cohort of 25 patients, with 22 used for model development and 3 for model testing, the best performing models were Fourier transform based Transformer ICP waveform estimation which produced a mean absolute error of 4.68 mm Hg (SD = 5.4) in estimation.

CONCLUSION

We did not find a significant improvement in ICP estimation accuracy by including signals measured by transcutaneous NIRS. We expect that with higher quality and greater volume of data, noninvasive estimation of ICP will improve.

Analysis of intracranial pressure pulse waveform in studies on cerebrospinal compliance: a narrative review

Continuous monitoring of mean intracranial pressure (ICP) has been an essential part of neurocritical care for more than half a century. Cerebrospinal pressure-volume compensation, i.e. the ability of the cerebrospinal system to buffer changes in volume without substantial increases in ICP, is considered an important factor in preventing adverse effects on the patient's condition that are associated with ICP elevation. However, existing assessment methods are poorly suited to the management of brain injured patients as they require external manipulation of intracranial volume. In the 1980s, studies suggested that spontaneous short-term variations in the ICP signal over a single cardiac cycle, called the ICP pulse waveform, may provide information on cerebrospinal compensatory reserve. In this review we discuss the approaches that have been proposed so far to derive this information, from pulse amplitude estimation and spectral techniques to most recent advances in morphological analysis based on artificial intelligence solutions. Each method is presented with focus on its clinical significance and the potential for application in standard clinical practice. Finally, we highlight the missing links that need to be addressed in future studies in order for ICP pulse waveform analysis to achieve widespread use in the neurocritical care setting.

Extended reality quantification of pupil reactivity as a non-invasive assessment for the pathogenesis of spaceflight associated neuro-ocular syndrome: A technology validation study for astronaut health

The National Aeronautics and Space Administration (NASA) has rigorously documented a group of neuro-ophthalmic findings in astronauts during and after long-duration spaceflight known as spaceflight associated neuro-ocular syndrome (SANS). For astronaut safety and mission effectiveness, understanding SANS and countermeasure development are of utmost importance. Although the pathogenesis of SANS is not well defined, a leading hypothesis is that SANS might relate to a sub-clinical increased intracranial pressure (ICP) from cephalad fluid shifts in microgravity. However, no direct ICP measurements are available during spaceflight. To further understand the role of ICP in SANS, pupillometry can serve as a promising non-invasive biomarker for spaceflight environment as ICP is correlated with the pupil variables under illumination. Extended reality (XR) can help to address certain limitations in current methods for efficient pupil testing during spaceflight. We designed a protocol to quantify parameters of pupil reactivity in XR with an equivalent time duration of illumination on each eye compared to pre-existing, non-XR methods. Throughout the assessment, the pupil diameter data was collected using HTC Vive Pro-VR headset, thanks to its eye-tracking capabilities. Finally, the data was used to compute several pupil variables. We applied our methods to 36 control subjects. Pupil variables such as maximum and minimum pupil size, constriction amplitude, average constriction amplitude, maximum constriction velocity, latency and dilation velocity were computed for each control data. We compared our methods of calculation of pupil variables with the non-XR methods existing in the literature. Distributions of the pupil variables such as latency, constriction amplitude, and velocity of 36 control data displayed near-identical results from the non-XR literature for normal subjects. We propose a new method to evaluate pupil reactivity with XR technology to further understand ICP's role in SANS and provide further insight into SANS countermeasure development for future spaceflight.

Tissue perfusion pressure enables continuous hemodynamic evaluation and risk prediction in the intensive care unit

Treatment of circulatory shock in critically ill patients requires management of blood pressure using invasive monitoring, but uncertainty remains as to optimal individual blood pressure targets. Critical closing pressure, which refers to the arterial pressure when blood flow stops, can provide a fundamental measure of vascular tone in response to disease and therapy, but it has not previously been possible to measure this parameter routinely in clinical care. Here we describe a method to continuously measure critical closing pressure in the systemic circulation using readily available blood pressure monitors and then show that tissue perfusion pressure (TPP), defined as the difference between mean arterial pressure and critical closing pressure, provides unique information compared to other hemodynamic parameters. Using analyses of 5,988 admissions to a modern cardiac intensive care unit, and externally validated with 864 admissions to another institution, we show that TPP can predict the risk of mortality, length of hospital stay and peak blood lactate levels. These results indicate that TPP may provide an additional target for blood pressure optimization in patients with circulatory shock.

A Deep Learning Framework for Deriving Noninvasive Intracranial Pressure Waveforms from Transcranial Doppler

Increased intracranial pressure (ICP) causes disability and mortality in the neurointensive care population. Current methods for monitoring ICP are invasive. We designed a deep learning framework using a domain adversarial neural network to estimate noninvasive ICP, from blood pressure, electrocardiogram, and cerebral blood flow velocity. Our model had a mean of median absolute error of 3.88 ± 3.26 mmHg for the domain adversarial neural network, and 3.94 ± 1.71 mmHg for the domain adversarial transformers. Compared with nonlinear approaches, such as support vector regression, this was 26.7% and 25.7% lower. Our proposed framework provides more accurate noninvasive ICP estimates than currently available. ANN NEUROL 2023;94:196-202.

Super resolution imaging reconstruction reveals that gold standard methods may not correctly conclude neural/brain functional recovery

The status of cerebral perfusion and its restoration level play a vital role in the prognosis and clinical decision making of many neurosurgical diseases. As such, gold standard methods including CT, MR and ICP monitoring, which can indicate and measure cerebral perfusion and restoration, have been widely adopted to evaluate whether or not a patient has recovered from neurofunctional disabilities. This robust combination of methods, however, is confronted with a growing number of contradictions in recent years due to its inability to measure the status of cerebral reperfusion in microvasculature level, even though this has been shown to determine neurofunctional restoration as well or even better. To this date, nevertheless, we have very limited imaging methods that could evaluate human cerebral microperfusion both safely and accurately under most neurosurgical conditions. We herein report a new method of acquiring a patient's cerebral microperfusion status noninvasively which could display the precise distribution of microvasculature in deep cerebral regions with a resolution of ∼30 µm, using everyday bed-side ultrasonography combined with a computerized super-resolution reconstruction algorithm. Using this imaging modality, we found that a patient's cerebral microperfusion might not be improved by some routine administrations even though the gold standard method had yielded the opposite conclusions. Our imaging modality retains the safe, portable feature of ordinary ultrasonography while possesses the extraordinary super-resolution nature, which enables an efficient, precise diagnosis of cerebral perfusion. Most importantly, the super resolution nature of this method may also facilitate early-stage evaluation of a patient's neurofunctional restoration level and avoid overoptimistic conclusions from conventional angiography or ICP monitoring.

Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study

Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals.

Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories.

Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60 years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR).

Results: The simulated mean ICPs in normal condition were in the normal range (8.87–11.35 mm Hg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (&gt;20 mm Hg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean.

Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.

Non-Invasive Intracranial Pressure Monitoring

(1) Background: Intracranial pressure (ICP) monitoring plays a key role in the treatment of patients in intensive care units, as well as during long-term surgeries and interventions. The gold standard is invasive measurement and monitoring via ventricular drainage or a parenchymal probe. In recent decades, numerous methods for non-invasive measurement have been evaluated but none have become established in routine clinical practice. The aim of this study was to reflect on the current state of research and shed light on relevant techniques for future clinical application. (2) Methods: We performed a PubMed search for “non-invasive AND ICP AND (measurement OR monitoring)” and identified 306 results. On the basis of these search results, we conducted an in-depth source analysis to identify additional methods. Studies were analyzed for design, patient type (e.g., infants, adults, and shunt patients), statistical evaluation (correlation, accuracy, and reliability), number of included measurements, and statistical assessment of accuracy and reliability. (3) Results: MRI-ICP and two-depth Doppler showed the most potential (and were the most complex methods). Tympanic membrane temperature, diffuse correlation spectroscopy, natural resonance frequency, and retinal vein approaches were also promising. (4) Conclusions: To date, no convincing evidence supports the use of a particular method for non-invasive intracranial pressure measurement. However, many new approaches are under development.

Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales

BACKGROUND

The clinical mitigation of intracranial hypertension due to traumatic brain injury requires timely knowledge of intracranial pressure to avoid secondary injury or death. Noninvasive intracranial pressure (nICP) estimation that operates sufficiently fast at multihour timescales and requires only common patient measurements is a desirable tool for clinical decision support and improving traumatic brain injury patient outcomes. However, existing model-based nICP estimation methods may be too slow or require data that are not easily obtained.

OBJECTIVE

This work considers short- and real-time nICP estimation at multihour timescales based on arterial blood pressure (ABP) to better inform the ongoing development of practical models with commonly available data.

METHODS

We assess and analyze the effects of two distinct pathways of model development, either by increasing physiological integration using a simple pressure estimation model, or by increasing physiological fidelity using a more complex model. Comparison of the model approaches is performed using a set of quantitative model validation criteria over hour-scale times applied to model nICP estimates in relation to observed ICP.

RESULTS

The simple fully coupled estimation scheme based on windowed regression outperforms a more complex nICP model with prescribed intracranial inflow when pulsatile ABP inflow conditions are provided. We also show that the simple estimation data requirements can be reduced to 1-minute averaged ABP summary data under generic waveform representation.

CONCLUSIONS

Stronger performance of the simple bidirectional model indicates that feedback between the systemic vascular network and nICP estimation scheme is crucial for modeling over long intervals. However, simple model reduction to ABP-only dependence limits its utility in cases involving other brain injuries such as ischemic stroke and subarachnoid hemorrhage. Additional methodologies and considerations needed to overcome these limitations are illustrated and discussed.

Bile-duct ligation renders the brain susceptible to hypotension-induced neuronal degeneration: Implications of ammonia

Hepatic encephalopathy (HE) is a debilitating neurological complication of cirrhosis. By definition, HE is considered a reversible disorder, and therefore HE should resolve following liver transplantation (LT). However, persisting neurological complications are observed in as many as 47% of LT recipients. LT is an invasive surgical procedure accompanied by various perioperative factors such as blood loss and hypotension which could influence outcomes post-LT. We hypothesize that minimal HE (MHE) renders the brain frail and susceptible to hypotension-induced neuronal cell death. Six-week bile duct-ligated (BDL) rats with MHE and respective SHAM-controls were used. Several degrees of hypotension (mean arterial pressure of 30, 60 and 90 mm Hg) were induced via blood withdrawal from the femoral artery and maintained for 120 min. Brains were collected for neuronal cell count and apoptotic analysis. In a separate group, BDL rats were treated for MHE with the ammonia-lowering strategy ornithine phenylacetate (OP; MNK-6105), administered orally (1 g/kg) for 3 weeks before induction of hypotension. Hypotension 30 and 60 mm Hg (not 90 mm Hg) significantly decreased neuronal marker expression (NeuN) and cresyl violet staining in the frontal cortex compared to respective hypotensive SHAM-operated controls as well as non-hypotensive BDL rats. Neuronal degeneration was associated with an increase in cleaved caspase-3, suggesting the mechanism of cell death was apoptotic. OP treatment attenuated hyperammonaemia, improved anxiety and activity, and protected the brain against hypotension-induced neuronal cell death. Our findings demonstrate that rats with chronic liver disease and MHE are more susceptible to hypotension-induced neuronal cell degeneration. This highlights MHE at the time of LT is a risk factor for poor neurological outcome post-transplant and that treating for MHE pre-LT might reduce this risk.

Artifact rejection and missing data imputation in cerebral blood flow velocity signals via trace norm minimization

OBJECTIVE

Many physiological signals are degraded by significant corruptions that limit their usefulness. One example is cerebral blood flow velocity (CBFV) signals, measured by transcranial Doppler, which are susceptible to large errors from patient motion. In this paper, we propose a method to remove artifacts and impute sections of missing data in these signals.

APPROACH

The method exploits the low-order dynamical relationship between CBFV, arterial blood pressure and, where available, intracranial pressure. It enhances the measured signals by fitting them to a low-order dynamical model, using convex regularization terms that improve robustness to large deviations and missing data. The method is based on a convex optimization formulation and utilizes recent work in trace norm approximation and subspace system identification.

MAIN RESULTS

Simulations demonstrate that the method successfully removes real CBFV artifacts and can impute missing data with reasonable accuracy. Performance was improved when intracranial pressure data was available.

CONCLUSION

The methods presented can be used by researchers to remove artifacts and estimate missing sections in CBFV signals. The general approach may be applied to other biomedical signal processing settings.

SIGNIFICANCE

This low-order dynamical approach has ongoing applications in noninvasive intracranial pressure estimation.

Long-duration spaceflight alters estimated intracranial pressure and cerebral blood velocity

Key points

During long‐duration spaceflights, some astronauts develop structural ocular changes including optic disc oedema that resemble signs of intracranial hypertension.

In the present study, intracranial pressure was estimated non‐invasively (nICP) using a model‐based analysis of cerebral blood velocity and arterial blood pressure waveforms in 11 astronauts before and after long‐duration spaceflights.

Our results show that group‐averaged estimates of nICP decreased significantly in nine astronauts without optic disc oedema, suggesting that the cephalad fluid shift during long‐duration spaceflight rarely increased postflight intracranial pressure.

The results of the two astronauts with optic disc oedema suggest that both increases and decreases in nICP are observed post‐flight in astronauts with ocular alterations, arguing against a primary causal relationship between elevated ICP and spaceflight associated optical changes.

Cerebral blood velocity increased independently of nICP and spaceflight‐associated ocular alterations. This increase may be caused by the reduced haemoglobin concentration after long‐duration spaceflight.

Abstract

Persistently elevated intracranial pressure (ICP) above upright values is a suspected cause of optic disc oedema in astronauts. However, no systematic studies have evaluated changes in ICP from preflight. Therefore, ICP was estimated non‐invasively before and after spaceflight to test whether ICP would increase after long‐duration spaceflight. Cerebral blood velocity in the middle cerebral artery (MCAv) was obtained by transcranial Doppler sonography and arterial pressure in the radial artery was obtained by tonometry, in the supine and sitting positions before and after 4−12 months of spaceflight in 11 astronauts (10 males and 1 female, 46 ± 7 years old at launch). Non‐invasive ICP (nICP) was computed using a validated model‐based estimation method. Mean MCAv increased significantly after spaceflight (ANOVA, P = 0.007). Haemoglobin decreased significantly after spaceflight (14.6 ± 0.8 to 13.3 ± 0.7 g/dL, P < 0.001). A repeated measures correlation analysis indicated a negative correlation between haemoglobin and mean MCAv (r = −0.589, regression coefficient = −4.68). The nICP did not change significantly after spaceflight in the 11 astronauts. However, nICP decreased significantly by 15% in nine astronauts without optic disc oedema (P < 0.005). Only one astronaut increased nICP to relatively high levels after spaceflight. Contrary to our hypothesis, nICP did not increase after long‐duration spaceflight in the vast majority (>90%) of astronauts, suggesting that the cephalad fluid shift during spaceflight does not systematically or consistently elevate postflight ICP in astronauts. Independently of nICP and ocular alterations, the present results of mean MCAv suggest that long‐duration spaceflight may increase cerebral blood flow, possibly due to reduced haemoglobin concentration.

During long‐duration spaceflights, some astronauts develop structural ocular changes including optic disc oedema that resemble signs of intracranial hypertension.

In the present study, intracranial pressure was estimated non‐invasively (nICP) using a model‐based analysis of cerebral blood velocity and arterial blood pressure waveforms in 11 astronauts before and after long‐duration spaceflights.

Our results show that group‐averaged estimates of nICP decreased significantly in nine astronauts without optic disc oedema, suggesting that the cephalad fluid shift during long‐duration spaceflight rarely increased postflight intracranial pressure.

The results of the two astronauts with optic disc oedema suggest that both increases and decreases in nICP are observed post‐flight in astronauts with ocular alterations, arguing against a primary causal relationship between elevated ICP and spaceflight associated optical changes.

Cerebral blood velocity increased independently of nICP and spaceflight‐associated ocular alterations. This increase may be caused by the reduced haemoglobin concentration after long‐duration spaceflight.

A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus

To develop and validate a mathematical model for predicting intracranial pressure (ICP) noninvasively using phase-contrast cine MRI (PC-MRI). We performed a retrospective analysis of PC-MRI from patients with communicating hydrocephalus (n = 138). The patients were recruited from Shenzhen Second People’s Hospital between November 2017 and April 2020, and randomly allocated into training (n = 97) and independent validation (n = 41) groups. All participants underwent lumbar puncture and PC-MRI in order to evaluate ICP and cerebrospinal fluid (CSF) parameters (i.e., aqueduct diameter and flow velocity), respectively. A novel ICP-predicting model was then developed based on the nonlinear relationships between the CSF parameters, using the Levenberg–Marquardt and general global optimisation methods. There was no significant difference in baseline demographic characteristics between the training and independent validation groups. The accuracy of the model for predicting ICP was 0.899 in the training cohort (n = 97) and 0.861 in the independent validation cohort (n = 41). We obtained an ICP-predicting model that showed excellent performance in the noninvasive diagnosis of clinically significant communicating hydrocephalus.

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

A Spectral Approach to Model-Based Noninvasive Intracranial Pressure Estimation

BACKGROUND

Intracranial pressure (ICP) normally ranges from 5 to 15 mmHg. Elevation in ICP is an important clinical indicator of neurological injury, and ICP is therefore monitored routinely in several neurological conditions to guide diagnosis and treatment decisions. Current measurement modalities for ICP monitoring are highly invasive, largely limiting the measurement to critically ill patients. An accurate noninvasive method to estimate ICP would dramatically expand the pool of patients that could benefit from this cranial vital sign.

METHODS

This article presents a spectral approach to model-based ICP estimation from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) measurements. The model captures the relationship between the ABP, CBFV, and ICP waveforms and utilizes a second-order model of the cerebral vasculature to estimate ICP.

RESULTS

The estimation approach was validated on two separate clinical datasets, one recorded from thirteen pediatric patients with a total duration of around seven hours, and the other recorded from five adult patients, one hour and 48 minutes in total duration. The algorithm was shown to have an accuracy (mean error) of 0.4 mmHg and -1.5 mmHg, and a precision (standard deviation of the error) of 5.1 mmHg and 4.3 mmHg, in estimating mean ICP (range of 1.3 mmHg to 24.8 mmHg) on the pediatric and adult data, respectively. These results are comparable to previous results and within the clinically relevant range. Additionally, the accuracy and precision in estimating the pulse pressure of ICP on a beat-by-beat basis were found to be 1.3 mmHg and 2.9 mmHg respectively.

CONCLUSION

These contributions take a step towards realizing the goal of implementing a real-time noninvasive ICP estimation modality in a clinical setting, to enable accurate clinical-decision making while overcoming the drawbacks of the invasive ICP modalities.

Adaptive Maximal Blood Flow Velocity Estimation From Transcranial Doppler Echos

OBJECTIVE

Novel applications of transcranial Doppler (TCD) ultrasonography, such as the assessment of cerebral vessel narrowing/occlusion or the non-invasive estimation of intracranial pressure (ICP), require high-quality maximal flow velocity waveforms. However, due to the low signal-to-noise ratio of TCD spectrograms, measuring the maximal flow velocity is challenging. In this work, we propose a calibration-free algorithm for estimating maximal flow velocities from TCD spectrograms and present a pertaining beat-by-beat signal quality index.

METHODS

Our algorithm performs multiple binary segmentations of the TCD spectrogram and then extracts the pertaining envelopes (maximal flow velocity waveforms) via an edge-following step that incorporates physiological constraints. The candidate maximal flow velocity waveform with the highest signal quality index is finally selected.

RESULTS

We evaluated the algorithm on 32 TCD recordings from the middle cerebral and internal carotid arteries in 6 healthy and 12 neurocritical care patients. Compared to manual spectrogram tracings, we obtained a relative error of -1.5%, when considering the whole waveform, and a relative error of -3.3% for the peak systolic velocity.

CONCLUSION

The feedback loop between the signal quality assessment and the binary segmentation yields a robust algorithm for maximal flow velocity estimation. Clinical Impact: The algorithm has already been used in our ICP estimation pipeline. By making the code and the data publicly available, we hope that the algorithm will be a useful building block for the development of novel TCD applications that require high-quality flow velocity waveforms.

Intracranial Pressure and Intracranial Elastance Monitoring in Neurocritical Care

Patients with acute brain injuries tend to be physiologically unstable and at risk of rapid and potentially life-threatening decompensation due to shifts in intracranial compartment volumes and consequent intracranial hypertension. Invasive intracranial pressure (ICP) monitoring therefore remains a cornerstone of modern neurocritical care, despite the attendant risks of infection and damage to brain tissue arising from the surgical placement of a catheter or pressure transducer into the cerebrospinal fluid or brain tissue compartments. In addition to ICP monitoring, tracking of the intracranial capacity to buffer shifts in compartment volumes would help in the assessment of patient state, inform clinical decision making, and guide therapeutic interventions. We review the anatomy, physiology, and current technology relevant to clinical management of patients with acute brain injury and outline unmet clinical needs to advance patient monitoring in neurocritical care.

Review: pathophysiology of intracranial hypertension and noninvasive intracranial pressure monitoring

Measurement of intracranial pressure (ICP) is crucial in the management of many neurological conditions. However, due to the invasiveness, high cost, and required expertise of available ICP monitoring techniques, many patients who could benefit from ICP monitoring do not receive it. As a result, there has been a substantial effort to explore and develop novel noninvasive ICP monitoring techniques to improve the overall clinical care of patients who may be suffering from ICP disorders. This review attempts to summarize the general pathophysiology of ICP, discuss the importance and current state of ICP monitoring, and describe the many methods that have been proposed for noninvasive ICP monitoring. These noninvasive methods can be broken down into four major categories: fluid dynamic, otic, ophthalmic, and electrophysiologic. Each category is discussed in detail along with its associated techniques and their advantages, disadvantages, and reported accuracy. A particular emphasis in this review will be dedicated to methods based on the use of transcranial Doppler ultrasound. At present, it appears that the available noninvasive methods are either not sufficiently accurate, reliable, or robust enough for widespread clinical adoption or require additional independent validation. However, several methods appear promising and through additional study and clinical validation, could eventually make their way into clinical practice.

Measuring intracranial pressure by invasive, less invasive or non-invasive means: limitations and avenues for improvement

Sixty years have passed since neurosurgeon Nils Lundberg presented his thesis about intracranial pressure (ICP) monitoring, which represents a milestone for its clinical introduction. Monitoring of ICP has since become a clinical routine worldwide, and today represents a cornerstone in surveillance of patients with acute brain injury or disease, and a diagnostic of individuals with chronic neurological disease. There is, however, controversy regarding indications, clinical usefulness and the clinical role of the various ICP scores. In this paper, we critically review limitations and weaknesses with the current ICP measurement approaches for invasive, less invasive and non-invasive ICP monitoring. While risk related to the invasiveness of ICP monitoring is extensively covered in the literature, we highlight other limitations in current ICP measurement technologies, including limited ICP source signal quality control, shifts and drifts in zero pressure reference level, affecting mean ICP scores and mean ICP-derived indices. Control of the quality of the ICP source signal is particularly important for non-invasive and less invasive ICP measurements. We conclude that we need more focus on mitigation of the current limitations of today's ICP modalities if we are to improve the clinical utility of ICP monitoring.

Wearable skin-like optoelectronic systems with suppression of motion artifacts for cuff-less continuous blood pressure monitor

According to the statistics of the World Health Organization, an estimated 17.9 million people die from cardiovascular diseases each year, representing 31% of all global deaths. Continuous non-invasive arterial pressure (CNAP) is essential for the management of cardiovascular diseases. However, it is difficult to achieve long-term CNAP monitoring with the daily use of current devices due to irritation of the skin as well as the lack of motion artifacts suppression. Here, we report a high-performance skin-like optoelectronic system integrated with ultra-thin flexible circuits to monitor CNAP. We introduce a theoretical model via the virtual work principle for predicting the precise blood pressure and suppressing motion artifacts, and propose optical difference in the frequency domain for stable optical measurements in terms of skin-like devices. We compare the results with the blood pressure acquired by invasive (intra-arterial) blood pressure monitoring for >1500 min in total on 44 subjects in an intensive care unit. The maximum absolute errors of diastolic and systolic blood pressure were ±7/±10 mm Hg, respectively, in immobilized, and ±10/±14 mm Hg, respectively, in walking scenarios. These strategies provide advanced blood pressure monitoring techniques, which would directly address an unmet clinical need or daily use for a highly vulnerable population.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

A Frequency-domain Approach to Noninvasive Intracranial Pressure Estimation

Intracranial pressure (ICP) is a cranial vital sign, crucial in the monitoring and treatment of several neurological injuries. The clinically accepted measurement modalities of ICP are highly invasive, carrying risks of infection and limiting the benefits of ICP measurement to a small subset of critically ill patients. This work aims to take a step towards developing an accurate noninvasive means of estimating ICP, by utilizing a model-based frequency-domain approach. The mean ICP and pulse pressures of ICP are estimated from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) waveforms, and the estimates are validated on an adult population, comprising of around two hours of data from five patients. The algorithm was shown to have an accuracy (mean error) of -1.5 mmHg and a precision (standard deviation of the error) of 4.3 mmHg in estimating the mean ICP. These results are comparable to the previously reported errors among the currently accepted invasive measurement methods, and well within the clinically relevant range.

Transcranial Doppler as a non-invasive method to estimate cerebral perfusion pressure in children with severe traumatic brain injury

INTRODUCTION

Cerebral perfusion pressure (CPP) is one of the most important parameters in preventing ischemic brain insults. Guidelines have used CPP values to guide treatment of traumatic brain injury (TBI) for many years. We tested the feasibility of a novel non-invasive method for CPP estimation (nCPP) in children with severe TBI.

METHODS

Retrospective analysis of prospectively monitored pediatric TBI patients with invasive intracranial pressure (ICP) monitoring, arterial blood pressure, and Transcranial Doppler (TCD) studies was performed daily. A novel estimator of CPP (nCPP) was calculated using TCD-spectral accounting method. We analyzed the correlation coefficient and correlation in time domain between CPP and nCPP, prediction ability of nCPP to detect low CPP, and the confidence intervals for CPP prediction (95% CI).

RESULTS

We retrospectively analyzed 69 TCD recordings from 19 children (median age 15 years, range 3-16 years). There was a good correlation between CPP and nCPP (Spearman correlation coefficient, R = 0.67 (p < 0.0001), and a good mean correlation in time domain (R = 0.55 ± 0.42). The ability of nCPP to predict values of CPP below 70 mmHg was excellent as demonstrated by an area under the curve of 0.908 (95% CI = 0.83-0.98) using a receiver operating curve analysis. Bland-Altman analysis revealed that nCPP overestimated CPP by 19.61 mmHg with a wide 95% CI of ± 40.4 mmHg.

CONCLUSIONS

nCPP monitoring with TCD appears to be a feasible method for CPP assessment in pediatric TBI. The novel spectral CPP tested in this study has a decent correlation with invasive CPP and can predict low CPP with excellent accuracy at the 70-mmHg threshold.

Perceiving Linear-Velocity by Multiphoton Upconversion

Up to now, the rising edge of the upconversion process does not receive due attention. Herein, a demonstration utilizing the feature of the rising edge to practically detect the linear-velocity of an object is presented. Typically, upconversion processes with different numbers of participant photons would exhibit diversity in the rising edge. On this account, when the emitter is moving, the emission intensity ratio of different multiphoton processes will vary with changing linear-velocity, which enables accurate speed detection through spectral analysis. To illustrate this principle, in this work, the modeling and numerical simulation were first performed, and then experimental demonstration was carried out in which core-shell upconversion nanocrystals were elaborately designed and fabricated as the speed sensing probe to calibrate the speed of a homemade turnplate. It is believed that the present work will exploit a novel speed sensing method and find a new application for lanthanide-doped upconversion materials.

Neurocritical care monitoring of encephalopathic children with acute liver failure: A systematic review

Research on non-invasive neuromonitoring specific to PALF is limited. This systematic review identifies and synthesis the existing literature on non-invasive approaches to monitoring for neurological sequelae in patients with PALF. A series of literature searches were performed to identify all publications pertaining to five different non-invasive neuromonitoring modalities, in line with PRISMA guidelines. Each modality was selected on the basis of its potential for direct or indirect measurement of cerebral perfusion; studies on electroencephalographic monitoring were therefore not sought. Data were recorded on study design, patient population, comparator groups, and outcomes. A preponderance of observational studies was observed, most with a small sample size. Few incorporated direct comparisons of different modalities; in particular, comparison to invasive intracranial pressure monitoring was largely lacking. The integration of current evidence is considered in the context of the clinically significant distinctions between pediatric and adult ALF, as well as the implications for planning of future investigations to best support the evidence-based clinical care of these patients.

Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 ± 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 and 30 ms, respectively, of the true onsets. The algorithm's performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.

Pseudo-Bayesian Model-Based Noninvasive Intracranial Pressure Estimation and Tracking

OBJECTIVE

A noninvasive intracranial pressure (ICP) estimation method is proposed that incorporates a model-based approach within a probabilistic framework to mitigate the effects of data and modeling uncertainties.

METHODS

A first-order model of the cerebral vasculature relates measured arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) to ICP. The model is driven by the ABP waveform and is solved for a range of mean ICP values to predict the CBFV waveform. The resulting errors between measured and predicted CBFV are transformed into likelihoods for each candidate ICP in two steps. First, a baseline ICP estimate is established over five data windows of 20 beats by combining the likelihoods with a prior distribution of the ICP to yield an a posteriori distribution whose median is taken as the baseline ICP estimate. A single-state model of cerebral autoregulatory dynamics is then employed in subsequent data windows to track changes in the baseline by combining ICP estimates obtained with a uniform prior belief and model-predicted ICP. For each data window, the estimated model parameters are also used to determine the ICP pulse pressure.

RESULTS

On a dataset of thirteen pediatric patients with a variety of pathological conditions requiring invasive ICP monitoring, the method yielded for mean ICP estimation a bias (mean error) of 0.6 mmHg and a root-mean-squared error of 3.7 mmHg.

CONCLUSION

These performance characteristics are well within the acceptable range for clinical decision making.

SIGNIFICANCE

The method proposed here constitutes a significant step towards robust, continuous, patient-specific noninvasive ICP determination.

Fully automated, real-time, calibration-free, continuous noninvasive estimation of intracranial pressure in children

OBJECTIVE

In the search for a reliable, cooperation-independent, noninvasive alternative to invasive intracranial pressure (ICP) monitoring in children, various approaches have been proposed, but at the present time none are capable of providing fully automated, real-time, calibration-free, continuous and accurate ICP estimates. The authors investigated the feasibility and validity of simultaneously monitored arterial blood pressure (ABP) and middle cerebral artery (MCA) cerebral blood flow velocity (CBFV) waveforms to derive noninvasive ICP (nICP) estimates.

METHODS

Invasive ICP and ABP recordings were collected from 12 pediatric and young adult patients (aged 2-25 years) undergoing such monitoring as part of routine clinical care. Additionally, simultaneous transcranial Doppler (TCD) ultrasonography-based MCA CBFV waveform measurements were performed at the bedside in dedicated data collection sessions. The ABP and MCA CBFV waveforms were analyzed in the context of a mathematical model, linking them to the cerebral vasculature's biophysical properties and ICP. The authors developed and automated a waveform preprocessing, signal-quality evaluation, and waveform-synchronization "pipeline" in order to test and objectively validate the algorithm's performance. To generate one nICP estimate, 60 beats of ABP and MCA CBFV waveform data were analyzed. Moving the 60-beat data window forward by one beat at a time (overlapping data windows) resulted in 39,480 ICP-to-nICP comparisons across a total of 44 data-collection sessions (studies). Moving the 60-beat data window forward by 60 beats at a time (nonoverlapping data windows) resulted in 722 paired ICP-to-nICP comparisons.

RESULTS

Greater than 80% of all nICP estimates fell within ± 7 mm Hg of the reference measurement. Overall performance in the nonoverlapping data window approach gave a mean error (bias) of 1.0 mm Hg, standard deviation of the error (precision) of 5.1 mm Hg, and root-mean-square error of 5.2 mm Hg. The associated mean and median absolute errors were 4.2 mm Hg and 3.3 mm Hg, respectively. These results were contingent on ensuring adequate ABP and CBFV signal quality and required accurate hydrostatic pressure correction of the measured ABP waveform in relation to the elevation of the external auditory meatus. Notably, the procedure had no failed attempts at data collection, and all patients had adequate TCD data from at least one hemisphere. Last, an analysis of using study-by-study averaged nICP estimates to detect a measured ICP > 15 mm Hg resulted in an area under the receiver operating characteristic curve of 0.83, with a sensitivity of 71% and specificity of 86% for a detection threshold of nICP = 15 mm Hg.

CONCLUSIONS

This nICP estimation algorithm, based on ABP and bedside TCD CBFV waveform measurements, performs in a manner comparable to invasive ICP monitoring. These findings open the possibility for rational, point-of-care treatment decisions in pediatric patients with suspected raised ICP undergoing intensive care.

Real-Time Noninvasive Intracranial State Estimation Using Unscented Kalman Filter

Intracranial pressure (ICP) monitoring is desirable as a first-line measure to assist decision-making in cases of increased ICP. Clinically, non-invasive ICP monitoring is also required to avoid infection and hemorrhage in patients. The relationships among the arterial blood pressure (ABP), ICP, cerebral blood flow, and its velocity ( [Formula: see text]) measured by transcranial Doppler ultrasound measurement have been reported. However, real-time non-invasive ICP estimation using these modalities is less well documented. This paper presents a novel algorithm for real-time and non-invasive ICP monitoring with [Formula: see text] and ABP, called direct-current (DC)-ICP. The technique was compared with invasive ICP for 10 acute-brain-injury patients admitted to Cheju Halla Hospital and Gangnam Severance Hospital from July 2017 to June 2018. The inter-subject correlation coefficient between true and estimate was 0.75 and the AUCs of the ROCs for prediction of increased ICP for the DC-ICP methods were 0.83. Thus, [Formula: see text] monitoring can facilitate reliable real-time ICP tracking with our novel DC-ICP algorithm, which can provide valuable information under clinical conditions.

Identification of Pulse Onset on Cerebral Blood Flow Velocity Waveforms: A Comparative Study

The low cost, simple, noninvasive, and continuous measurement of cerebral blood flow velocity (CBFV) by transcranial Doppler is becoming a common clinical tool for the assessment of cerebral hemodynamics. CBFV monitoring can also help with noninvasive estimation of intracranial pressure and evaluation of mild traumatic brain injury. Reliable CBFV waveform analysis depends heavily on its accurate beat-to-beat delineation. However, CBFV is inherently contaminated with various types of noise/artifacts and has a wide range of possible pathological waveform morphologies. Thus, pulse onset detection is in general a challenging task for CBFV signal. In this paper, we conducted a comprehensive comparative analysis of three popular pulse onset detection methods using a large annotated dataset of 92,794 CBFV pulses—collected from 108 subarachnoid hemorrhage patients admitted to UCLA Medical Center. We compared these methods not only in terms of their accuracy and computational complexity, but also for their sensitivity to the selection of their parameters' values. The results of this comprehensive study revealed that using optimal values of the parameters obtained from sensitivity analysis, one method can achieve the highest accuracy for CBFV pulse onset detection with true positive rate (TPR) of 97.06% and positive predictivity value (PPV) of 96.48%, when error threshold is set to just less than 10 ms. We conclude that the high accuracy and low computational complexity of this method (average running time of 4ms/pulse) makes it a reliable algorithm for CBFV pulse onset detection.

Noninvasive Transorbital Assessment of the Optic Nerve Sheath in Children: Relationship Between Optic Nerve Sheath Diameter, Deformability Index, and Intracranial Pressure

BACKGROUND

Measurement of optic nerve sheath diameter (ONSD) is a promising technique for noninvasive assessment of intracranial pressure (ICP), but has certain limitations. A recent study showed that the deformability index (DI), a dynamic parameter quantifying the pulsatile nature of the optic nerve sheath, could differentiate between patients with high vs normal ICP.

OBJECTIVE

To further evaluate the diagnostic accuracy of the DI, when interpreted together with ONSD.

METHODS

This prospective study included children undergoing invasive ICP measurement as part of their clinical management. Ultrasound images of the optic nerve sheath were acquired prior to measuring ICP, the images were further processed to obtain the DI. Patients were dichotomized into high (≥20 mm Hg) or normal ICP groups and compared using the Mann-Whitney U-test. Diagnostic accuracy was described using area under the receiver operating characteristic curve (AUC), sensitivity and specificity, correlation between DI, ONSD, and ICP was investigated using linear regression.

RESULTS

A total of 28 patients were included (19 high ICP). The DI was lower in the high ICP group (0.105 vs 0.28, P = .001). AUC was 0.87, and a cut-off value of DI ≤ 0.185 demonstrated sensitivity of 89.5% and specificity of 88.9%. Diagnostic accuracy improved when combining DI with ONSD (AUC 0.98, sensitivity 94.7%, specificity 88.9%) and correlation with ICP improved when combined analysis of DI and ONSD was performed (Pearson correlation coefficient: 0.82 vs 0.42, respectively, P = .012).

CONCLUSION

The DI was significantly lower for patients with high vs normal ICP. This relationship improved further when the DI and ONSD were interpreted together.

Data-Augmented Modeling of Intracranial Pressure

Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction.

Utility of the Tympanic Membrane Pressure Waveform for Non-invasive Estimation of The Intracranial Pressure Waveform

Time domain analysis of the intracranial pressure (ICP) waveform provides important information about the intracranial pressure-volume reserve capacity. The aim here was to explore whether the tympanic membrane pressure (TMP) waveform can be used to non-invasively estimate the ICP waveform. Simultaneous invasive ICP and non-invasive TMP signals were measured in a total of 28 individuals who underwent invasive ICP measurements as a part of their clinical work up (surveillance after subarachnoid hemorrhage in 9 individuals and diagnostic for CSF circulation disorders in 19 individuals). For each individual, a transfer function estimate between the invasive ICP and non-invasive TMP signals was established in order to explore the potential of the method. To validate the results, ICP waveform parameters including the mean wave amplitude (MWA) were computed in the time domain for both the ICP estimates and the invasively measured ICP. The patient-specific non-invasive ICP signals predicted MWA rather satisfactorily in 4/28 individuals (14%). In these four patients the differences between original and estimated MWA were <1.0 mmHg in more than 50% of observations, and <0.5 mmHg in more than 20% of observations. The study further disclosed that the cochlear aqueduct worked as a physical lowpass filter.

Regression-based noninvasive estimation of intracranial pressure

Monitoring of intracranial pressure (ICP) is indicated in patients with a variety of conditions affecting the brain and cerebrospinal fluid space. The measurement of ICP, however, is highly invasive as it requires placement of a catheter in the brain tissue or cerebral ventricular spaces. Several noninvasive techniques have been proposed to overcome this issue, and one class of approaches is based on analyzing cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP) waveforms to infer ICP. Here, we analyze a physiologic model linking ICP to CBFV and ABP and present a regression-based approach to estimating ICP. We tested the model on 20 datasets recorded from three patients in intensive care. Our estimates achieve a mean error (bias) of -1.12 mmHg and a standard deviation of the error of 5.56 mmHg, for a root-mean-square error of 5.68 mmHg, when compared against the invasive ICP measurement. Since transcranial Doppler ultrasound based CBFV measurements depend on the Doppler angle φ between the direction of the ultrasound beam and the (main) direction of blood flow velocity, we investigated the robustness of our ICP estimates against variations in φ. Our results show a change in the estimated ICP that is <;1 mmHg if we assume φ ~ N(μ; σ²), with μ = 0 and σ = 10°.

Cerebrovascular Compliance Within the Rigid Confines of the Skull

Pulsatile blood flow is generally mediated by the compliance of blood vessels whereby they distend locally and momentarily to accommodate the passage of the pressure wave. This freedom of the blood vessels to exercise their compliance may be suppressed within the confines of the rigid skull. The effect of this on the mechanics of pulsatile blood flow within the cerebral circulation is not known, and the situation is compounded by experimental access difficulties. We present an approach which we have developed to overcome these difficulties in a study of the mechanics of pulsatile cerebral blood flow. The main finding is that while the innate compliance of cerebral vessels is indeed suppressed within the confines of the skull, this is compensated somewhat by compliance provided by other “extravascular” elements within the skull. The net result is what we have termed “intracranial compliance,” which we argue is more pertinent to the mechanics of pulsatile cerebral blood flow than is intracranial pressure.

Non-invasive Estimation of the Intracranial Pressure Waveform from the Central Arterial Blood Pressure Waveform in Idiopathic Normal Pressure Hydrocephalus Patients

This study explored the hypothesis that the central aortic blood pressure (BP) waveform may be used for non-invasive estimation of the intracranial pressure (ICP) waveform. Simultaneous invasive ICP and radial artery BP waveforms were measured in 29 individuals with idiopathic normal pressure hydrocephalus (iNPH). The central aortic BP waveforms were estimated from the radial artery BP waveforms using the SphygmoCor system. For each individual, a transfer function estimate between the central aortic BP and the invasive ICP waveforms was found (Intra-patient approach). Thereafter, the transfer function estimate that gave the best fit was chosen and applied to the other individuals (Inter-patient approach). To validate the results, ICP waveform parameters were calculated for the estimates and the measured golden standard. For the Intra-patient approach, the mean absolute difference in invasive versus non-invasive mean ICP wave amplitude was 1.9 ± 1.0 mmHg among the 29 individuals. Correspondingly, the Inter-patient approach resulted in a mean absolute difference of 1.6 ± 1.0 mmHg for the 29 individuals. This method gave a fairly good estimate of the wave for about a third of the individuals, but the variability is quite large. This approach is therefore not a reliable method for use in clinical patient management.

Comparison of Different Calibration Methods in a Non-invasive ICP Assessment Model

OBJECTIVE

Previously we described the method of continuous intracranial pressure (ICP) estimation using arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). The model was constructed using reference patient data. Various individual calibration strategies were used in the current attempt to improve the accuracy of this non-invasive ICP (nICP) assessment tool.

MATERIALS AND METHODS

Forty-one patients (mean, 52 years; range, 18-77 years) with severe brain injuries were studied. CBFV in the middle cerebral artery (MCA), ABP and invasively assessed ICP were simultaneously recorded for 1 h. Recording was repeated at days 2, 4 and 7. In the first recording, invasively assessed ICP was recorded to calibrate the nICP procedure by means of either a constant shift of nICP (snICP), a constant shift of nICP/ABP ratio (anICP) or by including this recording for a model reconstruction (cnICP). At follow-up days, the calibrated nICP procedures were applied and the results compared to the original nICP.

RESULTS

In 76 follow-up recordings, the mean differences (Bias), the SD and the mean absolute differences (ΔICP) between ICP and the nICP methods were (in mmHg): nICP, -5.6 ± 5.72, 6.5; snICP, +0.7 ± 6.98, 5.5, n.s.; anICP, +1.0 ± 7.22, 5.6, n.s.; cnICP, -3.4 ± 5.68, 5.4, p < 0.001. In patients with craniotomy (n = 19), the nICP was generally higher than ICP. This overestimation could be reduced by cnICP calibration, but not completely avoided.

DISCUSSION

Constant shift calibrations (snICP, anICP) decrease the Bias to ICP, but increase SD and, therefore, increase the 95% confidence interval (CI = 2 × SD). This calibration method cannot be recommended. Compared to nICP, the cnICP method reduced the Bias and slightly reduced SD, and showed significantly decreased ΔICP. Compared to snICP and anICP, the Bias was higher. This effect was probably caused by the patients with craniotomy.

CONCLUSION

The cnICP calibration method using initial recordings for model reconstruction showed the best results.

An Embedded Device for Real-Time Noninvasive Intracranial Pressure Estimation

OBJECTIVE

The monitoring of intracranial pressure (ICP) is indicated for diagnosing and guiding therapy in many neurological conditions. Current monitoring methods, however, are highly invasive, limiting their use to the most critically ill patients only. Our goal is to develop and test an embedded device that performs all necessary mathematical operations in real-time for noninvasive ICP (nICP) estimation based on a previously developed model-based approach that uses cerebral blood flow velocity (CBFV) and arterial blood pressure (ABP) waveforms.

MATERIALS AND METHODS

The nICP estimation algorithm along with the required preprocessing steps were implemented on an NXP LPC4337 microcontroller unit (MCU). A prototype device using the MCU was also developed, complete with display, recording functionality, and peripheral interfaces for ABP and CBFV monitoring hardware.

RESULTS

The device produces an estimate of mean ICP once per minute and performs the necessary computations in 410 ms, on average. Real-time nICP estimates differed from the original batch-mode MATLAB implementation of theestimation algorithm by 0.63 mmHg (root-mean-square error).

CONCLUSIONS

We have demonstrated that real-time nICP estimation is possible on a microprocessor platform, which offers the advantages of low cost, small size, and product modularity over a general-purpose computer. These attributes take a step toward the goal of real-time nICP estimation at the patient's bedside in a variety of clinical settings.

Analysis of a Minimally Invasive Intracranial Pressure Signals During Infusion at the Subarachnoid Spinal Space of Pigs

OBJECTIVE

We developed a new minimally invasive method for intracranial pressure monitoring (ICPMI). The objective of this project is to verify the similarities between the ICPMI and the invasive method (ICPInv), for different components of the intracranial pressure signal-namely, the mean value (trend) as well as its pulsatile component.

MATERIALS AND METHODS

A 9 kg anesthetized pig was used for simultaneous ICP monitoring with both methods. ICP was increased by performing ten infusions of 6 ml 0.9% saline into the spinal subarachnoid space, using a catheter implanted in the lumbar region. For correlation analysis, the signals were decomposed into two components-trend and pulsatile signals. Pearson correlation coefficient was calculated between ICPInv and ICPMI.

RESULTS

During the infusions, the correlation between the pulsatile components of the signals was above 0.5 for most of the time. The signal trends showed a good agreement (correlation above 0.5) for most of the time during infusions.

CONCLUSIONS

The ICPMI signal trends showed a good linear agreement with the signal obtained invasively. Based on the waveform analysis of the pulsatile component of ICP, our results indicate the possibility of using the minimally invasive method for assessing the neuroclinical state of the patient.

Transcranial Doppler: a stethoscope for the brain-neurocritical care use

Transcranial Doppler (TCD) ultrasonography is a noninvasive bedside monitoring technique that can evaluate cerebral blood flow hemodynamics in the intracranial arterial vasculature. TCD allows assessment of linear cerebral blood flow velocity, with a high temporal resolution and is inexpensive, reproducible, and portable. The aim of this review is to provide an overview of the most commonly used TCD derived signals and measurements used commonly in neurocritical care. We describe both basic (flow velocity, pulsatility index) and advanced concepts, including critical closing pressure, wall tension, autoregulation, noninvasive intracranial pressure, brain compliance, and cerebrovascular time constant; we also describe the clinical applications of TCD to highlight their utility in the diagnosis and monitoring of cerebrovascular diseases as the "stethoscope for the brain."

Invasive and noninvasive means of measuring intracranial pressure: a review

Measurement of intracranial pressure (ICP) can be invaluable in the management of critically ill patients. Cerebrospinal fluid is produced by the choroid plexus in the brain ventricles (a set of communicating chambers), after which it circulates through the different ventricles and exits into the subarachnoid space around the brain, where it is reabsorbed into the venous system. If the fluid does not drain out of the brain or get reabsorbed, the ICP increases, which may lead to brain damage or death. ICP elevation accompanied by dilatation of the cerebral ventricles is termed hydrocephalus, whereas ICP elevation accompanied by normal or small ventricles is termed idiopathic intracranial hypertension.

OBJECTIVE

We performed a comprehensive literature review on how to measure ICP invasively and noninvasively.

APPROACH

This review discusses the advantages and disadvantages of current invasive and noninvasive approaches.

MAIN RESULTS

Invasive methods remain the most accurate at measuring ICP, but they are prone to a variety of complications including infection, hemorrhage and neurological deficits. Ventricular catheters remain the gold standard but also carry the highest risk of complications, including difficult or incorrect placement. Direct telemetric intraparenchymal ICP monitoring devices are a good alternative. Noninvasive methods for measuring and evaluating ICP have been developed and classified in five broad categories, but have not been reliable enough to use on a routine basis. These methods include the fluid dynamic, ophthalmic, otic, and electrophysiologic methods, as well as magnetic resonance imaging, transcranial Doppler ultrasonography (TCD), cerebral blood flow velocity, near-infrared spectroscopy, transcranial time-of-flight, spontaneous venous pulsations, venous ophthalmodynamometry, optical coherence tomography of retina, optic nerve sheath diameter (ONSD) assessment, pupillometry constriction, sensing tympanic membrane displacement, analyzing otoacoustic emissions/acoustic measure, transcranial acoustic signals, visual-evoked potentials, electroencephalography, skull vibrations, brain tissue resonance and the jugular vein.

SIGNIFICANCE

This review provides a current perspective of invasive and noninvasive ICP measurements, along with a sense of their relative strengths, drawbacks and areas for further improvement. At present, none of the noninvasive methods demonstrates sufficient accuracy and ease of use while allowing continuous monitoring in routine clinical use. However, they provide a realizable ICP measurement in specific patients especially when invasive monitoring is contraindicated or unavailable. Among all noninvasive ICP measurement methods, ONSD and TCD are attractive and may be useful in selected settings though they cannot be used as invasive ICP measurement substitutes. For a sufficiently accurate and universal continuous ICP monitoring method/device, future research and developments are needed to integrate further refinements of the existing methods, combine telemetric sensors and/or technologies, and validate large numbers of clinical studies on relevant patient populations.