Intracranial Pressure Waveform Research Articles

Impact of different blood pressure targets on cerebral hemodynamics in septic shock: A prospective pilot study protocol-SEPSIS-BRAIN.

Septic shock, a life-threatening condition, can result in cerebral dysfunction and heightened mortality rates. In these patients, disturbances in cerebral hemodynamics, as reflected by impairment of myogenic cerebral autoregulation (CA), metabolic regulation, expressed by critical closing pressure (CrCP) and reductions in intracranial compliance (ICC), can adversely impact septic shock outcomes. The general recommendation is to maintain a target mean arterial pressure (MAP) of 65 mmHg but the effect of different MAP targets on cerebral hemodynamics in these patients is not clear and optimal targets might be dependent on the status of CA. This protocol aims to assess the cerebral hemodynamics profile at different pressure targets in septic shock patients. Prospective, non-randomized, single-center trial, which will study cerebral hemodynamics in patients with septic shock within 48 hours of its onset. Patients will be studied at their baseline MAP and at three MAP targets (T1: 65, T2: 75, T3: 85 mmHg). Cerebral hemodynamics will be assessed by transcranial Doppler (TCD) and a skull micro-deformation sensor (B4C). Dynamic CA will be expressed by the autoregulation index (ARI), calculated by transfer function analysis, using fluctuations of MAP as input and corresponding oscillations in cerebral blood velocity (CBv). The instantaneous relationship between arterial blood pressure and CBv will be used to estimate CrCP and resistance-area product (RAP) for each cardiac cycle using the first harmonic method. The B4C will access ICC by intracranial pressure waveforms (P2/P1). The primary aim is to assess cerebral hemodynamics (ARI, CrCP, RAP, and P2/P1) at different targets of MAP in septic shock patients. Our secondary objective is to assess cerebral hemodynamics at 65mmHg (target recommended by guidelines). In addition, we will assess the correlation between markers of organ dysfunction (such as lactate levels, vasoactive drugs usage, SOFA score, and delirium) and CA. The results of this study may help to understand the effect of the recommended MAP and variations in blood pressure in patients with septic shock and impaired CA and ICC. Furthermore, the results can assist large trials in establishing new hypotheses about neurological management in this group of patients. Approval was obtained from the local Ethics Committee (28134720.1.0000.0048). It is anticipated that the results of this study will be presented at national and international conferences and will be published in peer-reviewed journals in 2024 and 2025. Trial registration number: NCT05833607. https://clinicaltrials.gov/study/NCT05833607.

Noninvasive Method Using Mechanical Extensometer for the Estimation of Intracranial Compliance by Repeated Measures Agreement Analysis.

Intracranial compliance refers to the relationship between changes in volume and the resultant changes in intracranial pressure (ICP). This study aimed to assess the agreement of a noninvasive ICP waveform device for the estimation of compliance compared with invasive ICP monitoring employing three distinct methods. We conducted a retrospective analysis of ICP waveform morphology recorded through both invasive (external ventricular drain) and noninvasive (mechanical extensometer) methods in adult patients with acute brain injury admitted to a neurointensive care unit between August 2021 and August 2022. Compliance was calculated as the amplitude of the fundamental component of cerebral arterial blood volume (estimated with concurrent Transcranial Doppler [TCD] recordings), divided by the amplitude of the fundamental component of the invasive and noninvasive ICP waveforms. Subsequently, we assessed the agreement between invasive and noninvasive intracranial compliance by repeated measures correlation coefficient analysis using three methods: TCD-derived, P2/P1 ratio, and time-to-peak (TTP). A linear mixed-effects model was used to compute the concordance correlation coefficient, total deviation index, and coefficient of individual agreement. Coverage probability plot was calculated to estimate the percent of observations within different cut points for each of the three methods. A total of 21 patients were identified for this study. Repeated measures correlation analysis showed a strong correlation (R = 0.982, 95% confidence interval [0.980-0.984], p < 0.0001) between log-transformed noninvasive and invasive compliance. Agreement statistics for TCD, P2/P1 ratio, and TTP indicated that although the concordance correlation coefficient was highest for log(TCD) values, TTP and P2:P1 ratio measures had better agreement with total deviation index and coverage probability plot analyses. Repeated measures correlations suggest that ICP waveform analyses may offer a more accurate estimate of compliance than TCD-derived methods for noninvasive ICP monitoring. Further validations studies are warranted to confidently establish this method as an indicator of intracranial compliance.

Observational study of intracranial compliance analysis in neurologically healthy pediatric patients using a non-invasive device

Information about the morphology of the intracranial pressure waveform, as well as the variations in intracranial pressure (ICP) and compliance in pediatric patients are essential to diagnose and predict the progression of various neurological conditions. However, there is no information on the morphology of the IP waveform in neurologically healthy pediatric patients. In the present study, intracranial compliance was therefore analyzed in neurologically healthy patients with the aid of a noninvasive device. The study was an observational, cross-sectional study. Fifty-five neurologically healthy participants were included. Data on intracranial compliance with the patient in two positions, lying down (0°) and seated (45°), were collected with a noninvasive extracranial sensor, which allowed the intracranial pressure waveforms to be recorded. The values of the ratio P2/P1 were then analyzed. A questionnaire (with a scale from zero to ten, where ten corresponds to the highest level of satisfaction) was applied for patients to evaluate their satisfaction with the sensor. Patients were 10 years old (average), and most of them were (58%). Mean P2/P1 ratio was 0.94 (sd = 0.14) in the supine position and 0.91 (sd = 0.15) in the seated position. Participants were satisfied with the length of time for which the equipment was used (9.8, sd = 0.71). The device did not cause any discomfort. The noninvasive method used was well accepted by the patients. Intracranial compliance values were determined by analysis of the P2/P1 ratio in neurologically healthy pediatric population.Trial registration: Brazilian Registry of Clinical Trials Identifier: RBR-5j74ddg.

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

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. Healthy participants, both sexes, aged from 9 to 74 years old were monitored for 5min 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. 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). 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. Brazilian Registry of Clinical Trials (RBR-9nv2h42), retrospectively registered 05/24/2022. UTN: U1111-1266-8006.

Deriving Automated Device Metadata From Intracranial Pressure Waveforms: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury ICU Physiology Cohort Analysis.

Treatment for intracranial pressure (ICP) has been increasingly informed by machine learning (ML)-derived ICP waveform characteristics. There are gaps, however, in understanding how ICP monitor type may bias waveform characteristics used for these predictive tools since differences between external ventricular drain (EVD) and intraparenchymal monitor (IPM)-derived waveforms have not been well accounted for. We sought to develop a proof-of-concept ML model differentiating ICP waveforms originating from an EVD or IPM. We examined raw ICP waveform data from the ICU physiology cohort within the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury multicenter study. Nested patient-wise five-fold cross-validation and group analysis with bagged decision trees (BDT) and linear discriminant analysis were used for feature selection and fair evaluation. Nine patients were kept as unseen hold-outs for further evaluation. ICP waveform data totaling 14,110 hours were included from 82 patients (EVD, 47; IPM, 26; both, 9). Mean age, Glasgow Coma Scale (GCS) total, and GCS motor score upon admission, as well as the presence and amount of midline shift, were similar between groups. The model mean area under the receiver operating characteristic curve (AU-ROC) exceeded 0.874 across all folds. In additional rigorous cluster-based subgroup analysis, targeted at testing the resilience of models to cross-validation with smaller subsets constructed to develop models in one confounder set and test them in another subset, AU-ROC exceeded 0.811. In a similar analysis using propensity score-based rather than cluster-based subgroup analysis, the mean AU-ROC exceeded 0.827. Of 842 extracted ICP features, 62 were invariant within every analysis, representing the most accurate and robust differences between ICP monitor types. For the nine patient hold-outs, an AU-ROC of 0.826 was obtained using BDT. The developed proof-of-concept ML model identified differences in EVD- and IPM-derived ICP signals, which can provide missing contextual data for large-scale retrospective datasets, prevent bias in computational models that ingest ICP data indiscriminately, and control for confounding using our model's output as a propensity score by to adjust for the monitoring method that was clinically indicated. Furthermore, the invariant features may be leveraged as ICP features for anomaly detection.

Peak detection in intracranial pressure signal waveforms: a comparative study

BackgroundThe monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms.MethodsPeak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise.ResultsThe quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ≤10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le 10$$\\end{document} ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data.ConclusionWhile machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.

Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning

Multimodal monitoring (MMM) in the intensive care unit (ICU) has become increasingly sophisticated with the integration of neurophysical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes. This manuscript reviewed current neuromonitoring tools, focusing on intracranial pressure, cerebral electrical activity, metabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the ICU were discussed. Invasive monitoring includes analysis of intracranial pressure waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, thermal diffusion flowmetry, electrocorticography, depth electroencephalography, and cerebral microdialysis. Noninvasive measures include transcranial Doppler, tympanic membrane displacement, near-infrared spectroscopy, optic nerve sheath diameter, positron emission tomography, and systemic hemodynamic monitoring including heart rate variability analysis. The neurophysical basis and clinical relevance of each method within the ICU setting were examined. Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools, helping clinicians make more accurate and timely decisions. These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies. MMM, grounded in neurophysics, offers a more nuanced understanding of cerebral physiology and disease in the ICU. Although each modality has its strengths and limitations, its integrated use, especially in combination with machine learning algorithms, can offer invaluable information for individualized patient care.

A Comprehensive Perspective on Intracranial Pressure Monitoring and Individualized Management in Neurocritical Care: Results of a Survey with Global Experts.

Numerous trials have addressed intracranial pressure (ICP) management in neurocritical care. However, identifying its harmful thresholds and controlling ICP remain challenging in terms of improving outcomes. Evidence suggests that an individualized approach is necessary for establishing tolerance limits for ICP, incorporating factors such as ICP waveform (ICPW) or pulse morphology along with additional data provided by other invasive (e.g., brain oximetry) and noninvasive monitoring (NIM) methods (e.g., transcranial Doppler, optic nerve sheath diameter ultrasound, and pupillometry). This study aims to assess current ICP monitoring practices among experienced clinicians and explore whether guidelines should incorporate ancillary parameters from NIM and ICPW in future updates. We conducted a survey among experienced professionals involved in researching and managing patients with severe injury across low-middle-income countries (LMICs) and high-income countries (HICs). We sought their insights on ICP monitoring, particularly focusing on the impact of NIM and ICPW in various clinical scenarios. From October to December 2023, 109 professionals from the Americas and Europe participated in the survey, evenly distributed between LMIC and HIC. When ICP ranged from 22 to 25 mm Hg, 62.3% of respondents were open to considering additional information, such as ICPW and other monitoring techniques, before adjusting therapy intensity levels. Moreover, 77% of respondents were inclined to reassess patients with ICP in the 18-22mm Hg range, potentially escalating therapy intensity levels with the support of ICPW and NIM. Differences emerged between LMIC and HIC participants, with more LMIC respondents preferring arterial blood pressure transducer leveling at the heart and endorsing the use of NIM techniques and ICPW as ancillary information. Experienced clinicians tend to personalize ICP management, emphasizing the importance of considering various monitoring techniques. ICPW and noninvasive techniques, particularly in LMIC settings, warrant further exploration and could potentially enhance individualized patient care. The study suggests updating guidelines to include these additional components for a more personalized approach to ICP management.

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.

Effect of Decompressive Craniectomy on Intracranial Pressure Waveforms and Vascular Reactivity: A Systematic Scoping Review.

Decompressive craniectomy (DC) primarily aims at decreasing intracranial pressure (ICP) by allowing for the brain tissue to expand. However, it is uncertain to what extent DC impacts the transmission of vasogenic slow waves and thus the validity and utility of the pressure reactivity index (PRx). The purpose of this systematically performed scoping review is to assess the current knowledge of the impact of DC on ICP waveforms and measures of vascular reactivity. This scoping review considered studies including patients over 18 years old suffering from acute brain injuries (ABIs), who underwent secondary DC and had a perioperative (pre/post-DC) recording of ICP or waveform analysis. A search was conducted in EMBASE, PubMed, Web of Science, Scopus, and Medline from November 2023 till January 2024, yielding 787 studies. Duplicated studies were automatically removed, and two researchers independently screened the remaining studies. After examining 586 titles and abstracts, 38 full-text studies were assessed for eligibility, and 4 studies were included in the final review. The review suggests that cerebrovascular reactivity and slow waves are altered after DC, with positive PRx values and reduced slow power. One study suggested that the nature of slow waves and interactions is on the whole largely preserved. However, the findings should be interpreted with caution due to methodological limitations and the low number of studies.

NON-INVASIVE CENTRAL BLOOD PRESSURE AND INTRACRANIAL WAVEFORM ASSESSMENT IN HYPERTENSIVE PATIENTS

Objective: Hypertension (HT) remains as the leading cause of death around the globe and brain diseases, such as cognitive decline. The association between HT and cerebrovascular diseases is strong, mainly with stroke and cognitive impairment, but the mechanistic bases remain to be established. The objective of this study is to observe central blood pressure, arterial stiffness and intracranial pressure in long-term chronic hypertensive patients. Design and method: Patients above 18 years were assessed from November 2022 to August 2023 with non-invasive central blood pressure and intracranial pressure waveform measurements in a cross-sectional analysis. Peripheral and central blood pressure parameters were obtained using a validated oscillometric device (Mobil-O-Graph). Non-invasive intracranial pressure measurement was performed with the validated Brain4Care (B4C) sensor. Using the B4C non-invasive evaluation, the cut-off point identified to define ICHT by the P2/P1 ratio was &gt; 1.2, and the cut-off for time to peak (TTP) was &gt; 0.25 seconds. To compare P2/P1 and TTP values between the categories of quantitative variables, the chi-square test was used; the Mann Whitney or ANOVA tests were used for bivariate analyses; and for correlations, the Spearman test was used. It was adopted as significant at p &lt; 0,05. Results: Non-invasive central blood pressure, arterial stiffness, and intracranial pressure waveform assessment were performed in 145 patients with essential long-term hypertension over a period of ten months. The median age was 69.0 (61.8 – 75.7) years, 77.9% were female, and the median BMI was 29.0 (25.4 – 33.1) kg/m2. The average time since the hypertension diagnosis was 20.0 years. The median value of P2/P1 ratio for all cohort was 1.4 (1.2 – 1.5) and TTP 0.24 (0.21 – 0.29) seconds. A bivariate analysis was performed considering ICHT or not and the parameters of central BP and PWV assessments, with findings of higher central SBP, central DBP, and peripheral DBP among patients with ICHT by the parameter of P2/P1. Conclusions: Central systolic blood pressure levels are more linked to intracranial hypertension, while diastolic blood pressure values are similar, raising questions about the most suitable blood pressure assessment method for brain disorders.

Associations Between Intracranial Pressure Extremes and Continuous Metrics of Cerebrovascular Pressure Reactivity in Acute Traumatic Neural Injury: A Scoping Review.

Cerebrovascular pressure reactivity plays a key role in maintaining constant cerebral blood flow. Unfortunately, this mechanism is often impaired in acute traumatic neural injury states, exposing the already injured brain to further pressure-passive insults. While there has been much work on the association between impaired cerebrovascular reactivity following moderate/severe traumatic brain injury (TBI) and worse long-term outcomes, there is yet to be a comprehensive review on the association between cerebrovascular pressure reactivity and intracranial pressure (ICP) extremes. Therefore, we conducted a systematic review of the literature for all studies presenting a quantifiable statistical association between a continuous measure of cerebrovascular pressure reactivity and ICP in a human TBI cohort. The methodology described in the Cochrane Handbook for Systematic Reviews was used. BIOSIS, Cochrane Library, EMBASE, Global Health, MEDLINE, and SCOPUS were all searched from their inceptions to March of 2023 for relevant articles. Full-length original works with a sample size of ≥10 patients with moderate/severe TBI were included in this review. Data were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A total of 16 articles were included in this review. Studies varied in population characteristics and statistical tests used. Five studies looked at transcranial Doppler-based indices and 13 looked at ICP-based indices. All but two studies were able to present a statistically significant association between cerebrovascular pressure reactivity and ICP. Based on the findings of this review, impaired reactivity seems to be associated with elevated ICP and reduced ICP waveform complexity. This relationship may allow for the calculation of patient-specific ICP thresholds, past which cerebrovascular reactivity becomes persistently deranged. However, further work is required to better understand this relationship and improve algorithmic derivation of such individualized ICP thresholds.

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

Intracranial pressure (ICP) is a vital parameter that is continuously monitored in patients with severe brain injury and imminent intracranial hypertension. To estimate intracranial pressure without intracranial probes based on transcutaneous near infrared spectroscopy (NIRS). 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. 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. 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.

Management of shunt dysfunction using noninvasive intracranial pressure waveform monitoring: illustrative case.

Normal pressure hydrocephalus (NPH) treatment consists of using valves for drainage, as it is for hydrocephalus in general. Despite this, complications can occur, putting the patient at risk, and neurological monitoring is crucial. A 61-year-old male, who had been diagnosed with NPH 3 years prior and was being treated with a ventriculoperitoneal shunt with a programmable valve, presented to the emergency department because of a traumatic brain injury due to a fall from standing height. No previous complications were reported. He had an altered intracranial pressure (ICP) waveform in the emergency room when monitored with the brain4care device, with a P2/P1 ratio of 1.6. Imaging helped to confirm shunt dysfunction. Revision surgery normalized the ratio to 1.0, and the patient was discharged. Upon return after 14 days, an outpatient analysis revealed a ratio of 0.6, indicating improvement. In selected cases of NPH, noninvasive ICP waveform morphology analysis can be effective as a diagnostic aid, as well as in the pre- and postsurgical follow-up, given the possibility of comparing the values of ICP preoperatively and immediately postoperatively and the outpatient P2/P1 ratio, helping to manage these patients.

A Soft Robotic Actuator System for In Vivo Modeling of Normal Pressure Hydrocephalus.

The intracranial pressure (ICP) affects the dynamics of cerebrospinal fluid (CSF) and its waveform contains information that is of clinical importance in medical conditions such as hydrocephalus. Active manipulation of the ICP waveform could enable the investigation of pathophysiological processes altering CSF dynamics and driving hydrocephalus. A soft robotic actuator system for intracranial pulse pressure amplification was developed to model normal pressure hydrocephalus in vivo. Different end actuators were designed for intraventricular implantation and manufactured by applying cyclic tensile loading on soft rubber tubing. Their mechanical properties were investigated, and the type that achieved the greatest pulse pressure amplification in an in vitro simulator of CSF dynamics was selected for application in vivo. A hydraulic actuation device based on a linear voice coil motor was developed to enable automated and fast operation of the end actuators. The combined system was validated in an acute ovine pilot in vivo study. in vitro results show that variations in the used materials and manufacturing settings altered the end actuator's dynamic properties, such as the pressure-volume characteristics. In the in vivo model, a cardiac-gated actuation volume of 0.125 mL at a heart rate of 62 bpm caused an increase of 205% in mean peak-to-peak amplitude but only an increase of 1.3% in mean ICP. The introduced soft robotic actuator system is capable of ICP waveform manipulation. Continuous amplification of the intracranial pulse pressure could enable in vivo modeling of normal pressure hydrocephalus and shunt system testing under pathophysiological conditions to improve therapy for hydrocephalus.

Propofol effects over intracranial pressure waveform and cerebral hemodynamics: A case report

Introduction: Few reports have been dedicated on assessing cerebral hemodynamics (CH) and intracranial compliance (ICC) in non-primary neurological patients. Objective: The present study aims to observe the anesthetic influence of propofol on CH and ICC using noninvasive monitoring techniques in the immediate postoperative period. Methodology: This is a case report in which CH and ICC were assessed noninvasively using transcranial Doppler (TCD) and a skull deformation sensor (B4C) respectively, in the immediate postoperative period of a patient just after cardiac revascularization. After exclusion of volemic depletion, TCD and B4C parameters indicated reduction in cerebral blood flow what would be attributed to propofol infusion during surgery. After endovenous additional volume administration and arterial pressure elevation, TCD and B4C parameters improved. Final Considerations: It was possible observing cerebrovascular influences of propofol in the immediate postoperative period, by means of neurological ancillary techniques. Noninvasive neuromonitoring is a way to assess changes in brain physiology that may occur subtly and influence in non-primary neurological patients outcomes.

Estimating intracranial parameters using an inverse mathematical model with viscoelastic elements that closely predicts complex ICP morphologies

The quantitative relationship between arterial blood pressure (ABP) and intracranial pressure (ICP) waveforms has not been adequately explained. We hypothesized that the ICP waveform results from interferences between propagating and reflected pressure waves occurring in the cranium following the initiating arterial waveform. To demonstrate cranial effects on interferences between waves and generation of an ICP waveform morphology, we modified our previously reported mathematical model to include viscoelastic elements that affect propagation velocity. Using patient data, we implemented an inverse model methodology to generate simulated ICP waveforms in response to given ABP waveforms. We used an open database of traumatic brain injury patients and studied 65 pairs of ICP and ABP waveforms from 13 patients (five pairs from each). Incorporating viscoelastic elements into the model resulted in model-generated ICP waveforms that very closely resembled the measured waveforms with a 16-fold increase in similarity index relative to the model with only pure elasticity elements. The mean similarity index for the pure elasticity model was 0.06 ± 0.12 SD, compared to 0.96 ± 0.28 SD for the model with viscoelastic components. The normalized root mean squared error (NRMSE) improved substantially for the model with viscoelastic elements compared to the model with pure elastic elements (NRMSE of 2.09% ± 0.62 vs. 15.2% ± 4.8, respectively). The ability of the model to generate complex ICP waveforms indicates that the model may indeed reflect intracranial dynamics. Our results suggest that the model may allow the estimation of intracranial biomechanical parameters with potential clinical significance. It represents a first step in the estimation of inaccessible intracranial parameters.

An Ultrasonic Transceiver for Non-Invasive Intracranial Pressure Sensing.

This paper presents a 9-mW ultrasonic through-transmission transceiver (TRX) for portable, non-invasive intracranial pressure (ICP) sensing. It employs two ultrasound transducers placed at the temporal bone windows to measure changes in the ultrasonic time-of-flight (ToF), based on which the skull expansion and the corresponding ICP waveform are derived. Key components include a high-efficiency Class-DE power amplifier (PA) with 95% efficiency and an output swing of 15.8 VPP, along with a successive approximation register (SAR) delay-locked loop (DLL)-based time-to-digital converter (TDC) with 29.8 ps resolution and 122 ns range. Other than electrical characterization, the sensor is validated through two demonstrations using a water tank setup and a human head phantom setup, respectively. It demonstrates a high correlation of R2 = 0.93 with a medical-grade invasive ICP sensor. The proposed system offers high accuracy, low power consumption, and reliable performance, making it a promising solution for real-time, portable, non-invasive ICP monitoring in various clinical settings.

Is the ICP pulse waveform P2/P1 ratio during -6° head-down tilt associated with relative VO2 peak? A non-invasive intracranial compliance monitoring approach

BackgroundSpaceflights influence intracranial compliance (ICC). P2/P1 ratio, from the intracranial pressure (ICP) waveform, provides information about ICC. Additionally, non-invasive methods for ICC monitoring are needed for spaceflights. Furthermore, astronauts try to maintain good levels of cardiorespiratory fitness before and during spaceflights, not only to sustain exploratory missions, but also to prevent diseases in extreme environments. Objectiveto correlate cardiorespiratory fitness levels with the P2/P1 ratio during a microgravity analog [-6° head-down tilt (HDT)]. Method34 individuals (11 women), mean age of 31.7 (±6.3) years and BMI 24.2 (±3.2) performed a cardiopulmonary exercise testing (CPET) with an incremental protocol on a cycle ergometer to determine the cardiopulmonary fitness through peak relative oxygen uptake (VO2 peak) of each individual. On the second test, which was conducted in an interval of 15 days of the CPET, participants remained for 30 min at HDT with P2/P1 ratio acquired using a non-invasive strain gauge sensor. The average of the last 5 min was used for analysis. The mean P2/P1 ratio and relative VO2 peak were correlated using the Spearman test. ResultsVolunteers presented 1.05 ± 0.2 of P2/P1 ratio and VO2 peak of 47.5 ± 7.6 mL/kg/min. The Spearman test indicated a negative and low correlation between the P2/P1 ratio and VO2 peak (ρ = -0.388; p = 0.023). ConclusionThe study suggests that the better the cardiorespiratory fitness, the better ICC in a weightlessness simulation.

Advanced neuromonitoring powered by ICM+ and its place in the Brand New AI World, reflections at the 20th anniversary boundary

IntroductionAdoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI). Research questionWe sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models. Material and methodsWe reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing. ResultsICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required. ConclusionsModernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side.