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

In Situ Polymer-Solution-Processed Graphene-PDMS Nanocomposites for Application in Intracranial Pressure Sensors

Polydimethylsiloxane (PDMS) has emerged as a promising candidate for the dielectric layer in implantable sensors due to its exceptional biocompatibility, stability, and flexibility. This study introduces an innovative approach to produce graphene-reinforced PDMS (Gr-PDMS), where graphite powders are exfoliated into mono- and few-layer graphene sheets within the polymer solution, concurrently forming cross-linkages with PDMS. This method yields a uniformly distributed graphene within the polymer matrix with improved interfaces between graphene and PDMS, significantly reducing the percolation threshold of graphene dispersed in PDMS from 10% to 5%. As-synthesized Gr-PDMS exhibits improved mechanical and electrical properties, tested for potential use in capacitive pressure sensors. The results demonstrate an impressive pressure sensitivity up to 0.0273 kpa-1, 45 times higher than that of pristine PDMS and 2.5 times higher than the reported literature value. The Gr-PDMS showcases excellent pressure sensing ability and stability, fulfilling the requirements for implantable intracranial pressure (ICP) sensors.

Intracranial Pressure Dysfunction Following Severe Intracerebral Hemorrhage in Middle-Aged Rats

Rising intracranial pressure (ICP) aggravates secondary injury and heightens risk of death following intracerebral hemorrhage (ICH). Long-recognized compensatory mechanisms that lower ICP include reduced cerebrospinal fluid and venous blood volumes. Recently, we identified another compensatory mechanism in severe stroke, a decrease in cerebral parenchymal volume via widespread reductions in cell volume and extracellular space (tissue compliance). Here, we examined how age affects tissue compliance and ICP dynamics after severe ICH in rats (collagenase model). A planned comparison to historical young animal data revealed that aged SHAMs (no stroke) had significant cerebral atrophy (9% reduction, p ≤ 0.05), ventricular enlargement (9% increase, p ≤ 0.05), and smaller CA1 neuron volumes (21%, p ≤ 0.05). After ICH in aged animals, contralateral striatal neuron density and CA1 astrocyte density significantly increased (12% for neurons, 7% for astrocytes, p ≤ 0.05 vs. aged SHAMs). Unlike young animals, other regions in aged animals did not display significantly reduced cell soma volume despite a few trends. Nonetheless, overall contralateral hemisphere volume was 10% smaller in aged ICH animals compared to aged SHAMs (p ≤ 0.05). This age-dependent pattern of tissue compliance is not due to absent ICH-associated mass effect (83.2 mm3 avg. bleed volume) as aged ICH animals had significantly elevated mean and peak ICP (p ≤ 0.01), occurrence of ICP spiking events, as well as bilateral evidence of edema (e.g., 3% in injured brain, p ≤ 0.05 vs. aged SHAMs). Therefore, intracranial compliance reserve changes with age; after ICH, these and other age-related changes may cause greater fluctuation from baseline, increasing the chance of adverse outcomes like mortality.

Transcranial, Non-Invasive Evaluation of Potential Misery Perfusion During Hyperventilation Therapy of Traumatic Brain Injury Patients

Hyperventilation (HV) therapy uses vasoconstriction to reduce intracranial pressure (ICP) by reducing cerebral blood volume. However, as HV also lowers cerebral blood flow (CBF), it may provoke misery perfusion (MP), in which the decrease in CBF is coupled with increased oxygen extraction fraction (OEF). MP may rapidly lead to the exhaustion of brain energy metabolites, making the brain vulnerable to ischemia. MP is difficult to detect at the bedside, which is where transcranial hybrid, near-infrared spectroscopies are promising because they non-invasively measure OEF and CBF. We have tested this technology during HV (∼30 min) with bilateral, frontal lobe monitoring to assess MP in 27 sessions in 18 patients with traumatic brain injury. In this study, HV did not lead to MP at a group level (p > 0.05). However, a statistical approach yielded 89 events with a high probability of MP in 19 sessions. We have characterized each statistically significant event in detail and its possible relationship to clinical and radiological status (decompressive craniectomy and presence of a cerebral lesion), without detecting any statistically significant difference (p > 0.05). However, MP detection stresses the need for personalized, real-time assessment in future clinical trials with HV, in order to provide an optimal evaluation of the risk-benefit balance of HV. Our study provides pilot data demonstrating that bedside transcranial hybrid near-infrared spectroscopies could be utilized to assess potential MP.

Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis

Objective. The objective of this study is to develop and validate a method for automatically identifying segments of intracranial pressure (ICP) waveform data from external ventricular drainage (EVD) recordings during intermittent drainage and closure.Methods. The proposed method uses time-frequency analysis through wavelets to distinguish periods of ICP waveform in EVD data. By comparing the frequency compositions of the ICP signals (when the EVD system is clamped) and the artifacts (when the system is open), the algorithm can detect short, uninterrupted segments of ICP waveform from the longer periods of non-measurement data. The method involves applying a wavelet transform, calculating the absolute power in a specific range, using Otsu thresholding to automatically identify a threshold, and performing a morphological operation to remove small segments. Two investigators manually graded the same randomly selected one-hour segments of the resulting processed data. Performance metrics were calculated as a percentage.Results. The study analyzed data from 229 patients who had EVD placed following subarachnoid hemorrhage between June 2006 and December 2012. Of these, 155 (67.7%) were female and 62 (27%) developed delayed cerebral ischemia. A total of 45 150 h of data were segmented. 2044 one-hour segments were randomly selected and evaluated by two investigators (MM and DN). Of those, the evaluators agreed on the classification of 1556 one-hour segments. The algorithm was able to correctly identify 86% (1338 h) of ICP waveform data. 8.2% (128 h) of the time the algorithm either partially or fully failed to segment the ICP waveform. 5.4% (84 h) of data, artifacts were mistakenly identified as ICP waveforms (false positives).Conclusion. The proposed algorithm automates the identification of valid ICP waveform segments of waveform in EVD data and thus enables the inclusion in real-time data analysis for decision support. It also standardizes and makes research data management more efficient.

Beyond intracranial pressure: monitoring cerebral perfusion and autoregulation in severe traumatic brain injury

PURPOSE OF REVIEW

Severe traumatic brain injury (TBI) remains the most prevalent neurological condition worldwide. Observational and interventional studies provide evidence to recommend monitoring of intracranial pressure (ICP) in all severe TBI patients. Existing guidelines focus on treating elevated ICP and optimizing cerebral perfusion pressure (CPP), according to fixed universal thresholds. However, both ICP and CPP, their target thresholds, and their interaction, need to be interpreted in a broader picture of cerebral autoregulation, the natural capacity to adjust cerebrovascular resistance to preserve cerebral blood flow in response to external stimuli.

RECENT FINDINGS

Cerebral autoregulation is often impaired in TBI patients, and monitoring cerebral autoregulation might be useful to develop personalized therapy rather than treatment of one size fits all thresholds and guidelines based on unidimensional static relationships.

SUMMARY

Today, there is no gold standard available to estimate cerebral autoregulation. Cerebral autoregulation can be triggered by performing a mean arterial pressure (MAP) challenge, in which MAP is increased by 10% for 20 min. The response of ICP (increase or decrease) will estimate the status of cerebral autoregulation and can steer therapy mainly concerning optimizing patient-specific CPP. The role of cerebral metabolic changes and its relationship to cerebral autoregulation is still unclear and awaits further investigation.

Spindle wave in intracranial pressure signal analysis for patients with traumatic brain injury: A single-center prospective observational cohort study

Objective: Intracranial pressure (ICP) monitoring is an integral part of the multimodality monitoring system in the neural intensive care unit. The present study aimed to describe the morphology of the spindle wave (a shuttle shape with wide middle and narrow ends) during ICP signal monitoring in TBI patients and to investigate its clinical significance. Methods: Sixty patients who received ICP sensor placement and admitted to the neurosurgical intensive care unit between January 2021 and September 2021 were prospectively enrolled. The patient's Glasgow Coma Scale (GCS) score on admission and at discharge and length of stay in hospital were recorded. ICP monitoring data were monitored continuously. The primary endpoint was 6-month Glasgow Outcome Scale-Extended (GOSE) score. Patients with ICP spindle waves were assigned to the spindle wave group and those without were assigned to the control group. The correlation between the spindle wave and 6-month GOSE was analyzed. Meanwhile, the mean ICP and two ICP waveform-derived indices, ICP pulse amplitude (AMP) and correlation coefficient between AMP and ICP (RAP) were comparatively analyzed. Results: There were no statistically significant differences between groups in terms of age (p = 0.89), gender composition (p = 0.62), and GCS score on admission (p = 0.73). Patients with spindle waves tended to have a higher GCS score at discharge (12.75 vs. 10.90, p = 0.01), a higher increment in GCS score during hospitalization (ΔGCS, the difference between discharge GCS score and admission GCS score) (4.95 vs. 2.80, p = 0.01), and a better 6-month GOSE score (4.90 vs. 3.68, p = 0.04) compared with the control group. And the total duration of the spindle wave was positively correlated with 6-month GOSE (r = 0.62, p = 0.004). Furthermore, the parameters evaluated during spindle waves, including mean ICP, AMP, and RAP, demonstrated significant decreases compared with the parameters before the occurrence of the spindle wave (all p < 0.025). Conclusion: The ICP spindle wave was associated with a better prognosis in TBI patients. Physiological parameters such as ICP, AMP, and RAP were significantly improved when spindle waves occurred, which may explain the enhancement of clinical outcomes. Further studies are needed to investigate the pathophysiological mechanisms behind this wave.

Computational fluid dynamics model to predict the dynamical behavior of the cerebrospinal fluid through implementation of physiological boundary conditions

Cerebrospinal fluid (CSF) dynamics play an important role in maintaining a stable central nervous system environment and are influenced by different physiological processes. Multiple studies have investigated these processes but the impact of each of them on CSF flow is not well understood. A deeper insight into the CSF dynamics and the processes impacting them is crucial to better understand neurological disorders such as hydrocephalus, Chiari malformation, and intracranial hypertension. This study presents a 3D computational fluid dynamics (CFD) model which incorporates physiological processes as boundary conditions. CSF production and pulsatile arterial and venous volume changes are implemented as inlet boundary conditions. At the outlets, 2-element windkessel models are imposed to simulate CSF compliance and absorption. The total compliance is first tuned using a 0D model to obtain physiological pressure pulsations. Then, simulation results are compared with in vivo flow measurements in the spinal subarachnoid space (SAS) and cerebral aqueduct, and intracranial pressure values reported in the literature. Finally, the impact of the distribution of and total compliance on CSF pressures and velocities is evaluated. Without respiration effects, compliance of 0.17 ml/mmHg yielded pressure pulsations with an amplitude of 5 mmHg and an average value within the physiological range of 7-15 mmHg. Also, model flow rates were found to be in good agreement with reported values. However, when adding respiration effects, similar pressure amplitudes required an increase of compliance value to 0.51 ml/mmHg, which is within the range of 0.4-1.2 ml/mmHg measured in vivo. Moreover, altering the distribution of compliance over the four different outlets impacted the local flow, including the flow through the foramen magnum. The contribution of compliance to each outlet was directly proportional to the outflow at that outlet. Meanwhile, the value of total compliance impacted intracranial pressure. In conclusion, a computational model of the CSF has been developed that can simulate CSF pressures and velocities by incorporating boundary conditions based on physiological processes. By tuning these boundary conditions, we were able to obtain CSF pressures and flows within the physiological range.

Probabilistic prediction of increased intracranial pressure in patients with severe traumatic brain injury

Traumatic brain injury (TBI) causes alteration in brain functions. Generally, at intensive care units (ICU), intracranial pressure (ICP) is monitored and treated to avoid increases in ICP with associated poor clinical outcome. The aim was to develop a model which could predict future ICP levels of individual patients in the ICU, to warn treating clinicians before secondary injuries occur. A simple and explainable, probabilistic Markov model was developed for the prediction task ICP ≥ 20 mmHg. Predictions were made for 10-min intervals during 60 min, based on preceding hour of ICP. A prediction enhancement method was developed to compensate for data imbalance. The model was evaluated on 29 patients with severe TBI. With random data selection from all patients (80/20% training/testing) the specificity of the model was high (0.94–0.95) and the sensitivity good to high (0.73–0.87). Performance was similar (0.90–0.95 and 0.73–0.89 respectively) when the leave-one-out cross-validation was applied. The new model could predict increased levels of ICP in a reliable manner and the enhancement method further improved the predictions. Further advantages are the straightforward expandability of the model, enabling inclusion of other time series data and/or static parameters. Next step is evaluation on more patients and inclusion of parameters other than ICP.

Harmonization of Physiological Data in Neurocritical Care: Challenges and a Path Forward

Continuous multimodal monitoring in neurocritical care provides valuable insights into the dynamics of the injured brain. Unfortunately, the "readiness" of this data for robust artificial intelligence (AI) and machine learning (ML) applications is low and presents a significant barrier for advancement. Harmonization standards and tools to implement those standards are key to overcoming existing barriers. Consensus in our professional community is essential for success.

Non-invasive intracranial pressure assessment using shear-wave elastography in neuro-critical care patients

OBJECTIVE

To determine if Young's modulus of the optic nerve (ON) structure as measured by shear-wave elastography can suggest changes in intracranial pressure (ICP) in neuro-critical care patients.

MATERIALS AND METHODS

Thirty-one healthy volunteers and twenty-two neuro-critical care patients were enrolled. ON sheath (ONS) diameter (ONSD) values and Young's modulus measurements of volunteers were collected in a calm state and during a Valsalva maneuver (VM). Ultrasound measurements and ICP values of patients were collected on operation day and at 24 and 48 h after the operation; measurements were thereafter assigned to three groups: severely elevated (ICP greater than 22 mmHg), mildly elevated (ICP = 14-22 mmHg), and normal (ICP ≤ 13 mmHg).

RESULTS

ONSD and Young's modulus for the ON and ONS of volunteers during VM were higher than those in the calm state (all P < 0.001). In contrast to ONSD, Young's modulus for ON and ONS did not correlate with age, body mass index, or sex. The best cutoff values of Young's modulus for ON for predicting elevated and severely elevated ICP were 16.67 kPa and 22.74 kPa, respectively. Accordingly, the sensitivity values were 96.7% and 88.9%, and the specificity values were 86.1% and 73.7%, which had the same diagnostic performance as ONSD.

CONCLUSION

Young's modulus of the ON accurately reflects changes in ICP. It is not confounded by age, sex, or body mass index compared to ONSD.

Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach

BACKGROUND

Machine learning (ML) approaches can significantly improve the classical Rout-based evaluation of the lumbar infusion test (LIT) and the clinical management of the normal pressure hydrocephalus.

OBJECTIVE

To develop a ML model that accurately identifies patients as candidates for permanent cerebral spinal fluid shunt implantation using only intracranial pressure and electrocardiogram signals recorded throughout LIT.

METHODS

This was a single-center cohort study of prospectively collected data of 96 patients who underwent LIT and 5-day external lumbar cerebral spinal fluid drainage (external lumbar drainage) as a reference diagnostic method. A set of selected 48 intracranial pressure/electrocardiogram complex signal waveform features describing nonlinear behavior, wavelet transform spectral signatures, or recurrent map patterns were calculated for each patient. After applying a leave-one-out cross-validation training-testing split of the data set, we trained and evaluated the performance of various state-of-the-art ML algorithms.

RESULTS

The highest performing ML algorithm was the eXtreme Gradient Boosting. This model showed a good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.891 (accuracy: 82.3%, sensitivity: 86.1%, and specificity: 73.9%) obtained for 8 selected features. Our ML model clearly outperforms the classical Rout-based manual classification commonly used in clinical practice with an accuracy of 62.5%.

CONCLUSION

This study successfully used the ML approach to predict the outcome of a 5-day external lumbar drainage and hence which patients are likely to benefit from permanent shunt implantation. Our automated ML model thus enhances the diagnostic utility of LIT in management.

Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients

Structured Abstract—Objective: Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving lives in an intensive care unit (ICU). Despite many applications of machine learning (ML) techniques related to clinical diagnosis, ML applications for continuous ICP detection or short-term predictions have been rarely reported. This study proposes an efficient method of applying an artificial recurrent neural network on the early prediction of ICP evaluation continuously for TBI patients. Methods: After ICP data preprocessing, the learning model is generated for thirteen patients to continuously predict the ICP signal occurrence and classify events for the upcoming 10 minutes by inputting the previous 20-minutes of the ICP signal. Results: As the overall model performance, the average accuracy is 94.62%, the average sensitivity is 74.91%, the average specificity is 94.83%, and the average root mean square error is approximately 2.18 mmHg. Conclusion: This research addresses a significant clinical problem with the management of traumatic brain injury patients. The machine learning model data enables early prediction of ICP continuously in a real-time fashion, which is crucial for appropriate clinical interventions. The results show that our machine learning-based model has high adaptive performance, accuracy, and efficiency.

Intracranial Compliance Assessed by Intracranial Pressure Pulse Waveform

BACKGROUND

Morphological alterations in intracranial pressure (ICP) pulse waveform (ICPW) secondary to intracranial hypertension (ICP >20 mmHg) and a reduction in intracranial compliance (ICC) are well known indicators of neurological severity. The exclusive exploration of modifications in ICPW after either the loss of skull integrity or surgical procedures for intracranial hypertension resolution is not a common approach studied. The present study aimed to assess the morphological alterations in ICPW among neurocritical care patients with skull defects and decompressive craniectomy (DC) by comparing the variations in ICPW features according to elevations in mean ICP values.

METHODS

Patients requiring ICP monitoring because of acute brain injury were included. A continuous record of 10 min-length for the beat-by-beat analysis of ICPW was performed, with ICP elevation produced by means of ultrasound-guided manual internal jugular vein compression at the end of the record. ICPW features (peak amplitude ratio (P2/P1), time interval to pulse peak (TTP) and pulse amplitude) were counterweighed between baseline and compression periods. Results were distributed for three groups: intact skull (exclusive burr hole for ICP monitoring), craniotomy/large fractures (group 2) or DC (group 3).

RESULTS

57 patients were analyzed. A total of 21 (36%) presented no skull defects, 21 (36%) belonged to group 2, whereas 15 (26%) had DC. ICP was not significantly different between groups: ±15.11 for intact, 15.33 for group 2 and ±20.81 mmHg for group 3, with ICP-induced elevation also similar between groups (p = 0.56). Significant elevation was observed for the P2/P1 ratio for groups 1 and 2, whereas a reduction was observed in group 3 (elevation of ±0.09 for groups 1 and 2, but a reduction of 0.03 for group 3, p = 0.01), and no significant results were obtained for TTP and pulse amplitudes.

CONCLUSION

In the present study, intracranial pressure pulse waveform analysis indicated that intracranial compliance was significantly more impaired among decompressive craniectomy patients, although ICPW indicated DC to be protective for further influences of ICP elevations over the brain. The analysis of ICPW seems to be an alternative to real-time ICC assessment.