Multimodal monitoring and neurocritical care bioinformatics

Injectable 2D Material-Based Sensor Array for Minimally Invasive Neural Implants

Intracranial implants for diagnosis and treatment of brain diseases have been developed over the past few decades. However, the platform of conventional implantable devices still relies on invasive probes and bulky sensors in conjunction with large-area craniotomy and provides only limited biometric information. Here, an implantable multi-modal sensor array that can be injected through a small hole in the skull and inherently spread out for conformal contact with the cortical surface is reported. The injectable sensor array, composed of graphene multi-channel electrodes for neural recording and electrical stimulation and MoS2-based sensors for monitoring intracranial temperature and pressure, is designed based on a mesh structure whose elastic restoring force enables the contracted device to spread out. It is demonstrated that the sensor array injected into a rabbit's head can detect epileptic discharges on the surface of the cortex and mitigate it by electrical stimulation while monitoring both intracranial temperature and pressure. This method provides good potential for implanting a variety of functional devices via minimally invasive surgery.

Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury

BACKGROUND

Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients.

METHODS

We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting.

RESULTS

The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results.

CONCLUSIONS

The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

The State of the Field of Pediatric Multimodality Neuromonitoring

BACKGROUND

The use of multimodal neuromonitoring in pediatrics is in its infancy relative to adult neurocritical care. Multimodal neuromonitoring encompasses the amalgamation of information from multiple individual neuromonitoring devices to gain a more comprehensive understanding of the condition of the brain. It allows for adaptation to the changing state of the brain throughout various stages of injury with potential to individualize and optimize therapies.

METHODS

Here we provide an overview of multimodal neuromonitoring in pediatric neurocritical care and its potential application in the future.

RESULTS

Multimodal neuromonitoring devices are key to the process of multimodal neuromonitoring, allowing for visualization of data trends over time and ideally improving the ability of clinicians to identify patterns and find meaning in the immense volume of data now encountered in the care of critically ill patients at the bedside. Clinical use in pediatrics requires more study to determine best practices and impact on patient outcomes. Potential uses include guidance for targets of physiological parameters in the setting of acute brain injury, neuroprotection for patients at high risk for brain injury, and neuroprognostication. Implementing multimodal neuromonitoring in pediatric patients involves interprofessional collaboration with the development of a simultaneous comprehensive program to support the use of multimodal neuromonitoring while maintaining the fundamental principles of the delivery of neurocritical care at the bedside.

CONCLUSIONS

The possible benefits of multimodal neuromonitoring are immense and have great potential to advance the field of pediatric neurocritical care and the health of critically ill children.

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

BACKGROUND

Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.

METHODS

aSAH patients from 2 prospective cohorts (Zurich-derivation cohort, Aachen-validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1-4 vs. 5-8) or ordinal outcome (GOSE-extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.

RESULTS

A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.

CONCLUSIONS

MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Milestones in the history of neurocritical care

Over the last century, significant milestones have been achieved in managing critical illness and diagnosing and treating neurological diseases. Building upon these milestones, the field of neurocritical care emerged in the 1980 and 1990 s at the convergence of critical care medicine and acute neurological treatment. This comprehensive review presents a historical account of key developments in neurocritical care in both the United States and Europe, with a special emphasis on German contributions. The scope of the review encompasses: the foundations of neurocritical care, including post-operative units in the 1920s and 30s, respiratory support during the poliomyelitis epidemics in the 40 and 50 s, cardiac and hemodynamic care in the 60 and 70 s, and stroke units in the 80 and 90 s; key innovations including cerebral angiography, computed tomography, and intracranial pressure and multi-modal monitoring; and advances in stroke, traumatic brain injury, cardiac arrest, neuromuscular disorders, meningitis and encephalitis. These advances have revolutionized the management of neurological emergencies, emphasizing interdisciplinary teamwork, evidence-based protocols, and personalized approaches to care.

Altered levels of transthyretin in human cerebral microdialysate after subarachnoid haemorrhage using proteomics; a descriptive pilot study

BACKGROUND

Subarachnoid haemorrhage (SAH) is one of the most severe forms of stroke in which delayed cerebral ischemia is one of the major complications. Neurointensive care aims at preventing and treating such complications and identification of biomarkers of early signs of ischemia might therefore be helpful.

METHODS

We aimed at describing proteome profile in cerebral microdialysate in four patients with aneurysmal SAH using two dimensional gel electrophoresis in combination with mass spectrometry in search for new biomarkers for delayed cerebral ischemia and to investigate if there were temporal fluctuations in those biomarkers over time after aneurysmal bleed.

RESULTS

The results showed transthyretin in nine different proteoforms (1001, 1102, 2101, 3101, 4101, 4102, 5001, 5101, 6101) in cerebral microdialysate samples from four patients having sustained SAH. Several proteoforms show highly differing levels and pooled analysis of all samples showed varying optical density related to time from aneurysmal bleed, indicating a temporal evolution.

CONCLUSIONS

Transthyretin proteoforms have not earlier been shown in cerebral microdialysate after SAH and we describe differing levels based on proteoform as well as time from subarachnoid bleed. Transthyretin is well known to be synthetized in choroid plexus, whilst intraparenchymal synthesis remains controversial. The results need to be confirmed in larger studies in order to further describe transthyretin.

Multimodal and autoregulation monitoring in the neurointensive care unit

Given the complexity of cerebral pathology in patients with acute brain injury, various neuromonitoring strategies have been developed to better appreciate physiologic relationships and potentially harmful derangements. There is ample evidence that bundling several neuromonitoring devices, termed "multimodal monitoring," is more beneficial compared to monitoring individual parameters as each may capture different and complementary aspects of cerebral physiology to provide a comprehensive picture that can help guide management. Furthermore, each modality has specific strengths and limitations that depend largely on spatiotemporal characteristics and complexity of the signal acquired. In this review we focus on the common clinical neuromonitoring techniques including intracranial pressure, brain tissue oxygenation, transcranial doppler and near-infrared spectroscopy with a focus on how each modality can also provide useful information about cerebral autoregulation capacity. Finally, we discuss the current evidence in using these modalities to support clinical decision making as well as potential insights into the future of advanced cerebral homeostatic assessments including neurovascular coupling.

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.

Cerebral multimodality monitoring in adult neurocritical care patients with acute brain injury: A narrative review

Cerebral multimodality monitoring (MMM) is, even with a general lack of Class I evidence, increasingly recognized as a tool to support clinical decision-making in the neuroscience intensive care unit (NICU). However, literature and guidelines have focused on unimodal signals in a specific form of acute brain injury. Integrating unimodal signals in multiple signal monitoring is the next step for clinical studies and patient care. As such, we aimed to investigate the recent application of MMM in studies of adult patients with traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), acute ischemic stroke (AIS), and hypoxic ischemic brain injury following cardiac arrest (HIBI). We identified continuous or daily updated monitoring modalities and summarized the monitoring setting, study setting, and clinical characteristics. In addition, we discussed clinical outcome in intervention studies. We identified 112 MMM studies, including 11 modalities, over the last 7 years (2015-2022). Fifty-eight studies (52%) applied only two modalities. Most frequently combined were ICP monitoring (92 studies (82%)) together with PbtO₂ (63 studies (56%). Most studies included patients with TBI (59 studies) or SAH (53 studies). The enrollment period of 34 studies (30%) took more than 5 years, whereas the median sample size was only 36 patients (q1- q3, 20-74). We classified studies as either observational (68 studies) or interventional (44 studies). The interventions were subclassified as systemic (24 studies), cerebral (10 studies), and interventions guided by MMM (11 studies). We identified 20 different systemic or cerebral interventions. Nine (9/11, 82%) of the MMM-guided studies included clinical outcome as an endpoint. In 78% (7/9) of these MMM-guided intervention studies, a significant improvement in outcome was demonstrated in favor of interventions guided by MMM. Clinical outcome may be improved with interventions guided by MMM. This strengthens the belief in this application, but further interdisciplinary collaborations are needed to overcome the heterogeneity, as illustrated in the present review. Future research should focus on increasing sample sizes, improved data collection, refining definitions of secondary injuries, and standardized interventions. Only then can we proceed with complex outcome studies with MMM-guided treatment.

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.

The ICEBERG: a score and visual representation to track the severity of traumatic brain injury: design principles and preliminary results

BACKGROUND

Establishing neurological prognoses in traumatic brain injury (TBI) patients remains challenging. To help physicians in the early management of severe TBI, we have designed a visual score (ICEBERG score) including multimodal monitoring and treatment-related criteria. We evaluated the ICEBERG scores among patients with severe TBI to predict the 28-day mortality and long-term disability (Extended Glasgow Outcome Score (GOSE) at 3 years). Additionally, we made a preliminary assessment of the nurses and doctors on the uptake and reception to the use of the ICEBERG visual tool.

METHODS

This study was part of a larger prospective cohort study of 207 patients with severe TBI in the Parisian region (PariS-TBI study). The ICEBERG score included 6 variables from multimodal monitoring and treatment-related criteria: cerebral perfusion pressure (CPP), intracranial pressure (ICP), body temperature, sedation depth, arterial partial pressure of CO2 and blood osmolarity. The primary outcome measures included the ICEBERG score and its relationship with hospital mortality and GOSE.

RESULTS

The hospital mortality was 21% (45/207). The ICEBERG score baseline value and changes during the 72nd first hours were more strongly associated with TBI prognosis than the ICEBERG parameters measured individually. Interestingly, when the clinical and CT parameters at admission were combined with the ICEBERG score at 48 h using a multimodal approach, the predictive value was significantly increased (AUC = 0.92). Furthermore, comparing the ICEBERG visual representation with the traditional numerical readout revealed that changes in patient vitals were more promptly detected using ICEBERG representation (p < 0.05).

CONCLUSIONS

The ICEBERG score could represent a simple and effective method to describe severity in TBI patients, where a high score is associated with increased mortality and disability. Additionally, ICEBERG representation could enhances the recognition of unmet therapeutic goals and dynamic evolution of the patient's condition. These preliminary results must be confirmed in a prospective manner.

STUDY TYPE

Prognostic.

LEVEL OF EVIDENCE

Level III.

Neurocritical care management of poor-grade subarachnoid hemorrhage: Unjustified nihilism to reasonable optimism

BACKGROUND AND PURPOSE

Historically, overall outcomes for patients with high-grade subarachnoid hemorrhage (SAH) have been poor. Generally, between physicians, either reluctance to treat, or selectivity in treating such patients has been the paradigm. Recent studies have shown that early and aggressive care leads to significant improvement in survival rates and favorable outcomes of grade V SAH patients. With advancements in both neurocritical care and end-of-life care, non-treatment or selective treatment of grade V SAH patients is rarely justified. Current paradigm shifts towards early and aggressive care in such cases may lead to improved outcomes for many more patients.

MATERIALS AND METHODS

We performed a detailed review of the current literature regarding neurointensive management strategies in high-grade SAH, discussing multiple aspects. We discussed the neurointensive care management protocols for grade V SAH patients.

RESULTS

Acutely, intracranial pressure control is of utmost importance with external ventricular drain placement, sedation, optimization of cerebral perfusion pressure, osmotherapy and hyperventilation, as well as cardiopulmonary support through management of hypotension and hypertension.

CONCLUSIONS

Advancements of care in SAH patients make it unethical to deny treatment to poor Hunt and Hess grade patients. Early and aggressive treatment results in a significant improvement in survival rate and favorable outcome in such patients.

Subspace Clustering of Physiological Data From Acute Traumatic Brain Injury Patients: Retrospective Analysis Based on the PROTECT III Trial

BACKGROUND

With advances in digital health technologies and proliferation of biomedical data in recent years, applications of machine learning in health care and medicine have gained considerable attention. While inpatient settings are equipped to generate rich clinical data from patients, there is a dearth of actionable information that can be used for pursuing secondary research for specific clinical conditions.

OBJECTIVE

This study focused on applying unsupervised machine learning techniques for traumatic brain injury (TBI), which is the leading cause of death and disability among children and adults aged less than 44 years. Specifically, we present a case study to demonstrate the feasibility and applicability of subspace clustering techniques for extracting patterns from data collected from TBI patients.

METHODS

Data for this study were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment-Phase III (PROTECT III) trial, which included a cohort of 882 TBI patients. We applied subspace-clustering methods (density-based, cell-based, and clustering-oriented methods) to this data set and compared the performance of the different clustering methods.

RESULTS

The analyses showed the following three clusters of laboratory physiological data: (1) international normalized ratio (INR), (2) INR, chloride, and creatinine, and (3) hemoglobin and hematocrit. While all subclustering algorithms had a reasonable accuracy in classifying patients by mortality status, the density-based algorithm had a higher F1 score and coverage.

CONCLUSIONS

Clustering approaches serve as an important step for phenotype definition and validation in clinical domains such as TBI, where patient and injury heterogeneity are among the major reasons for failure of clinical trials. The results from this study provide a foundation to develop scalable clustering algorithms for further research and validation.

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby’s overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby’s stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant’s HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant’s clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients’ health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

Multimodal neurocritical monitoring

Neurocritical monitoring is important in caring for patients in the neurological intensive care unit. Although clinical neurologic examination is standard for neurocritical monitoring, changes found during the examination are often late signs and insufficient to detect and prevent secondary brain injury. Therefore, various neuromonitoring tools have been developed to monitor different physiologic parameters, such as cerebral oxygenation, cerebral blood flow, cerebral pressure, cerebral autoregulation, cerebral electric activity, and cerebral metabolism. In this review, we have discussed current commonly used neurocritical monitoring tools. No single monitor is sufficient and perfect for neurocritical monitoring. Multimodal neurocritical monitoring is the current trend. However, the lack of common formatting standards and uncertainty of improvement in patients' outcomes warrant further studies of multimodal neurocritical monitoring. Nevertheless, multimodal neurocritical monitoring considers individual pathophysiological variations in patients or their injuries and allows clinicians to tailor individualized management decisions.

Cerebral Vascular Changes During Acute Intracranial Pressure Drop

OBJECTIVE

This study applied a new external ventricular catheter, which allows intracranial pressure (ICP) monitoring and cerebral spinal fluid (CSF) drainage simultaneously, to study cerebral vascular responses during acute CSF drainage.

METHODS

Six patients with 34 external ventricular drain (EVD) opening sessions were retrospectively analyzed. A published algorithm was used to extract morphological features of ICP recordings, and a template-matching algorithm was applied to calculate the likelihood of cerebral vasodilation index (VDI) and cerebral vasoconstriction index (VCI) based on the changes of ICP waveforms during CSF drainage. Power change (∆P) of ICP B-waves after EVD opening was also calculated. Cerebral autoregulation (CA) was assessed through phase difference between arterial blood pressure (ABP) and ICP using a previously published wavelet-based algorithm.

RESULTS

The result showed that acute CSF drainage reduced mean ICP (P = 0.016) increased VCI (P = 0.02) and reduced ICP B-wave power (P = 0.016) significantly. VCI reacted to ICP changes negatively when ICP was between 10 and 25 mmHg, and VCI remained unchanged when ICP was outside the 10-25 mmHg range. VCI negatively (r = - 0.44) and VDI positively (r = 0.82) correlated with ∆P of ICP B-waves, indicating that stronger vasoconstriction resulted in bigger power drop in ICP B-waves. Better CA prior to EVD opening triggered bigger drop in the power of ICP B-waves (r = - 0.612).

CONCLUSIONS

This study demonstrates that acute CSF drainage reduces mean ICP, and results in vasoconstriction which can be detected through an index, VCI. Cerebral vessels actively respond to ICP changes or cerebral perfusion pressure (CPP) changes in a certain range; beyond which, the vessels are insensitive to the changes in ICP and CPP.

Current Status and Recommendations in Multimodal Neuromonitoring

Every patient in neurocritical care evolves through two phases. Acute pathologies are addressed first. These include trauma, hemorrhagic or ischemic stroke, or neuroinfection. Soon after, the concentration shifts to identifying secondary pathologies like fever, seizures, and ischemia, which may exacerbate the brain injury. Frequent bedside examinations are not sufficient for timely detection and prevention of secondary brain injury (SBI) as per the International Multidisciplinary Consensus Conference on Multimodality Monitoring in Neurocritical Care. Multimodality monitoring (MMM) can help in tailoring treatment decisions to prevent such a brain injury. Multimodal neuromonitoring involves data-guided therapeutic interventions by employing various tools and data integration to understand brain physiology. Monitors provide real-time information on cerebral hemodynamics, oxygenation, metabolism, and electrophysiology. The monitors may be invasive/noninvasive and global/regional. We have reviewed such technologies in this write-up. Novel themes like bioinformatics, clinical research, and device development will also be discussed.

Big data in status epilepticus

Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Implementation of a Data Acquisition and Integration Device in the Neurologic Intensive Care Unit

The neurologic intensive care unit has evolved into a data-rich, complex arena. Various neurologic monitors, collectively referred to as multimodality monitoring, provide clinicians with a plethora of real-time information about a comatose patient's condition. The time and cognitive burden required to synthesize the available data and reach meaningful clinical conclusions can be overwhelming. The Moberg Component Neuromonitoring System (Moberg Research, Inc) is a data acquisition and integration device that collects data from multiple monitors, displaying them on a single screen in a way that highlights physiological trends throughout a patient's clinical course. Implementation of the Moberg Component Neuromonitoring System in the neurologic intensive care unit can improve understanding of a patient's neurophysiology, enhance clinical decision-making, and improve quality of care. Use of a staged process of implementation including exploration, installation, initial implementation, and full implementation can bring technology to the bedside in a sustainable fashion.

Multimodality Monitoring in the Neurocritical Care Unit

PURPOSE OF REVIEW

This article focuses on the multiple neuromonitoring devices that can be used to collect bedside data in the neurocritical care unit and the methodology to integrate them into a multimodality monitoring system. The article describes how to apply the collected data to appreciate the physiologic changes and develop therapeutic approaches to prevent secondary injury.

RECENT FINDINGS

The neurologic examination has served as the primary monitor for secondary brain injury in patients admitted to the neurocritical care unit. However, the International Multidisciplinary Consensus Conference on Multimodality Monitoring in Neurocritical Care concluded that frequent bedside examinations are not sufficient to detect and prevent secondary brain injury and that integration of multimodality monitoring with advanced informatics tools will most likely enhance our assessments compared to the clinical examinations alone. This article reviews the invasive and noninvasive technologies used to monitor focal and global neurophysiologic cerebral alterations.

SUMMARY

Multimodal monitoring is still in the early stages of development. Research is still needed to establish more advanced monitors with the bioinformatics to identify useful trends from data gathered to predict clinical outcome or prevent secondary brain injury.

Clinical trials for pediatric traumatic brain injury: definition of insanity?

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality in children both in the United States and throughout the world. Despite valiant efforts and multiple clinical trials completed over the last few decades, there are no high-level recommendations for pediatric TBI available in current guidelines. In this review, the authors explore key findings from the major pediatric clinical trials in children with TBI that have shaped present-day recommendations and the insights gained from them. The authors also offer a perspective on potential efforts to improve the efficacy of future clinical trials in children following TBI.

Data Dissemination: Shortening the Long Tail of Traumatic Brain Injury Dark Data

Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multi-species data across diverse data types including imaging and molecular biomarkers, histopathology, behavior, and functional outcomes. In this review we argue that TBI translation should be acknowledged for what it is: a problem of big data that can be addressed using modern data science approaches. We review the history of the term big data, tracing its origins in Internet technology as data that are "big" according to the "4Vs" of volume, velocity, variety, veracity and discuss how the term has transitioned into the mainstream of biomedical research. We argue that the problem of TBI translation fundamentally centers around data variety and that solutions to this problem can be found in modern machine learning and other cutting-edge analytical approaches. Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature). We review a few early examples of published articles in both the pre-clinical and clinical TBI research literature to demonstrate how data reuse can drive new discoveries leading into translational therapies. Making TBI data resources more Findable, Accessible, Interoperable, and Reusable (FAIR) through better data stewardship has great potential to accelerate discovery and translation for the silent epidemic of TBI.

Unraveling the complexities of invasive multimodality neuromonitoring

Acute brain injuries are a major cause of death and disability worldwide. Survivors of life-threatening brain injury often face a lifetime of dependent care, and novel approaches that improve outcome are sorely needed. A delayed cascade of brain damage, termed secondary injury, occurs hours to days and even weeks after the initial insult. This delayed phase of injury provides a crucial window for therapeutic interventions that could limit brain damage and improve outcome. A major barrier in the ability to prevent and treat secondary injury is that physicians are often unable to target therapies to patients' unique cerebral physiological disruptions. Invasive neuromonitoring with multiple complementary physiological monitors can provide useful information to enable this tailored, precision approach to care. However, integrating the multiple streams of time-varying data is challenging and often not possible during routine bedside assessment. The authors review and discuss the principles and evidence underlying several widely used invasive neuromonitors. They also provide a framework for integrating data for clinical decision making and discuss future developments in informatics that may allow new treatment paradigms to be developed.

Multimodality Neuromonitoring in Adult Traumatic Brain Injury: A Narrative Review

Neuromonitoring plays an important role in the management of traumatic brain injury. Simultaneous assessment of cerebral hemodynamics, oxygenation, and metabolism allows an individualized approach to patient management in which therapeutic interventions intended to prevent or minimize secondary brain injury are guided by monitored changes in physiologic variables rather than generic thresholds. This narrative review describes various neuromonitoring techniques that can be used to guide the management of patients with traumatic brain injury and examines the latest evidence and expert consensus guidelines for neuromonitoring.

Secondary brain injury: Predicting and preventing insults

Mortality or severe disability affects the majority of patients after severe traumatic brain injury (TBI). Adherence to the brain trauma foundation guidelines has overall improved outcomes; however, traditional as well as novel interventions towards intracranial hypertension and secondary brain injury have come under scrutiny after series of negative randomized controlled trials. In fact, it would not be unfair to say there has been no single major breakthrough in the management of severe TBI in the last two decades. One plausible hypothesis for the aforementioned failures is that by the time treatment is initiated for neuroprotection, or physiologic optimization, irreversible brain injury has already set in. We, and others, have recently developed predictive models based on machine learning from continuous time series of intracranial pressure and partial brain tissue oxygenation. These models provide accurate predictions of physiologic crises events in a timely fashion, offering the opportunity for an earlier application of targeted interventions. In this article, we review the rationale for prediction, discuss available predictive models with examples, and offer suggestions for their future prospective testing in conjunction with preventive clinical algorithms. This article is part of the Special Issue entitled "Novel Treatments for Traumatic Brain Injury".

Impact of extended monitoring-guided intensive care on outcome after severe traumatic brain injury: A prospective multicentre cohort study (PariS-TBI study)

OBJECTIVE

We evaluated whether an integrated monitoring with systemic and specific monitoring affect mortality and disability in adults with severe traumatic brain injury (sTBI).

METHODS

Adults with severeTBI (Glasgow Coma Scale [GCS] ≤ 8) admitted alive in intensive care units (ICUs) were prospectively included. Primary endpoints were in-hospital 30-day mortality and extended Glasgow outcome score (GOSE) at 3 years. Association with the intensity of monitoring and outcome was studied by comparing a high level of monitoring (HLM) (systemic and ≥3 specific monitoring) and low level of monitoring (LLM) (systemic and 0-2 specific monitoring) and using inverse probability weighting procedure.

RESULTS

476 patients were included and IPW was used to improve the balance between the two groups of treatments (HLM/LMM). Overall hospital mortality (at 30 days) was 43%, being significantly lower in HLM than LLM group (27% vs. 53%: RR, 1.63: 95% CI: 1.23-2.15). The 14-day hospital mortality was also lower in the HLM group than expected, based upon the CRASH prediction model (35%). At 3 years, disability was not significantly different between the monitoring groups.

CONCLUSIONS

After adjustment, HLM group improved short-term mortality but did not show any improvement in the 3-year outcome compared with LLM.

Antioxidant therapies in traumatic brain injury: a review

Oxidative stress constitute one of the commonest mechanism of the secondary injury contributing to neuronal death in traumatic brain injury cases. The oxidative stress induced secondary injury blockade may be considered as to be a good alternative to improve the outcome of traumatic brain injury (TBI) treatment. Due to absence of definitive therapy of traumatic brain injury has forced researcher to utilize unconventional therapies and its roles investigated in the improvement of management and outcome in recent year. Antioxidant therapies are proven effective in many preclinical studies and encouraging results and the role of antioxidant mediaction may act as further advancement in the traumatic brain injury management it may represent aonr of newer moadlaity in neurosurgical aramamentorium, this kind of therapy could be a good alternative or adjuct to the previously established neuroprotection agents in TBI.

"Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology"

Background

Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks.

Methods

Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues.

Results

Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived.

Conclusions

Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians’ decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0520-7) contains supplementary material, which is available to authorized users.

Big Data in traumatic brain injury; promise and challenges

Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.

Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients With Severe Traumatic Brain Injury

OBJECTIVES

To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury.

DESIGN

A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future.

SETTING

The neurosurgical unit of Ben Taub Hospital (Houston, TX).

SUBJECTS

Our cohort consisted of 817 subjects with severe traumatic brain injury.

MEASUREMENTS AND MAIN RESULTS

Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91.

CONCLUSIONS

Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

[Specialized neurological neurosurgical intensive care medicine]

In Germany dedicated neurological-neurosurgical critical care (NCC) is the fastest growing specialty and one of the five big disciplines integrated within the German critical care society (Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin; DIVI). High-quality investigations based on resilient evidence have underlined the need for technical advances, timely optimization of therapeutic procedures, and multidisciplinary team-work to treat those critically ill patients. This evolution has repeatedly raised questions, whether NCC-units should be run independently or better be incorporated within multidisciplinary critical care units, whether treatment variations exist that impact clinical outcome, and whether nowadays NCC-units can operate cost-efficiently? Stroke is the most frequent disease entity treated on NCC-units, one of the most common causes of death in Germany leading to a great socio-economic burden due to long-term disabled patients. The main aim of NCC employs surveillance of structural and functional integrity of the central nervous system as well as the avoidance of secondary brain damage. However, clinical evaluation of these severely injured commonly sedated and mechanically ventilated patients is challenging and highlights the importance of neuromonitoring to detect secondary damaging mechanisms. This multimodal strategy not only requires medical expertise but also enforces the need for specialized teams consisting of qualified nurses, technical assistants and medical therapists. The present article reviews most recent data and tries to answer the aforementioned questions.

Guiding Principles for a Pediatric Neurology ICU (neuroPICU) Bedside Multimodal Monitor: Findings from an International Working Group

BACKGROUND

Physicians caring for children with serious acute neurologic disease must process overwhelming amounts of physiological and medical information. Strategies to optimize real time display of this information are understudied.

OBJECTIVES

Our goal was to engage clinical and engineering experts to develop guiding principles for creating a pediatric neurology intensive care unit (neuroPICU) monitor that integrates and displays data from multiple sources in an intuitive and informative manner.

METHODS

To accomplish this goal, an international group of physicians and engineers communicated regularly for one year. We integrated findings from clinical observations, interviews, a survey, signal processing, and visualization exercises to develop a concept for a neuroPICU display.

RESULTS

Key conclusions from our efforts include: (1) A neuroPICU display should support (a) rapid review of retrospective time series (i.e. cardiac, pulmonary, and neurologic physiology data), (b) rapidly modifiable formats for viewing that data according to the specialty of the reviewer, and (c) communication of the degree of risk of clinical decline. (2) Specialized visualizations of physiologic parameters can highlight abnormalities in multivariable temporal data. Examples include 3-D stacked spider plots and color coded time series plots. (3) Visual summaries of EEG with spectral tools (i.e. hemispheric asymmetry and median power) can highlight seizures via patient-specific "fingerprints." (4) Intuitive displays should emphasize subsets of physiology and processed EEG data to provide a rapid gestalt of the current status and medical stability of a patient.

CONCLUSIONS

A well-designed neuroPICU display must present multiple datasets in dynamic, flexible, and informative views to accommodate clinicians from multiple disciplines in a variety of clinical scenarios.

Data collection and interpretation

Patient monitoring is routinely performed in all patients who receive neurocritical care. The combined use of monitors, including the neurologic examination, laboratory analysis, imaging studies, and physiological parameters, is common in a platform called multi-modality monitoring (MMM). However, the full potential of MMM is only beginning to be realized since for the most part, decision making historically has focused on individual aspects of physiology in a largely threshold-based manner. The use of MMM now is being facilitated by the evolution of bio-informatics in critical care including developing techniques to acquire, store, retrieve, and display integrated data and new analytic techniques for optimal clinical decision making. In this review, we will discuss the crucial initial steps toward data and information management, which in this emerging era of data-intensive science is already shifting concepts of care for acute brain injury and has the potential to both reshape how we do research and enhance cost-effective clinical care.

Alternative clinical trial design in neurocritical care

Neurocritical care involves the care of highly complex patients with combinations of physiologic derangements in the brain and in extracranial organs. The level of evidence underpinning treatment recommendations remains low due to a multitude of reasons including an incomplete understanding of the involved physiology; lack of good quality, prospective, standardized data; and the limited success of conventional randomized controlled trials. Comparative effectiveness research can provide alternative perspectives and methods to enhance knowledge and evidence within the field of neurocritical care; these include large international collaborations for generation and maintenance of high quality data, statistical methods that incorporate heterogeneity and individualize outcome prediction, and finally advanced bioinformatics that integrate large amounts of variable-source data into patient-specific phenotypes and trajectories.

The International Multi-disciplinary Consensus Conference on Multimodality Monitoring: future directions and emerging technologies

Neuromonitoring has evolved rapidly in recent years and there now are many new monitors that have revealed a great deal about the ongoing pathophysiology of brain injury and coma. Further evolution will include the consolidation of multi-modality monitoring (MMM), the development of next-generation informatics tools to identify complex physiologic events and decision support tools to permit targeted individualized care. In this review, we examine future directions and emerging technologies in neuromonitoring including: (1) device development, (2) what is the current limitation(s) of MMM in its present format(s), (3) what would improve the ability of MMM to enhance neurocritical care, and (4) how do we develop evidence for use of MMM?

Does heart rate variability reflect the systemic inflammatory response in a fetal sheep model of lipopolysaccharide-induced sepsis?

Fetal inflammatory response occurs during chorioamnionitis, a frequent and often subclinical inflammation associated with increased risk for brain injury and life-lasting neurologic deficits. No means of early detection exist. We hypothesized that systemic fetal inflammation without septic shock will be reflected in alterations of fetal heart rate (FHR) variability (fHRV) distinguishing baseline versus inflammatory response states. In chronically instrumented near-term fetal sheep (n = 24), we induced an inflammatory response with lipopolysaccharide (LPS) injected intravenously (n = 14). Ten additional fetuses served as controls. We measured fetal plasma inflammatory cytokine IL-6 at baseline, 1, 3, 6, 24 and 48 h. 44 fHRV measures were determined continuously every 5 min using continuous individualized multi-organ variability analysis (CIMVA). CIMVA creates an fHRV measures matrix across five signal-analytical domains, thus describing complementary properties of fHRV. Using principal component analysis (PCA), a widely used technique for dimensionality reduction, we derived and quantitatively compared the CIMVA fHRV PCA signatures of inflammatory response in LPS and control groups. In the LPS group, IL-6 peaked at 3 h. In parallel, PCA-derived fHRV composite measures revealed a significant difference between LPS and control group at different time points. For the LPS group, a sharp increase compared to baseline levels was observed between 3 h and 6 h, and then abating to baseline levels, thus tracking closely the IL-6 inflammatory profile. This pattern was not observed in the control group. We also show that a preselection of fHRV measures prior to the PCA can potentially increase the difference between LPS and control groups, as early as 1 h post LPS injection. We propose a fHRV composite measure that correlates well with levels of inflammation and tracks well its temporal profile. Our results highlight the potential role of HRV to study and monitor the inflammatory response non-invasively over time.

Big Data Analytics in Healthcare

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

Brain tissue oxygenation, lactate-pyruvate ratio, and cerebrovascular pressure reactivity monitoring in severe traumatic brain injury: systematic review and viewpoint

BACKGROUND

Prevention and detection of secondary brain insults via multimodality neuromonitoring is a major goal in patients with severe traumatic brain injury (TBI).

OBJECTIVE

Explore the underlying pathophysiology and clinical outcome correlates as it pertains to combined monitoring of ≥2 from the following variables: partial brain tissue oxygen tension (PbtO(2)), pressure reactivity index (PRx), and lactate pyruvate ratio (LPR).

METHODS

Data sources included Medline, EMBASE, and evidence-based databases (Cochrane DSR, ACP Journal Club, DARE, and the Cochrane Controlled Trials Register). The PRISMA recommendations were followed. Two authors independently selected articles meeting inclusion criteria. Studies enrolled adults who required critical care and monitoring in the setting of TBI. Included studies reported on correlations between the monitored variables and/or reported on correlations of the variables with clinical outcomes.

RESULTS

Thirty-four reports were included (32 observational studies and 2 randomized controlled trials) with a mean sample size of 34 patients (range 6-223), and a total of 1,161 patient-observations. Overall methodological quality was moderate. Due to inter-study heterogeneity in outcomes of interest, study design, and in both number and type of covariates included in multivariable analyses, quantitative synthesis of study results was not undertaken.

CONCLUSION

Several literature limitations were identified including small number of subjects, lack of clinical outcome correlations, inconsistent probe location, and overall moderate quality among the included studies. These limitations preclude any firm conclusions; nevertheless we suggest that the status of cerebrovascular reactivity is not only important for cerebral perfusion pressure optimization but should also inform interpretation and interventions targeted on PbtO(2) and LPR. Assessment of reactivity can be the first step in approaching the relations among cerebral blood flow, oxygen delivery, demand, and cellular metabolism.

A review of flux considerations for in vivo neurochemical measurements

The mass transport or flux of neurochemicals in the brain and how this flux affects chemical measurements and their interpretation is reviewed. For all endogenous neurochemicals found in the brain, the flux of each of these neurochemicals exists between sources that produce them and the sites that consume them all within μm distances. Principles of convective-diffusion are reviewed with a significant emphasis on the tortuous paths and discrete point sources and sinks. The fundamentals of the primary methods of detection, microelectrodes and microdialysis sampling of brain neurochemicals are included in the review. Special attention is paid to the change in the natural flux of the neurochemicals caused by implantation and consumption at microelectrodes and uptake by microdialysis. The detection of oxygen, nitric oxide, glucose, lactate, and glutamate, and catecholamines by both methods are examined and where possible the two techniques (electrochemical vs. microdialysis) are compared. Non-invasive imaging methods: magnetic resonance, isotopic fluorine MRI, electron paramagnetic resonance, and positron emission tomography are also used for different measurements of the above-mentioned solutes and these are briefly reviewed. Although more sophisticated, the imaging techniques are unable to track neurochemical flux on short time scales, and lack spatial resolution. Where possible, determinations of flux using imaging are compared to the more classical techniques of microdialysis and microelectrodes.

Windowed multitaper correlation analysis of multimodal brain monitoring parameters

Although multimodal monitoring sets the standard in daily practice of neurocritical care, problem-oriented analysis tools to interpret the huge amount of data are lacking. Recently a mathematical model was presented that simulates the cerebral perfusion and oxygen supply in case of a severe head trauma, predicting the appearance of distinct correlations between arterial blood pressure and intracranial pressure. In this study we present a set of mathematical tools that reliably detect the predicted correlations in data recorded at a neurocritical care unit. The time resolved correlations will be identified by a windowing technique combined with Fourier-based coherence calculations. The phasing of the data is detected by means of Hilbert phase difference within the above mentioned windows. A statistical testing method is introduced that allows tuning the parameters of the windowing method in such a way that a predefined accuracy is reached. With this method the data of fifteen patients were examined in which we found the predicted correlation in each patient. Additionally it could be shown that the occurrence of a distinct correlation parameter, called scp, represents a predictive value of high quality for the patients outcome.

Bench-to-Bedside and Bedside Back to the Bench; Seeking a Better Understanding of the Acute Pathophysiological Process in Severe Traumatic Brain Injury

Despite substantial investments, traumatic brain injury (TBI) remains one of the major disorders that lack specific pharmacotherapy. To a substantial degree, this situation is due to lack of understanding of the pathophysiological process of the disease. Experimental TBI research offers controlled, rapid, and cost-effective means to identify the pathophysiology but translating experimental findings into clinical practice can be further improved by using the same or similar outcome measures and clinically relevant time points. The pathophysiology during the acute phase of severe TBI is especially poorly understood. In this Mini review, I discuss some of the incongruences between current clinical practices and needs versus information provided by experimental TBI research as well as the benefits of designing animal experiments with translation into clinical practice in mind.

Multimodal monitoring in the pediatric intensive care unit: new modalities and informatics challenges

We review several newer modalities to monitor the brain in children with acute neurologic disease in the pediatric intensive care unit, such as partial brain tissue oxygen tension (PbtO2), jugular venous oxygen saturation (SjvO2), near infrared spectroscopy (NIRS), thermal diffusion measurement of cerebral blood flow, cerebral microdialysis, and EEG. We then discuss the informatics challenges to acquire, consolidate, analyze, and display the data. Acquisition includes multiple data types: discrete, waveform, and continuous. Consolidation requires device interoperability and time synchronization. Analysis could include pressure reactivity index and quantitative EEG. Displays should communicate the patient's current status, longitudinal and trend information, and critical alarms.

Intracranial pressure after the BEST TRIP trial: a call for more monitoring

PURPOSE OF REVIEW

Increased intracranial pressure (ICP) is associated with worse outcome after traumatic brain injury (TBI), but whether its management improves the outcome is unclear. In this review, we will examine the implications of the Benchmark Evidence from South American Trials: Treatment of Intracranial Pressure (BEST TRIP) trial, evidence for an influence of ICP care on outcome, and a need for greater understanding of the pathophysiology than just ICP through multimodal monitoring (MMM) to enhance the outcome.

RECENT FINDINGS

The primary impact of the BEST TRIP trial, a randomized clinical trial that examined two TBI management strategies, one that used an ICP monitor, is in research and should not alter clinical practice. Analyses of large databases suggest TBI care based on the Brain Trauma Foundation guidelines and management of intracranial hypertension can improve patient outcome. However, accumulating evidence demonstrates there are several mechanisms of secondary brain injury (SBI), for example, microvascular dysfunction or alterations in glucose utilization that cannot be detected using an ICP monitor. In these patients, growing clinical evidence suggests that MMM can help manage SBI and improve TBI outcome.

SUMMARY

ICP-based monitoring and treatment alone may not be enough to enhance TBI outcome, but ICP and cerebral perfusion pressure therapy remain important in TBI care. Although high-quality evidence for MMM is limited, it should be more widely adapted to better understand the complex pathophysiology after TBI, better target care, and identify new therapeutic opportunities.