Impaired Percent Alpha Variability on Continuous Electroencephalography Is Associated with Thalamic Injury and Predicts Poor Long-Term Outcome after Human Traumatic Brain Injury

A nomogram based on quantitative EEG to predict the prognosis of nontraumatic coma patients in the neuro-intensive care unit

OBJECTIVE

We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work.

METHODS

This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model.

RESULTS

After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979).

CONCLUSION

A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge.

CLINICAL IMPLICATION

This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

Resting-State EEG Signature of Early Consciousness Recovery in Comatose Patients with Traumatic Brain Injury

BACKGROUND

Resting-state electroencephalography (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI). We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in patients with TBI.

METHODS

This is a retrospective study of comatose patients with TBI who were admitted to a trauma center (October 2013 to January 2022). Demographics, basic clinical data, imaging characteristics, and EEGs were collected. We calculated the following using 10-min rsEEGs: power spectral density, permutation entropy (complexity measure), weighted symbolic mutual information (wSMI, global information sharing measure), Kolmogorov complexity (Kolcom, complexity measure), and heart-evoked potentials (the averaged EEG signal relative to the corresponding QRS complex on electrocardiography). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, and rsEEG data via a support vector machine.

RESULTS

We studied 113 of 134 (84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50 years, p = 0.01). Patients who recovered also had higher Kolcom (U = 1688, p = 0.01), increased beta power (U = 1,652 p = 0.003) with higher variability across channels (U = 1534, p = 0.034) and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04), and higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = 0.026; U = 1639, p = 0.024) than those who did not recover. The area under the receiver operating characteristic curve for rsEEG was higher than that for clinical data (using age, motor response, pupil reactivity) and higher than that for the Marshall computed tomography classification (0.69 vs. 0.66 vs. 0.56, respectively; p < 0.001).

CONCLUSIONS

We describe the rsEEG signature in recovery of consciousness prior to discharge in comatose patients with TBI. rsEEG measures performed modestly better than the clinical and imaging data in predicting recovery.

Resting-State EEG Signature of Early Consciousness Recovery in Comatose Traumatic Brain Injury Patients

Background Resting-state electroencephalogram (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI) patients. We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in comatose TBI patients. Methods This is a retrospective study of comatose TBI patients who were admitted to a level-1 trauma center (10/2013-1/2022). Demographics, basic clinical data, imaging characteristics, and EEG data were collected. We calculated using 10-minute rsEEGs: power spectral density (PSD), permutation entropy (PE - complexity measure), weighted symbolic-mutual-information (wSMI - global information sharing measure), Kolmogorov complexity (Kolcom - complexity measure), and heart-evoked potentials (HEP - the averaged EEG signal relative to the corresponding QRS complex on electrocardiogram). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, rsEEG data via Support Vector Machine with a linear kernel (SVM). Results We studied 113 (out of 134, 84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50, p .01). Patients who recovered consciousness had higher Kolcom (U = 1688, p = 0.01,), increased beta power (U = 1652 p = 0.003), with higher variability across channels ( U = 1534, p = 0.034), and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04) and showed higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = .026, U = 1639, p = 0.024) than those who didn't recover. The ROC-AUC improved from 0.66 (using age, motor response, pupils' reactivity, and CT Marshall classification) to 0.69 (p < 0.001) when adding rsEEG measures. Conclusion We describe the rsEEG EEG signature in recovery of consciousness prior to discharge in comatose TBI patients. Resting-state EEG measures improved prediction beyond the clinical and imaging data.

Electroencephalography as a Biomarker of Prognosis in Acute Brain Injury

Electroencephalography (EEG) is a noninvasive tool that allows the monitoring of cerebral brain function in critically ill patients, aiding with diagnosis, management, and prognostication. Specific EEG features have shown utility in the prediction of outcomes in critically ill patients with status epilepticus, acute brain injury (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and traumatic brain injury), anoxic brain injury, and toxic-metabolic encephalopathy. Studies have also found an association between particular EEG patterns and long-term functional and cognitive outcomes as well as prediction of recovery of consciousness following acute brain injury. This review summarizes these findings and demonstrates the value of utilizing EEG findings in the determination of prognosis.

Incidence and Predictors of Mortality Among Patients with Traumatic Brain Injury at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia: A Retrospective Follow-Up Study

Background

Traumatic brain injury is a major list of health and socioeconomic problems especially in low- and middle-income countries which influences productive age groups. Differences in patient characteristics, socioeconomic status, intensive care unit admission thresholds, health-care systems, and the availability of varying numbers of intensive care unit (ICU) beds among hospitals had shown to be the causes for the variation on the incidence in mortality following traumatic brain injury across different continents. The aim of this study was to assess the incidence and predictors of mortality among patients with traumatic brain injury at University of Gondar Comprehensive Specialized Hospital.

Methods

A retrospective follow-up study was conducted based on chart review and selected patient charts admitted from January, 2017 to January, 2022. Participants in the study were chosen using a simple random sample procedure that was computer generated. Data was entered with epi-data version 4.6 and analyzed using SPSS version 26. Both bivariate and multivariate logistic regression analyses were used, and in multivariate logistic regression analysis, P-value <0.05 with 95% CI was considered statistically significant.

Results

The magnitude of mortality was 28.8%. Most of the injuries were caused by assault followed by road traffic accident (RTA). About 30% of the subjects presented with severe head injuries and epidural hematoma (EDH) followed by skull fracture were the most common diagnoses on admission. The independent predictors of mortality were male sex (AOR: 6.12, CI: 1.82, 20.5), severe class injury with Glasco coma scale (GCS <9) (AOR: 5.96, CI: 2.07, 17.12), intraoperative hypoxia episode (AOR: 10.5, CI: 2.6-42.1), hyperthermia (AOR: 25, CI: 5.54, 115.16), lack of pre-hospital care (AOR: 2.64 CI: 1.6-4.2), abnormal appearance on both eyes (AOR: 13.4, CI: 5.1-34.6), in-hospital hypoxia episode and having extra-cranial concomitant injury were positively associated with mortality, while on admission, systolic blood pressure (SBP) of 100-149 (AOR: 0.086, CI: 0.016-0.46) was negatively associated with mortality.

Conclusion

The overall mortality rate was considerably high. As a result, traumatic brain injury management should be focused on modifiable factors that increase patient mortality, such as on-admission hypotension, a lack of pre-hospital care, post-operative complications, an intraoperative hypoxia episode, and hyperthermia.

A predictive model for consciousness recovery of comatose patients after acute brain injury

Background

Predicting the consciousness recovery for comatose patients with acute brain injury is an important issue. Although some efforts have been made in the study of prognostic assessment methods, it is still unclear which factors can be used to establish model to directly predict the probability of consciousness recovery.

Objectives

We aimed to establish a model using clinical and neuroelectrophysiological indicators to predict consciousness recovery of comatose patients after acute brain injury.

Methods

The clinical data of patients with acute brain injury admitted to the neurosurgical intensive care unit of Xiangya Hospital of Central South University from May 2019 to May 2022, who underwent electroencephalogram (EEG) and auditory mismatch negativity (MMN) examinations within 28 days after coma onset, were collected. The prognosis was assessed by Glasgow Outcome Scale (GOS) at 3 months after coma onset. The least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select the most relevant predictors. We combined Glasgow coma scale (GCS), EEG, and absolute amplitude of MMN at Fz to develop a predictive model using binary logistic regression and then presented by a nomogram. The predictive efficiency of the model was evaluated with AUC and verified by calibration curve. The decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model.

Results

A total of 116 patients were enrolled for analysis, of which 60 had favorable prognosis (GOS ≥ 3). Five predictors, including GCS (OR = 13.400, P < 0.001), absolute amplitude of MMN at Fz site (FzMMNA, OR = 1.855, P = 0.038), EEG background activity (OR = 4.309, P = 0.023), EEG reactivity (OR = 4.154, P = 0.030), and sleep spindles (OR = 4.316, P = 0.031), were selected in the model by LASSO and binary logistic regression analysis. This model showed favorable predictive power, with an AUC of 0.939 (95% CI: 0.899-0.979), and calibration. The threshold probability of net benefit was between 5% and 92% in the DCA.

Conclusion

This predictive model for consciousness recovery in patients with acute brain injury is based on a nomogram incorporating GCS, EEG background activity, EEG reactivity, sleep spindles, and FzMMNA, which can be conveniently obtained during hospitalization. It provides a basis for care givers to make subsequent medical decisions.

Early EEG monitoring predicts clinical outcome in patients with moderate to severe traumatic brain injury

There is a need for reliable predictors in patients with moderate to severe traumatic brain injury to assist clinical decision making. We assess the ability of early continuous EEG monitoring at the intensive care unit (ICU) in patients with traumatic brain injury (TBI) to predict long term clinical outcome and evaluate its complementary value to current clinical standards. We performed continuous EEG measurements in patients with moderate to severe TBI during the first week of ICU admission. We assessed the Extended Glasgow Outcome Scale (GOSE) at 12 months, dichotomized into poor (GOSE 1-3) and good (GOSE 4-8) outcome. We extracted EEG spectral features, brain symmetry index, coherence, aperiodic exponent of the power spectrum, long range temporal correlations, and broken detailed balance. A random forest classifier using feature selection was trained to predict poor clinical outcome based on EEG features at 12, 24, 48, 72 and 96 h after trauma. We compared our predictor with the IMPACT score, the best available predictor, based on clinical, radiological and laboratory findings. In addition we created a combined model using EEG as well as the clinical, radiological and laboratory findings. We included hundred-seven patients. The best prediction model using EEG parameters was found at 72 h after trauma with an AUC of 0.82 (0.69-0.92), specificity of 0.83 (0.67-0.99) and sensitivity of 0.74 (0.63-0.93). The IMPACT score predicted poor outcome with an AUC of 0.81 (0.62-0.93), sensitivity of 0.86 (0.74-0.96) and specificity of 0.70 (0.43-0.83). A model using EEG and clinical, radiological and laboratory parameters resulted in a better prediction of poor outcome (p < 0.001) with an AUC of 0.89 (0.72-0.99), sensitivity of 0.83 (0.62-0.93) and specificity of 0.85 (0.75-1.00). EEG features have potential use for predicting clinical outcome and decision making in patients with moderate to severe TBI and provide complementary information to current clinical standards.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group's short-term, intermediate, and long-term goals.

The prognostic value of resting-state EEG in acute post-traumatic unresponsive states

Accurate early prognostication is vital for appropriate long-term care decisions after traumatic brain injury. While measures of resting-state EEG oscillations and their network properties, derived from graph theory, have been shown to provide clinically useful information regarding diagnosis and recovery in patients with chronic disorders of consciousness, little is known about the value of these network measures when calculated from a standard clinical low-density EEG in the acute phase post-injury. To investigate this link, we first validated a set of measures of oscillatory network features between high-density and low-density resting-state EEG in healthy individuals, thus ensuring accurate estimation of underlying cortical function in clinical recordings from patients. Next, we investigated the relationship between these features and the clinical picture and outcome of a group of 18 patients in acute post-traumatic unresponsive states who were not following commands 2 days+ after sedation hold. While the complexity of the alpha network, as indexed by the standard deviation of the participation coefficients, was significantly related to the patients' clinical picture at the time of EEG, no network features were significantly related to outcome at 3 or 6 months post-injury. Rather, mean relative alpha power across all electrodes improved the accuracy of outcome prediction at 3 months relative to clinical features alone. These results highlight the link between the alpha rhythm and clinical signs of consciousness and suggest the potential for simple measures of resting-state EEG band power to provide a coarse snapshot of brain health for stratification of patients for rehabilitation, therapy and assessments of both covert and overt cognition.

Multiparametric Monitoring of Hypnosis and Nociception-Antinociception Balance during General Anesthesia-A New Era in Patient Safety Standards and Healthcare Management

The development of general anesthesia techniques and anesthetic substances has opened new horizons for the expansion and improvement of surgical techniques. Nevertheless, more complex surgical procedures have brought a higher complexity and longer duration for general anesthesia, which has led to a series of adverse events such as hemodynamic instability, under- or overdosage of anesthetic drugs, and an increased number of post-anesthetic events. In order to adapt the anesthesia according to the particularities of each patient, the multimodal monitoring of these patients is highly recommended. Classically, general anesthesia monitoring consists of the analysis of vital functions and gas exchange. Multimodal monitoring refers to the concomitant monitoring of the degree of hypnosis and the nociceptive-antinociceptive balance. By titrating anesthetic drugs according to these parameters, clinical benefits can be obtained, such as hemodynamic stabilization, the reduction of awakening times, and the reduction of postoperative complications. Another important aspect is the impact on the status of inflammation and the redox balance. By minimizing inflammatory and oxidative impact, a faster recovery can be achieved that increases patient safety. The purpose of this literature review is to present the most modern multimodal monitoring techniques to discuss the particularities of each technique.

Resting-State Electroencephalography for Prognosis in Disorders of Consciousness Following Traumatic Brain Injury

Although the majority of patients recover consciousness after a traumatic brain injury (TBI), a minority develop a prolonged disorder of consciousness, which may never fully resolve. For these patients, accurate prognostication is essential to treatment decisions and long-term care planning. In this review, we evaluate the use of resting-state electroencephalography (EEG) as a prognostic measure in disorders of consciousness following TBI. We highlight that routine clinical EEG recordings have prognostic utility in the short to medium term. In particular, measures of alpha power and variability are indicative of relatively better functional outcomes within the first year post-TBI. This is hypothesized to reflect intact thalamocortical loops, and thus the potential for recovery of consciousness even in the apparent absence of current consciousness. However, there is a lack of research into the use of resting-state EEG for predicting longer-term recovery following TBI. We conclude that, given the potential for patients to demonstrate improvements in consciousness and functional capacity even years after TBI, a research focus on EEG-augmented prognostication in very long-term disorders of consciousness is now required.

Electroencephalographic features in patients undergoing extracorporeal membrane oxygenation

Background

Neurologic injury is one of the most frequent causes of death in patients undergoing extracorporeal membrane oxygenation (ECMO). As neurological examination is often unreliable in sedated patients, additional neuromonitoring is needed. However, the value of electroencephalogram (EEG) in adult ECMO patients has not been well assessed. Therefore, the aim of this study was to assess the occurrence of electroencephalographic abnormalities in patients treated with extracorporeal membrane oxygenation (ECMO) and their association with 3-month neurologic outcome.

Methods

Retrospective analysis of all patients undergoing venous–venous (V–V) or venous–arterial (V–A) ECMO with a concomitant EEG recording (April 2009–December 2018), either recorded intermittently or continuously. EEG background was classified into four categories: mild/moderate encephalopathy (i.e., mostly defined by the presence of reactivity), severe encephalopathy (mostly defined by the absence of reactivity), burst-suppression (BS) and suppressed background. Epileptiform activity (i.e., ictal EEG pattern, sporadic epileptiform discharges or periodic discharges) and asymmetry were also reported. EEG findings were analyzed according to unfavorable neurological outcome (UO, defined as Glasgow Outcome Scale < 4) at 3 months after discharge.

Results

A total of 139 patients (54 [41–62] years; 60 (43%) male gender) out of 596 met the inclusion criteria and were analyzed. Veno–arterial (V–A) ECMO was used in 98 (71%); UO occurred in 99 (71%) patients. Continuous EEG was performed in 113 (81%) patients. The analysis of EEG background showed that 29 (21%) patients had severe encephalopathy, 4 (3%) had BS and 19 (14%) a suppressed background. In addition, 11 (8%) of patients had seizures or status epilepticus, 10 (7%) had generalized periodic discharges or lateralized periodic discharges, and 27 (19%) had asymmetry on EEG. In the multivariate analysis, the occurrence of ischemic stroke or intracranial hemorrhage (OR 4.57 [1.25–16.74]; p = 0.02) and a suppressed background (OR 10.08 [1.24–82.20]; p = 0.03) were independently associated with UO. After an adjustment for covariates, an increasing probability for UO was observed with more severe EEG background categories.

Conclusions

In adult patients treated with ECMO, EEG can identify patients with a high likelihood of poor outcome. In particular, suppressed background was independently associated with unfavorable neurological outcome.

Neural Oscillations: Understanding a Neural Code of Pain

Neural oscillations play an important role in the integration and segregation of brain regions that are important for brain functions, including pain. Disturbances in oscillatory activity are associated with several disease states, including chronic pain. Studies of neural oscillations related to pain have identified several functional bands, especially alpha, beta, and gamma bands, implicated in nociceptive processing. In this review, we introduce several properties of neural oscillations that are important to understand the role of brain oscillations in nociceptive processing. We also discuss the role of neural oscillations in the maintenance of efficient communication in the brain. Finally, we discuss the role of neural oscillations in healthy and chronic pain nociceptive processing. These data and concepts illustrate the key role of regional and interregional neural oscillations in nociceptive processing underlying acute and chronic pains.

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.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

BACKGROUND

Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.

METHODS

Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.

RESULTS

Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.

CONCLUSIONS

Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

Disruption of Brain-Heart Coupling in Sepsis

PURPOSE

To investigate heart rate and EEG variability and their coupling in patients with sepsis and determine their relationship to sepsis severity and severity of sepsis-associated brain dysfunction.

METHODS

Fifty-two patients with sepsis were prospectively identified, categorized as comatose (N = 30) and noncomatose (N = 22), and compared with 11 control subjects. In a 30-minute EEG and electrocardiogram recording, heart rate variability and EEG variability (measured by the variability of relative power in a modified alpha band = RAP) and their coupled oscillations were quantified using linear (least-square periodogram and magnitude square coherence) and nonlinear (Shannon entropy and mutual information) measures. These measures were compared between the three groups and correlated with outcome, adjusting for severity of sepsis.

RESULTS

Several measures of heart rate variability and EEG variability and of their coupled oscillations were significantly lower in patients with sepsis compared with controls and correlated with outcome. This correlation was not independent when adjusting for severity of sepsis.

CONCLUSIONS

Sepsis is associated with lower variability of both heart rate and RAP on EEG and reduction of their coupled oscillations. This uncoupling is associated with the severity of encephalopathy. Combined EEG and electrocardiogram monitoring may be used to gain insight in underlying mechanisms of sepsis and quantify brainstem or thalamic dysfunction.

Quantitative EEG Parameters for Prediction of Outcome in Severe Traumatic Brain Injury: Development Study

Monitoring of quantitative EEG (QEEG) parameters in the intensive care unit (ICU) can aid in the treatment of traumatic brain injury (TBI) patients by complementing visual EEG review done by an expert. We performed an explorative study investigating the prognostic value of 59 QEEG parameters in predicting the outcome of patients with severe TBI. Continuous EEG recordings were done on 28 patients with severe TBI in the ICU of Turku University Hospital. We computed a set of QEEG parameters for each patient, and correlated these to patient outcome, measured by dichotomized Glasgow Outcome Scale (GOS) at a follow-up visit between 6 and 12 months, using area under receiver operating characteristic curve (AUC) as a nonlinear correlation measure. For 17 of the 59 QEEG parameters (28.8%), the AUC differed significantly from 0.5, most of these parameters measured EEG power or variability. The best QEEG parameters for outcome prediction were alpha power (AUC = 0.87, P < .01) and variability of the relative fast theta power (AUC = 0.84, P < .01). The results of this study indicate that QEEG parameters provide useful information for predicting outcome in severe TBI. Novel QEEG parameters with potential in outcome prediction were found, the prognostic value of these parameters should be confirmed in later studies. The results also provide further evidence of the usefulness of parameters studied in preexisting studies.

Seizures and the Role of Anticonvulsants After Traumatic Brain Injury

Posttraumatic seizures are a common complication of traumatic brain injury. Posttraumatic epilepsy accounts for 20% of symptomatic epilepsy in the general population and 5% of all epilepsy. Early posttraumatic seizures occur in more than 20% of patients in the intensive care unit and are associated with secondary brain injury and worse patient outcomes. Most posttraumatic seizures are nonconvulsive and therefore continuous electroencephalography monitoring should be the standard of care for patients with moderate or severe brain injury. The literature shows that posttraumatic seizures result in secondary brain injury caused by increased intracranial pressure, cerebral edema and metabolic crisis.

Neurophysiological assessment of brain dysfunction in critically ill patients: an update

The aim of this review was to provide up-to-date information about the usefulness of clinical neurophysiology testing in the management of critically ill patients. Evoked potentials (EPs) and electroencephalogram (EEG) are non-invasive clinical neurophysiology tools that allow an objective assessment of the central nervous system's function at the bedside in intensive care unit (ICU). These tests are quite useful in diagnosing cerebral complications, and establishing the vital and functional prognosis in ICU. EEG keeps a particularly privileged importance in detecting seizures phenomena such as subclinical seizures and non-convulsive status epilepticus. Quantitative EEG (QEEG) analysis techniques commonly called EEG Brain mapping can provide obvious topographic displays of digital EEG signals characteristics, showing the potential distribution over the entire scalp including filtering, frequency, and amplitude analysis and color mapping. Evidences of usefulness of QEEG for seizures detection in ICU are provided by several recent studies. Furthermore, beyond detection of epileptic phenomena, changes of some QEEG panels are early warning indicators of sedation level as well as brain damage or dysfunction in ICU. EPs offer the opportunity for assessing brainstem's functional integrity, as well as subcortical and cortical brain areas. A multimodal use, combining EEG and various modalities of EPs is recommended since this allows a more accurate functional exploration of the brain and helps caregivers to tailor therapeutic measures according to neurological worsening trends and to anticipate the prognosis in ICU.

Multimodal neurologic monitoring

Neurocritical care has two main objectives. Initially, the emphasis is on treatment of patients with acute damage to the central nervous system whether through infection, trauma, or hemorrhagic or ischemic stroke. Thereafter, attention shifts to the identification of secondary processes that may lead to further brain injury, including fever, seizures, and ischemia, among others. Multimodal monitoring is the concept of using various tools and data integration to understand brain physiology and guide therapeutic interventions to prevent secondary brain injury. This chapter will review the use of electroencephalography, intracranial pressure monitoring, brain tissue oxygenation, cerebral microdialysis and neurochemistry, near-infrared spectroscopy, and transcranial Doppler sonography as they relate to neuromonitoring in the critically ill. The concepts and design of each monitor, in addition to the patient population that may most benefit from each modality, will be discussed, along with the various tools that can be used together to guide individualized patient treatment options. Major clinical trials, observational studies, and their effect on clinical outcomes will be reviewed. The future of multimodal monitoring in the field of bioinformatics, clinical research, and device development will conclude the chapter.

Pre-stimulus Alpha Oscillations and Inter-subject Variability of Motor Evoked Potentials in Single- and Paired-Pulse TMS Paradigms

Inter- and intra-subject variability of the motor evoked potentials (MEPs) to TMS is a well-known phenomenon. Although a possible link between this variability and ongoing brain oscillations was demonstrated, the results of the studies are not consistent with each other. Exploring this topic further is important since the modulation of MEPs provides unique possibility to relate oscillatory cortical phenomena to the state of the motor cortex probed with TMS. Given that alpha oscillations were shown to reflect cortical excitability, we hypothesized that their power and variability might explain the modulation of subject-specific MEPs to single- and paired-pulse TMS (spTMS, ppTMS, respectively). Neuronal activity was recorded with multichannel electroencephalogram. We used spTMS and two ppTMS conditions: intracortical facilitation (ICF) and short-interval intracortical inhibition (SICI). Spearman correlations were calculated within and across subjects between MEPs and the pre-stimulus power of alpha oscillations in low (8-10 Hz) and high (10-12 Hz) frequency bands. Coefficient of quartile variation was used to measure variability. Across-subject analysis revealed no difference in the pre-stimulus alpha power among the TMS conditions. However, the variability of high-alpha power in spTMS condition was larger than in the SICI condition. In ICF condition pre-stimulus high-alpha power variability correlated positively with MEP amplitude variability. No correlation has been observed between the pre-stimulus alpha power and MEP responses in any of the conditions. Our results show that the variability of the alpha oscillations can be more predictive of TMS effects than the commonly used power of oscillations and we provide further support for the dissociation of high and low-alpha bands in predicting responses produced by the stimulation of the motor cortex.

Prediction of outcome in traumatic brain injury patients using long-term qEEG features

Treatment of patients suffering from severe traumatic brain injury (TBI) commonly involves sedation and mechanical ventilation during prolonged stay in the intensive care unit. Continuous EEG is often monitored in these patients to detect epileptic seizures. It has also been suggested that EEG has prognostic value regarding the outcome of the treatment. In this study the ability of 186 qEEG features to predict the outcome of the treatment of TBI patients is assessed. The features are based on the power spectrum of the EEG. The data underlying the study contains long term (over 24 h) recordings from 20 patients treated in the postoperative intensive care unit of the North Estonian Medical Center. 12 qEEG features were found to have predictive value when evaluated by calculating the area under the receiver operating curve constructed from feature probabilities.

Electrophysiologic monitoring in acute brain injury

To determine the optimal use and indications of electroencephalography (EEG) in critical care management of acute brain injury (ABI). An electronic literature search was conducted for articles in English describing electrophysiological monitoring in ABI from January 1990 to August 2013. A total of 165 studies were included. EEG is a useful monitor for seizure and ischemia detection. There is a well-described role for EEG in convulsive status epilepticus and cardiac arrest (CA). Data suggest EEG should be considered in all patients with ABI and unexplained and persistent altered consciousness and in comatose intensive care unit (ICU) patients without an acute primary brain condition who have an unexplained impairment of mental status. There remain uncertainties about certain technical details, e.g., the minimum duration of EEG studies, the montage, and electrodes. Data obtained from both EEG and EP studies may help estimate prognosis in ABI patients, particularly following CA and traumatic brain injury. Data supporting these recommendations is sparse, and high quality studies are needed. EEG is used to monitor and detect seizures and ischemia in ICU patients and indications for EEG are clear for certain disease states, however, uncertainty remains on other applications.

Electrophysiologic recordings in traumatic brain injury

Following a traumatic brain injury (TBI), the brain undergoes numerous electrophysiologic changes. The most common techniques used to evaluate these changes include electroencepalography (EEG) and evoked potentials. In animals, EEGs immediately following TBI can show either diffuse slowing or voltage attenuation, or high voltage spiking. Following a TBI, many animals display evidence of hippocampal excitability and a reduced seizure threshold. Some mice subjected to severe TBI via a fluid percussion injury will eventually develop seizures, which provides a useful potential model for studying the neurophysiology of epileptogenesis. In humans, the EEG changes associated with mild TBI are relatively subtle and may be challenging to distinguish from EEG changes seen in other conditions. Quantitative EEG (QEEG) may enhance the ability to detect post-traumatic electrophysiologic changes following a mild TBI. Some types of evoked potential (EP) and event related potential (ERP) can also be used to detect post-traumatic changes following a mild TBI. Continuous EEG monitoring (cEEG) following moderate and severe TBI is useful in detecting the presence of seizures and status epilepticus acutely following an injury, although some seizures may only be detectable using intracranial monitoring. CEEG can also be helpful for assessing prognosis after moderate or severe TBI. EPs, particularly somatosensory evoked potentials, can also be useful in assessing prognosis following severe TBI. The role for newer technologies such as magnetoencephalography and bispectral analysis (BIS) in the evaluation of patients with TBI remains unclear.

Measuring brain activity cycling (BAC) in long term EEG monitoring of preterm babies

Measuring fluctuation of vigilance states in early preterm infants undergoing long term intensive care holds promise for monitoring their neurological well-being. There is currently, however, neither objective nor quantitative methods available for this purpose in a research or clinical environment. The aim of this proof-of-concept study was, therefore, to develop quantitative measures of the fluctuation in vigilance states or brain activity cycling (BAC) in early preterm infants. The proposed measures of BAC were summary statistics computed on a frequency domain representation of the proportional duration of spontaneous activity transients (SAT%) calculated from electroencephalograph (EEG) recordings. Eighteen combinations of three statistics and six frequency domain representations were compared to a visual interpretation of cycling in the SAT% signal. Three high performing measures (band energy/periodogram: R = 0.809, relative band energy/nonstationary frequency marginal: R = 0.711, g-statistic/nonstationary frequency marginal: R = 0.638) were then compared to a grading of sleep wake cycling based on the visual interpretation of the amplitude-integrated EEG trend. These measures of BAC are conceptually straightforward, correlate well with the visual scores of BAC and sleep wake cycling, are robust enough to cope with the technically compromised monitoring data available in intensive care units, and are recommended for further validation in prospective studies.

Utility of continuous EEG in children with acute traumatic brain injury

Traumatic brain injury is a leading cause of childhood morbidity and mortality. The use of continuous EEG monitoring in the pediatric intensive care unit setting to aid in the management of acute traumatic brain injury is becoming more common, although practice does vary between institutions. This variability is a product of the relative paucity of data, particularly as it applies to prospective studies, in evaluating the use of continuous EEG after traumatic brain injury in the pediatric population. This review will summarize the current literature involving the utility of continuous EEG monitoring in children with acute traumatic brain injury, with focus on specific indications, impact on management, and prognostic value.

Physiological monitoring of the severe traumatic brain injury patient in the intensive care unit

Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. Despite encouraging animal research, pharmacological agents and neuroprotectants have disappointed in the clinical environment. Current TBI management therefore is directed towards identification, prevention, and treatment of secondary cerebral insults that are known to exacerbate outcome after injury. This strategy is based on a variety of monitoring techniques that include the neurological examination, imaging, laboratory analysis, and physiological monitoring of the brain and other organ systems used to guide therapeutic interventions. Recent clinical series suggest that TBI management informed by multimodality monitoring is associated with improved patient outcome, in part because care is provided in a patient-specific manner. In this review we discuss physiological monitoring of the brain after TBI and the emerging field of neurocritical care bioinformatics.

Clinical review: neuromonitoring - an update

Critically ill patients are frequently at risk of neurological dysfunction as a result of primary neurological conditions or secondary insults. Determining which aspects of brain function are affected and how best to manage the neurological dysfunction can often be difficult and is complicated by the limited information that can be gained from clinical examination in such patients and the effects of therapies, notably sedation, on neurological function. Methods to measure and monitor brain function have evolved considerably in recent years and now play an important role in the evaluation and management of patients with brain injury. Importantly, no single technique is ideal for all patients and different variables will need to be monitored in different patients; in many patients, a combination of monitoring techniques will be needed. Although clinical studies support the physiologic feasibility and biologic plausibility of management based on information from various monitors, data supporting this concept from randomized trials are still required.

Research and technology in neurocritical care

The daily practice of neurointensivists focuses on the recognition of subtle changes in the neurological examination, interactions between the brain and systemic derangements, and brain physiology. Common alterations such as fever, hyperglycemia, and hypotension have different consequences in patients with brain insults compared with patients of general medical illness. Various technologies have become available or are currently being developed. The session on "research and technology" of the first neurocritical care research conference held in Houston in September of 2009 was devoted to the discussion of the current status, and the research role of state-of-the art technologies in neurocritical patients including multi-modality neuromonitoring, biomarkers, neuroimaging, and "omics" research (proteomix, genomics, and metabolomics). We have summarized the topics discussed in this session. We have provided a brief overview of the current status of these technologies, and put forward recommendations for future research applications in the field of neurocritical care.

Trauma and impaired consciousness

This article discusses brain trauma and impaired consciousness. It reviews the various states of impaired consciousness related to trauma, with an historical and current literature viewpoint. The causes and pathophysiology of impaired consciousness in concussion, diffuse axonal injury, and focal brain lesions are discussed and management options evaluated.

Predicting Outcome after Traumatic Brain Injury

The purpose of this review is to look at the most recent work carried out on predicting outcome after traumatic brain injury (TBI). TBI is a leading cause of death and disability but prediction of long-term outcome for individual patients is difficult. In particular, predicting outcome in the first few hours or days after injury is limited by the paucity of scoring systems or clinical models available. Many clinical variables have been studied to determine if they may play a role in outcome prediction, including age, admission Glasgow Coma Score and pupillary reactivity. Newer variables being studied include serum biomarkers, abnormalities seen on magnetic resonance imaging and data obtained from evoked potentials and electroencephalography studies. There are many factors that impact on outcome and a perfect model is yet to be developed. Models must take into account the economic status of the country in which the trauma occurs. It is important that less affluent nations are not left behind in the search for accurate prognostic modelling.

Continuous EEG monitoring: is it ready for prime time?

PURPOSE OF REVIEW

Continuous electroencephalography (cEEG) is being used more frequently in intensive care units to detect epileptic activity and ischemia. This review analyzes clinical applications and limitations of cEEG as a routine neuromonitoring tool.

RECENT FINDINGS

cEEG is primarily used to detect nonconvulsive seizures, which are frequent and possibly associated with harm. Cerebral ischemia, such as that from vasospasm after subarachnoid hemorrhage, can be detected earlier by EEG and quantitative EEG (qEEG). Highly skilled technicians and subspecialty-trained physicians are needed to generate good quality EEG and to interpret these data. qEEG allows more efficient interpretation of large amounts of EEG and may trigger prespecified alarms. Currently, there is little high-quality data on cEEG to define indications, cost-saving potential, and impact on outcome. A few studies have demonstrated how cEEG can be integrated into multimodality brain monitoring of severely brain-injured patients.

SUMMARY

cEEG should be considered as an integral part of multimodality monitoring of the injured brain, particularly in patients at risk for nonconvulsive seizure or ischemia. Automated alarms may help establish cEEG monitoring as an integral part of brain monitoring. All neurological ICUs that routinely care for patients with refractory status epilepticus should have the capability to perform cEEG monitoring. Further research determining the impact on outcome and making EEG monitoring more user friendly may help move this technique out of the subspecialized ICU setting into the general ICU environment. In the future, it may be possible to use specific EEG parameters as endpoints for therapeutic interventions.

Continuous EEG monitoring in the ICU

Continuous EEG (cEEG) monitoring is one of many available techniques to assess cerebral function in critically ill patients. Detection and treatment of nonconvulsive seizures (NCSZ) and nonconvulsive status epilepticus (NCSE) are the main clinical applications of cEEG. These patterns are common and associated with poor outcome after severe brain injury. Quantitative EEG parameters can be used for early detection of NCSZ and ischemia caused by vasospasm after subarachnoid hemorrhage. Early and aggressive treatment of such complications may prevent secondary brain injury and avoid irreversible damage. Periodic epileptiform discharges (PEDs) are also seen frequently after acute brain injury and may be associated with poor outcome. However, to date, it is uncertain whether NCSZ, NCSE or PEDs cause additional injury or if they are epiphenomena of brain damage. Currently, there are many limitations to the widespread use of cEEG, particularly the lack of high quality studies. In the future, the role of cEEG as part of multimodality neuromonitoring should be further investigated to determine if optimization of neuronal activity, brain metabolism, oxygenation and perfusion profiles can prevent further damage to the brain and thereby improve outcome.

Morphometric MRI findings in the thalamus and brainstem in children after moderate to severe traumatic brain injury

Generalized whole brain volume loss is well documented in moderate to severe traumatic brain injury. Whether this atrophy occurs in the thalamus and brainstem has not been systematically studied in children. Magnetic resonance imaging (MRI) quantitative analysis was used to investigate brain volume loss in the thalamus and brainstem in 16 traumatic brain injury subjects (age range 9-16 years) compared with 16 age and demo-graphically matched controls. Based on multiple analysis of covariance, controlling for age and head size, reduced volume in the thalamus and the midbrain region of the brainstem were found. General linear model analyses revealed a relation between processing speed on a working memory task and midbrain and brain stem volumes. Reduced volume in thalamic and brainstem structures were associated with traumatic brain injury. Reduction in midbrain and thalamic volume is probably a reflection of the secondary effects of diffuse axonal injury and reduction in cortical volume from brain injury.

Multimodality monitoring in neurocritical care

Multimodality monitoring of cerebral physiology encompasses the application of different monitoring techniques and integration of several measured physiologic and biochemical variables into assessment of brain metabolism, structure, perfusion, and oxygenation status. Novel monitoring techniques include transcranial Doppler ultrasonography, neuroimaging, intracranial pressure, cerebral perfusion, and cerebral blood flow monitors, brain tissue oxygen tension monitoring, microdialysis, evoked potentials, and continuous electroencephalogram. Multimodality monitoring enables immediate detection and prevention of acute neurologic injury as well as appropriate intervention based on patients' individual disease states in the neurocritical care unit. Real-time analysis of cerebral physiologic, metabolic, and cardiovascular parameters simultaneously has broadened knowledge about complex brain pathophysiology and cerebral hemodynamics. Integration of this information allows for more precise diagnosis and optimization of management of patients with brain injury.