Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury

Neurocritical Care in 2024: Where are We Headed?

Providing specialized care to critically ill neurology patients has improved outcomes for patients with neurological emergencies; however, there are still some gaps in neurocritical care (NCC) that offer opportunities for improvement. Among these gaps, improving education of the multidisciplinary NCC team, targeting individualized treatments for neurologically critically ill patients, and reducing disparities for undeserved patients as well as disadvantaged areas are priorities to advance the field. This review focuses on the current challenges neurointensivists face, including difficulties in neuroprognostication, ethical challenges in end-of-life care, and neuropalliative care. Challenges also involve providing specific NCC education for the multidisciplinary NCC team, as well as advancing research to provide treatments for critically ill neurological patients. Finally, the authors describe future directions that can take NCC to the next level.

Methods of Evaluating EEG Reactivity in Adult Intensive Care Units: A Review

PURPOSE

EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent.

METHODS

A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias.

RESULTS

One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds).

CONCLUSIONS

This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature.

EEG reactivity in neurologic prognostication in post-cardiac arrest patients: A narrative review

Electroencephalographic reactivity (EEG-R) is a promising early predictor of arousal in comatose patients after cardiac arrest. Despite recent guidelines advocating for the integration of EEG-R into the multimodal prognostication model, EEG-R testing methods remain heterogeneous across studies. While efforts towards standardization have been made to reduce interrater variability by the development of quantitative approaches and machine learning models, future validation studies are needed to increase clinical applicability. Furthermore, the specific neurophysiological mechanisms and neuroanatomical correlates underlying EEG-R are not fully understood. In this narrative review, we explore the value and possible mechanisms of EEG-R, focusing on post-cardiac arrest comatose patients. We aim to discuss the current standard of knowledge and future directions, as well as elucidate possible implications for patient care and research.

Burst-Suppression EEG Reactivity to Photic Stimulation-A Translational Biomarker in Hypoxic-Ischemic Brain Injury

The reactivity of an electroencephalogram (EEG) to external stimuli is impaired in comatose patients showing burst-suppression (BS) patterns following hypoxic-ischemic brain injury (HIBI). We explored the reactivity of BS induced by isoflurane in rat models of HIBI and controls using intermittent photic stimulation (IPS) delivered to one eye. The relative time spent in suppression referred to as the suppression ratio (SR) was measured on the contralateral fronto-occipital cortical EEG channel. The BS reactivity (BSR) was defined as the decrease in the SR during IPS from the baseline before stimulation (SRPRE). We found that BSR increased with SRPRE. To standardize by anesthetic depth, we derived the BSR index (BSRi) as BSR divided by SRPRE. We found that the BSRi was decreased at 3 days after transient global cerebral ischemia in rats, which is a model of brain injury after cardiac arrest. The BSRi was also reduced 2 months after experimental perinatal asphyxia in rats, a model of birth asphyxia, which is a frequent neonatal complication in humans. Furthermore, Oxytocin attenuated BSRi impairment, consistent with a neuroprotective effect in this model. Our data suggest that the BSRi is a promising translational marker in HIBI which should be considered in future neuroprotection studies.

Prognostic value of early EEG abnormalities in severe stroke patients requiring mechanical ventilation: a pre-planned analysis of the SPICE prospective multicenter study

INTRODUCTION

Prognostication of outcome in severe stroke patients necessitating invasive mechanical ventilation poses significant challenges. The objective of this study was to assess the prognostic significance and prevalence of early electroencephalogram (EEG) abnormalities in adult stroke patients receiving mechanical ventilation.

METHODS

This study is a pre-planned ancillary investigation within the prospective multicenter SPICE cohort study (2017-2019), conducted in 33 intensive care units (ICUs) in the Paris area, France. We included adult stroke patients requiring invasive mechanical ventilation, who underwent at least one intermittent EEG examination during their ICU stay. The primary endpoint was the functional neurological outcome at one year, determined using the modified Rankin scale (mRS), and dichotomized as unfavorable (mRS 4-6, indicating severe disability or death) or favorable (mRS 0-3). Multivariable regression analyses were employed to identify EEG abnormalities associated with functional outcomes.

RESULTS

Of the 364 patients enrolled in the SPICE study, 153 patients (49 ischemic strokes, 52 intracranial hemorrhages, and 52 subarachnoid hemorrhages) underwent at least one EEG at a median time of 4 (interquartile range 2-7) days post-stroke. Rates of diffuse slowing (70% vs. 63%, p = 0.37), focal slowing (38% vs. 32%, p = 0.15), periodic discharges (2.3% vs. 3.7%, p = 0.9), and electrographic seizures (4.5% vs. 3.7%, p = 0.4) were comparable between patients with unfavorable and favorable outcomes. Following adjustment for potential confounders, an unreactive EEG background to auditory and pain stimulations (OR 6.02, 95% CI 2.27-15.99) was independently associated with unfavorable outcomes. An unreactive EEG predicted unfavorable outcome with a specificity of 48% (95% CI 40-56), sensitivity of 79% (95% CI 72-85), and positive predictive value (PPV) of 74% (95% CI 67-81). Conversely, a benign EEG (defined as continuous and reactive background activity without seizure, periodic discharges, triphasic waves, or burst suppression) predicted favorable outcome with a specificity of 89% (95% CI 84-94), and a sensitivity of 37% (95% CI 30-45).

CONCLUSION

The absence of EEG reactivity independently predicts unfavorable outcomes at one year in severe stroke patients requiring mechanical ventilation in the ICU, although its prognostic value remains limited. Conversely, a benign EEG pattern was associated with a favorable outcome.

Neuromonitoring in the ICU - what, how and why?

PURPOSE OF REVIEW

We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury.

RECENT FINDINGS

Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms.

SUMMARY

The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.

Coma Prognostication After Acute Brain Injury: A Review

Importance

Among the most impactful neurologic assessments is that of neuroprognostication, defined here as the prediction of neurologic recovery from disorders of consciousness caused by severe, acute brain injury. Across a range of brain injury etiologies, these determinations often dictate whether life-sustaining treatment is continued or withdrawn; thus, they have major implications for morbidity, mortality, and health care costs. Neuroprognostication relies on a diverse array of tests, including behavioral, radiologic, physiological, and serologic markers, that evaluate the brain's functional and structural integrity.

Observations

Prognostic markers, such as the neurologic examination, electroencephalography, and conventional computed tomography and magnetic resonance imaging (MRI), have been foundational in assessing a patient's current level of consciousness and capacity for recovery. Emerging techniques, such as functional MRI, diffusion MRI, and advanced forms of electroencephalography, provide new ways of evaluating the brain, leading to evolving schemes for characterizing neurologic function and novel methods for predicting recovery.

Conclusions and Relevance

Neuroprognostic markers are rapidly evolving as new ways of assessing the brain's structural and functional integrity after brain injury are discovered. Many of these techniques remain in development, and further research is needed to optimize their prognostic utility. However, even as such efforts are underway, a series of promising findings coupled with the imperfect predictive value of conventional prognostic markers and the high stakes of these assessments have prompted clinical guidelines to endorse emerging techniques for neuroprognostication. Thus, clinicians have been thrust into an uncertain predicament in which emerging techniques are not yet perfected but too promising to ignore. This review illustrates the current, and likely future, landscapes of prognostic markers. No matter how much prognostic markers evolve and improve, these assessments must be approached with humility and individualized to reflect each patient's values.

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Introduction

Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.

Methods

In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.

Results

Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).

Discussion

Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Predicting neurological outcome after cardiac arrest by combining computational parameters extracted from standard and deviant responses from auditory evoked potentials

Background

Despite multimodal assessment (clinical examination, biology, brain MRI, electroencephalography, somatosensory evoked potentials, mismatch negativity at auditory evoked potentials), coma prognostic evaluation remains challenging.

Methods

We present here a method to predict the return to consciousness and good neurological outcome based on classification of auditory evoked potentials obtained during an oddball paradigm. Data from event-related potentials (ERPs) were recorded noninvasively using four surface electroencephalography (EEG) electrodes in a cohort of 29 post-cardiac arrest comatose patients (between day 3 and day 6 following admission). We extracted retrospectively several EEG features (standard deviation and similarity for standard auditory stimulations and number of extrema and oscillations for deviant auditory stimulations) from the time responses in a window of few hundreds of milliseconds. The responses to the standard and the deviant auditory stimulations were thus considered independently. By combining these features, based on machine learning, we built a two-dimensional map to evaluate possible group clustering.

Results

Analysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favoring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode. Using Gaussian, K-neighborhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. Moreover, the same results were obtained with one single electrode (Cz).

Conclusion

statistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.