Early prediction of delayed cerebral ischemia in subarachnoid hemorrhage based on quantitative EEG: A prospective study in adults

Prolonged Monitoring of Brain Electrical Activity in the Intensive Care Unit

Electroencephalography (EEG) has been used to assess brain electrical activity for over a century. More recently, technological advancements allowed EEG to be a widely available and powerful tool in the intensive care unit (ICU), where patients at risk for cerebral dysfunction and brain injury can be monitored in a continuous, real-time manner. In the last 2 decades, several organizations established guidelines for continuous EEG monitoring in the ICU, defining critical care EEG terminology and technical standards for technicians, machines, and electroencephalographers. This article provides an overview of the current role of continuous EEG monitoring in the ICU.

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment.

Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting.

Novel EEG Metric Correlates with Intracranial Pressure in an Animal Model

BACKGROUND

Intracranial pressure (ICP) can be continuously and reliably measured using invasive monitoring through an external ventricular catheter or an intraparenchymal probe. We explore electroencephalography (EEG) to identify a reliable real-time noninvasive ICP correlate.

METHODS

Using a previously described porcine model of intracranial hypertension, we examined the cross correlation between ICP time series and the slope of the EEG power spectral density as described by ϕ. We calculated ϕ as tan-1 (slope of power spectral density) and normalized it by π, where slope is that of the power-law fit (log frequency vs. log power) to the power spectral density of the EEG signal. Additionally, we explored the relationship between the ϕ time series and cerebral perfusion pressure. A total of 11 intracranial hypertension episodes across three different animals were studied.

RESULTS

The mean correlation between ϕ angle and ICP was - 0.85 (0.15); the mean correlation with cerebral perfusion pressure was 0.92 (0.02). Significant correlation occurred at zero lag. In the absence of intracranial hypertension, the absolute value of the ϕ angle was greater than 0.9 (mean 0.936 radians). However, during extreme intracranial hypertension causing cerebral circulatory arrest, the ϕ angle is on average below 0.9 radians (mean 0.855 radians).

CONCLUSIONS

EEG ϕ angle is a promising real-time noninvasive measure of ICP/cerebral perfusion using surface electroencephalography.

Contemporary management of aneurysmal subarachnoid haemorrhage. An update for the intensivist

Aneurysmal subarachnoid haemorrhage (aSAH) is a rare yet profoundly debilitating condition associated with high global case fatality and morbidity rates. The key determinants of functional outcome include early brain injury, rebleeding of the ruptured aneurysm and delayed cerebral ischaemia. The only effective way to reduce the risk of rebleeding is to secure the ruptured aneurysm quickly. Prompt diagnosis, transfer to specialized centers, and meticulous management in the intensive care unit (ICU) significantly improved the prognosis of aSAH. Recently, multimodality monitoring with specific interventions to correct pathophysiological imbalances has been proposed. Vigilance extends beyond intracranial concerns to encompass systemic respiratory and haemodynamic monitoring, as derangements in these systems can precipitate secondary brain damage. Challenges persist in treating aSAH patients, exacerbated by a paucity of robust clinical evidence, with many interventions showing no benefit when tested in rigorous clinical trials. Given the growing body of literature in this field and the issuance of contemporary guidelines, our objective is to furnish an updated review of essential principles of ICU management for this patient population. Our review will discuss the epidemiology, initial stabilization, treatment strategies, long-term prognostic factors, the identification and management of post-aSAH complications. We aim to offer practical clinical guidance to intensivists, grounded in current evidence and expert clinical experience, while adhering to a concise format.

Electrophysiological monitoring of neurological functions at the acute phase of brain injury: An overview of current knowledge and future perspectives in the adult population

The continuous monitoring of physiological parameters is now considered as a standard of care in intensive care units (ICU). While multiple techniques are available to guide hemodynamic or respiratory management, the monitoring of neurological function in unconscious patients is usually limited to discontinuous bedside neurological examination or morphological brain imaging. However, cortical activity is accessible at the bedside with electroencephalography (EEG), electrocorticography (ECoG) or evoked potentials. The analysis of the unprocessed signal requires a trained neurophysiologist and could be time consuming. During the past decades, advances in neurophysiological signal acquisition make it possible to calculate quantified EEG parameters in real-time. New monitors also provide ICU friendly display for a dynamic and live assessment of neurological function changes. In this review, we will describe the technical aspects of EEG, ECoG and evoked potentials required for a good signal quality before interpretation. We will discuss how to use those electrophysiological techniques in the ICU to assess neurological function in comatose patients at the acute phase of brain injuries such as traumatic brain injuries, haemorrhagic or ischemic stroke. We will discuss, which quantitative EEG or evoked potentials monitoring parameters can be used at the bedside to guide sedation, evaluate neurological function during awaking and look for new neurological (encephalic or brainstem) injuries. We will present the state of the art and discuss some analyses, which may develop shortly.

The neurophysiological effect of mild hypothermia in gyrencephalic brains submitted to ischemic stroke and spreading depolarizations

Objective

Characterize the neurophysiological effects of mild hypothermia on stroke and spreading depolarizations (SDs) in gyrencephalic brains.

Methods

Left middle cerebral arteries (MCAs) of six hypothermic and six normothermic pigs were permanently occluded (MCAo). Hypothermia began 1 h after MCAo and continued throughout the experiment. ECoG signals from both frontoparietal cortices were recorded. Five-minute ECoG epochs were collected 5 min before, at 5 min, 4, 8, 12, and 16 h after MCAo, and before, during, and after SDs. Power spectra were decomposed into fast (alpha, beta, and gamma) and slow (delta and theta) frequency bands.

Results

In the vascular insulted hemisphere under normothermia, electrodes near the ischemic core exhibited power decay across all frequency bands at 5 min and the 4th hour after MCAo. The same pattern was registered in the two furthest electrodes at the 12th and 16th hour. When mild hypothermia was applied in the vascular insulted hemispheres, the power decay was generalized and seen even in electrodes with uncompromised blood flow. During SD analysis, hypothermia maintained increased delta and beta power during the three phases of SDs in the furthest electrode from the ischemic core, followed by the second furthest and third electrode in the beta band during preSD and postSD segments. However, in hypothermic conditions, the third electrode showed lower delta, theta, and alpha power.

Conclusion

Mild hypothermia attenuates all frequency bands in the vascularly compromised hemisphere, irrespective of the cortical location. During SD formation, it preserves power spectra more significantly in electrodes further from the ischemic core.

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.

2023 Guideline for the Management of Patients With Aneurysmal Subarachnoid Hemorrhage: A Guideline From the American Heart Association/American Stroke Association

AIM

The "2023 Guideline for the Management of Patients With Aneurysmal Subarachnoid Hemorrhage" replaces the 2012 "Guidelines for the Management of Aneurysmal Subarachnoid Hemorrhage." The 2023 guideline is intended to provide patient-centric recommendations for clinicians to prevent, diagnose, and manage patients with aneurysmal subarachnoid hemorrhage.

METHODS

A comprehensive search for literature published since the 2012 guideline, derived from research principally involving human subjects, published in English, and indexed in MEDLINE, PubMed, Cochrane Library, and other selected databases relevant to this guideline, was conducted between March 2022 and June 2022. In addition, the guideline writing group reviewed documents on related subject matter previously published by the American Heart Association. Newer studies published between July 2022 and November 2022 that affected recommendation content, Class of Recommendation, or Level of Evidence were included if appropriate. Structure: Aneurysmal subarachnoid hemorrhage is a significant global public health threat and a severely morbid and often deadly condition. The 2023 aneurysmal subarachnoid hemorrhage guideline provides recommendations based on current evidence for the treatment of these patients. The recommendations present an evidence-based approach to preventing, diagnosing, and managing patients with aneurysmal subarachnoid hemorrhage, with the intent to improve quality of care and align with patients' and their families' and caregivers' interests. Many recommendations from the previous aneurysmal subarachnoid hemorrhage guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.

Precision Medicine in Neurocritical Care for Cerebrovascular Disease Cases

Scientific advances have informed many aspects of acute stroke care but have also highlighted the complexity and heterogeneity of cerebrovascular diseases. While practice guidelines are essential in supporting the clinical decision-making process, they may not capture the nuances of individual cases. Personalized stroke care in ICU has traditionally relied on integrating clinical examinations, neuroimaging studies, and physiologic monitoring to develop a treatment plan tailored to the individual patient. However, to realize the potential of precision medicine in stroke, we need advances and evidence in several critical areas, including data capture, clinical phenotyping, serum biomarker development, neuromonitoring, and physiology-based treatment targets. Mathematical tools are being developed to analyze the multitude of data and provide clinicians with real-time information and personalized treatment targets for the critical care management of patients with cerebrovascular diseases. This review summarizes research advances in these areas and outlines principles for translating precision medicine into clinical practice.

The Spectrum of Quantitative EEG Utilization Across North America: A Cross-Sectional Survey

BACKGROUND

Continuous electroencephalography (cEEG) is commonly used for neuromonitoring in pediatric intensive care units (PICU); however, there are barriers to real-time interpretation of EEG data. Quantitative EEG (qEEG) transforms the EEG signal into time-compressed graphs, which can be displayed at the bedside. A survey was designed to understand current PICU qEEG use.

METHODS

An electronic survey was sent to the Pediatric Neurocritical Care Research Group and Pediatric Status Epilepticus Research Group, and intensivists in 16 Canadian PICUs. Questions addressed demographics, qEEG acquisition and storage, clinical use, and education.

RESULTS

Fifty respondents from 39 institutions completed the survey (response rate 53% [39 of 74 institutions]), 76% (37 of 50) from the United States and 24% (12 of 50) from Canada. Over half of the institutions (22 of 39 [56%]) utilize qEEG in their ICUs. qEEG use was associated with having a neurocritical care (NCC) service, ≥200 NCC consults/year, ≥1500 ICU admissions/year, and ≥4 ICU EEGs/day (P < 0.05 for all). Nearly all users (92% [24 of 26]) endorsed that qEEG enhanced care of children with acute neurological injury. Lack of training in qEEG was identified as a common barrier [85% (22 of 26)]. Reviewing and reporting of qEEG was not standard at most institutions. Training was required by 14% (three of 22) of institutions, and 32% (seven of 22) had established curricula.

CONCLUSIONS

ICU qEEG was used at more than half of the institutions surveyed, but review, reporting, and application of this tool remained highly variable. Although providers identify qEEG as a useful tool in patient management, further studies are needed to define clinically meaningful pediatric trends, standardize reporting, and enhance educate bedside providers.

Bispectral index monitoring to detect delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

PURPOSE

Evaluate the bispectral index (BIS) monitoring to detect delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH).

MATERIALS AND METHODS

A single-center prospective study in patients with aSAH. BIS monitoring was recorded during 25-120 min in two periods, within the initial 72 h (BIS1) and between days 4 and 6 (BIS2) from admission. The median for each exported BIS parameter was analyzed. Transcranial Doppler (TCD) sonography was simultaneously performed with BIS1 (TCD1) and BIS2 (TCD2) monitoring. A multivariate logistic regression model was built to identify the variables associated with DCI.

RESULTS

Sixty-four patients were included and 16 (25%) developed DCI. During BIS2 monitoring, significant differences were found in BIS value (left, p = 0.01; right, p = 0.009), 95% spectral edge frequency (left and right, p = 0.04), and total power (left and right, p = 0.04). In multivariable analysis, vasospasm on TCD2 (OR 42.8 [95% CI 3.1-573]; p = 0.005), a median BIS2 value <85 in one or both sides (OR 6.2 [95% CI 1.28-30]; p = 0.023), and age (OR 1.08 [95% CI 1.00-1.17]; p = 0.04) were associated with the development of DCI.

CONCLUSIONS

BIS value is the most useful BIS parameter for detecting DCI after aSAH. Pending further validation, BIS monitoring might be even more accurate than TCD.

Automated EEG-based prediction of delayed cerebral ischemia after subarachnoid hemorrhage

OBJECTIVE

Delayed cerebral ischemia (DCI) is a leading complication of aneurysmal subarachnoid hemorrhage (SAH) and electroencephalography (EEG) is increasingly used to evaluate DCI risk. Our goal is to develop an automated DCI prediction algorithm integrating multiple EEG features over time.

METHODS

We assess 113 moderate to severe grade SAH patients to develop a machine learning model that predicts DCI risk using multiple EEG features.

RESULTS

Multiple EEG features discriminate between DCI and non-DCI patients when aligned either to SAH time or to DCI onset. DCI and non-DCI patients have significant differences in alpha-delta ratio (0.08 vs 0.05, p < 0.05) and percent alpha variability (0.06 vs 0.04, p < 0.05), Shannon entropy (p < 0.05) and epileptiform discharge burden (205 vs 91 discharges per hour, p < 0.05) based on whole brain and vascular territory averaging. Our model improves predictions by emphasizing the most informative features at a given time with an area under the receiver-operator curve of 0.73, by day 5 after SAH and good calibration between 48-72 hours (calibration error 0.13).

CONCLUSIONS

Our proposed model obtains good performance in DCI prediction.

SIGNIFICANCE

We leverage machine learning to enable rapid, automated, multi-featured EEG assessment and has the potential to increase the utility of EEG for DCI prediction.

Closing the Gap in Pediatric Hemorrhagic Stroke: A Systematic Review

Pediatric hemorrhagic stroke (HS) accounts for a large proportion of childhood strokes, 1 of the top 10 causes of pediatric deaths. Morbidity and mortality lead to significant socio-economic and psychosocial burdens. To understand published data on recognizing and managing children with HS, we conducted a systematic review of the literature presented here. We searched PubMed, Embase, CINAHL and the Cochrane Library databases limited to English language and included 174 studies, most conducted in the USA (52%). Terminology used interchangeably for HS included intraparenchymal/intracranial hemorrhage, spontaneous ICH, and cerebrovascular accident (CVA). Key assessments informing prognosis and management included clinical scoring (Glasgow coma scale), and neuroimaging. HS etiologies reported were systemic coagulopathy (genetic, acquired pathologic, or iatrogenic), or focal cerebrovascular lesions (brain arteriovenous malformations, cavernous malformations, aneurysms, or tumor vascularity). Several scales were used to measure outcome: Glasgow outcome score (GOS), Kings outcome score for head injury (KOSCHI), modified Rankin scale (mRS) and pediatric stroke outcome measure (PSOM). Most studies described treatments of at-risk lesions. Few studies described neurocritical care management including raised ICP, seizures, vasospasm, or blood pressure. Predictors of poor outcome included ethnicity, comorbidity, location of bleed, and hematoma &gt;2% of total brain volume. Motor and cognitive outcomes followed independent patterns. Few studies reported on cognitive outcomes, rehabilitation, and transition of care models. Interdisciplinary approach to managing HS is urgently needed, informed by larger cohort studies targeting key clinical question (eg development of a field-guide for the clinician managing patients with HS that is reproducible internationally).

Electroencephalogram in the intensive care unit: a focused look at acute brain injury

Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.

High epileptiform discharge burden predicts delayed cerebral ischemia after subarachnoid hemorrhage

OBJECTIVE

To investigate whether epileptiform discharge burden can identify those at risk for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH).

METHODS

Retrospective analysis of 113 moderate to severe grade SAH patients who had continuous EEG (cEEG) recordings during their hospitalization. We calculated the burden of epileptiform discharges (ED), measured as number of ED per hour.

RESULTS

We find that many SAH patients have an increase in ED burden during the first 3-10 days following rupture, the major risk period for DCI. However, those who develop DCI have a significantly higher hourly burden from days 3.5-6 after SAH vs. those who do not. ED burden is higher in DCI patients when assessed in relation to the onset of DCI (area under the receiver operator curve 0.72). Finally, specific trends of ED burden over time, assessed by group-based trajectory analysis, also help stratify DCI risk.

CONCLUSIONS

These results suggest that ED burden is a useful parameter for identifying those at higher risk of developing DCI after SAH. The higher burden rate associated with DCI supports the theory of metabolic supply-demand mismatch which contributes to this complication.

SIGNIFICANCE

ED burden is a novel biomarker for predicting those at high risk of DCI.

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

Background

Better outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.

Methods

We retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.

Results

A total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.

Conclusion

Multifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU.

Delayed cerebral ischemia: A look at the role of endothelial dysfunction, emerging endovascular management, and glymphatic clearance

Delayed cerebral ischemia (DCI) contributes to extensive morbidity and mortality for patients with aneurysmal subarachnoid hemorrhage (SAH). Recent contributions to the basic and translational investigation of DCI have shed light on emerging concepts that may aid in the development of novel therapeutics. A clear association between cerebral vasospasm (CV) and DCI exists, but it is also known that DCI can affect brain parenchyma remote from sites of vasospasm. In this review, we highlight the most recent contributions to the understanding of the underlying pathophysiology of DCI including the emerging role of the glymphatic system. Furthermore, we discuss treatments for DCI, including both pharmacologic therapies and endovascular treatment of vasospasm. There continues to be a disconnect between interventions and targeted treatment against pathophysiology. This review is intended to serve as a catalyst for further research and discovery that can aid in improved treatment options for DCI.

Quantitative EEG may predict weaning failure in ventilated patients on the neurological intensive care unit

Neurocritical patients suffer from a substantial risk of extubation failure. The aim of this prospective study was to analyze if quantitative EEG (qEEG) monitoring is able to predict successful extubation in these patients. We analyzed EEG-monitoring for at least six hours before extubation in patients receiving mechanical ventilation (MV) on our neurological intensive care unit (NICU) between November 2017 and May 2019. Patients were divided in 2 groups: patients with successful extubation (SE) versus patients with complications after MV withdrawal (failed extubation; FE), including reintubation, need for non-invasive ventilation (NIV) or death. Bipolar six channel EEG was applied. Unselected raw EEG signal underwent automated artefact rejection and Short Time Fast Fourier Transformation. The following relative proportions of global EEG spectrum were analyzed: relative beta (RB), alpha (RA), theta (RT), delta (RD) as well as the alpha delta ratio (ADR). Coefficient of variation (CV) was calculated as a measure of fluctuations in the different power bands. Mann-Whitney U test and logistic regression were applied to analyze group differences. 52 patients were included (26 male, mean age 65 ± 17 years, diagnosis: 40% seizures/status epilepticus, 37% ischemia, 13% intracranial hemorrhage, 10% others). Successful extubation was possible in 40 patients (77%), reintubation was necessary in 6 patients (12%), 5 patients (10%) required NIV, one patient died. In contrast to FE patients, SE patients showed more stable EEG power values (lower CV) considering all EEG channels (RB: p < 0.0005; RA: p = 0.045; RT: p = 0.045) with RB as an independent predictor of weaning success in logistic regression (p = 0.004). The proportion of the EEG frequency bands (RB, RA RT, RD) of the entire EEG power spectrum was not significantly different between SE and FE patients. Higher fluctuations in qEEG frequency bands, reflecting greater fluctuation in alertness, during the hours before cessation of MV were associated with a higher rate of complications after extubation in this cohort. The stability of qEEG power values may represent a non-invasive, examiner-independent parameter to facilitate weaning assessment in neurocritical patients.

Neuroelectric Mechanisms of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) remains a challenging but very important condition, because DCI is preventable and treatable for improving functional outcomes after aneurysmal subarachnoid hemorrhage (SAH). The pathologies underlying DCI are multifactorial. Classical approaches to DCI focus exclusively on preventing and treating the reduction of blood flow supply. However, recently, glutamate-mediated neuroelectric disruptions, such as excitotoxicity, cortical spreading depolarization and seizures, and epileptiform discharges, have been reported to occur in high frequencies in association with DCI development after SAH. Each of the neuroelectric disruptions can trigger the other, which augments metabolic demand. If increased metabolic demand exceeds the impaired blood supply, the mismatch leads to relative ischemia, resulting in DCI. The neuroelectric disruption also induces inverted vasoconstrictive neurovascular coupling in compromised brain tissues after SAH, causing DCI. Although glutamates and the receptors may play central roles in the development of excitotoxicity, cortical spreading ischemia and epileptic activity-related events, more studies are needed to clarify the pathophysiology and to develop novel therapeutic strategies for preventing or treating neuroelectric disruption-related DCI after SAH. This article reviews the recent advancement in research on neuroelectric disruption after SAH.

The Utility of Quantitative EEG in Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

SUMMARY

In this review, we discuss the utility of quantitative EEG parameters for the detection of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage in the context of the complex pathophysiology of DCI and the limitations of current diagnostic methods. Because of the multifactorial pathophysiology of DCI, methodologies solely assessing blood vessel narrowing (vasospasm) are insufficient to detect all DCI. Quantitative EEG has facilitated the exploration of EEG as a diagnostic modality of DCI. Multiple quantitative EEG parameters such as alpha power, relative alpha variability, and alpha/delta ratio show reliable detection of DCI in multiple studies. Recent studies on epileptiform abnormalities suggest that their potential for the detection of DCI. Quantitative EEG is a promising, continuous, noninvasive, monitoring modality of DCI implementable in daily practice. Future work should validate these parameters in larger populations, facilitated by the development of automated detection algorithms and multimodal data integration.

Electroencephalography for detection of vasospasm and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a retrospective analysis and systematic review

OBJECTIVE

Good functional outcomes after aneurysmal subarachnoid hemorrhage (aSAH) are often dependent on early detection and treatment of cerebral vasospasm (CVS) and delayed cerebral ischemia (DCI). There is growing evidence that continuous monitoring with cranial electroencephalography (cEEG) can predict CVS and DCI. Therefore, the authors sought to assess the value of continuous cEEG monitoring for the detection of CVS and DCI in aSAH.

METHODS

The cerebrovascular database of a quaternary center was reviewed for patients with aSAH and cEEG monitoring between January 1, 2017, and July 31, 2019. Demographic data, cardiovascular risk factors, Glasgow Coma Scale score at admission, aneurysm characteristics, and outcomes were abstracted from the medical record. Patient data were retrospectively analyzed for DCI and angiographically assessed CVS. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and odds ratio for cEEG, transcranial Doppler ultrasonography (TCDS), CTA, and DSA in detecting DCI and angiographic CVS were calculated. A systematic literature review was conducted in accordance with PRISMA guidelines querying the PubMed, Cochrane Controlled Trials Register, Web of Science, and Embase databases.

RESULTS

A total of 77 patients (mean age 60 years [SD 15 years]; female sex, n = 54) were included in the study. Continuous cEEG monitoring detected DCI and angiographically assessed CVS with specificities of 82.9% (95% CI 66.4%–93.4%) and 94.4% (95% CI 72.7%–99.9%), respectively. The sensitivities were 11.1% (95% CI 3.1%–26.1%) for DCI (n = 71) and 18.8% (95% CI 7.2%–36.4%) for angiographically assessed CVS (n = 50). Furthermore, TCDS detected angiographically determined CVS with a sensitivity of 87.5% (95% CI 71.0%–96.5%) and specificity of 25.0% (95% CI 7.3%–52.4%). In patients with DCI, TCDS detected vasospasm with a sensitivity of 85.7% (95% CI 69.7%–95.2%) and a specificity of 18.8% (95% CI 7.2%–36.4%). DSA detected vasospasm with a sensitivity of 73.9% (95% CI 51.6%–89.8%) and a specificity of 47.8% (95% CI 26.8%–69.4%).

CONCLUSIONS

The study results suggest that continuous cEEG monitoring is highly specific in detecting DCI as well as angiographically assessed CVS. More prospective studies with predetermined thresholds and endpoints are needed to assess the predictive role of cEEG in aSAH.

Combining Transcranial Doppler and EEG Data to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage

BACKGROUND AND OBJECTIVES

Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction.

METHODS

We retrospectively assessed patients with moderate to severe SAH (2011-2015; Fisher 3-4 or Hunt-Hess 4-5) who had both prospective TCD and EEG acquisition during hospitalization. Middle cerebral artery (MCA) peak systolic velocities (PSVs) and the presence or absence of epileptiform abnormalities (EAs), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify significant covariates of EAs and TCD to predict DCI. Group-based trajectory modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA PSV and EAs associated with DCI risk.

RESULTS

We assessed 107 patients; DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV ≥200 cm/s, sensitivity 27%, specificity 89%) and EAs (sensitivity 66%, specificity 62%) on or before day 3. Two univariate GBTM trajectories of EAs predicted DCI (sensitivity 64%, specificity 62.75%). Logistic regression and GBTM models using both TCD and EEG monitoring performed better. The best logistic regression and GBTM models used both TCD and EEG data, Hunt-Hess score at admission, and aneurysm treatment as predictors of DCI (logistic regression: sensitivity 90%, specificity 70%; GBTM: sensitivity 89%, specificity 67%).

DISCUSSION

EEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EAs and high MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after SAH.

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

Seizures, Status Epilepticus, and Continuous EEG in the Intensive Care Unit

PURPOSE OF REVIEW

This article discusses the evolving definitions of seizures and status epilepticus in the critical care environment and the role of critical care EEG in both diagnosing seizure activity and serving as a predictive biomarker of clinical trajectory.

RECENT FINDINGS

Initial screening EEG has been validated as a tool to predict which patients are at risk of future seizures. However, accepted definitions of seizures and nonconvulsive status epilepticus encourage a treatment trial when the diagnosis on EEG is indeterminate because of periodic or rhythmic patterns or uncertain clinical correlation. Similarly, recent data have demonstrated the diagnostic utility of intracranial EEG in increasing the yield of seizure detection. EEG has additionally been validated as a diagnostic biomarker of covert consciousness, a predictive biomarker of cerebral ischemia and impending neurologic deterioration, and a prognostic biomarker of coma recovery and status epilepticus resolution. A recent randomized trial concluded that patients allocated to continuous EEG had no difference in mortality than those undergoing intermittent EEG but could not demonstrate whether this lack of difference was because of studying heterogeneous conditions, examining a monitoring tool rather than a therapeutic approach, or examining an outcome measure (mortality) perhaps more strongly associated with early withdrawal of life-sustaining therapy than to a sustained response to pharmacotherapy.

SUMMARY

Seizures and status epilepticus are events of synchronous hypermetabolic activity that are either discrete and intermittent or, alternatively, continuous. Seizures and status epilepticus represent the far end of a continuum of ictal-interictal patterns that include lateralized rhythmic delta activity and periodic discharges, which not only predict future seizures but may be further classified as status epilepticus on the basis of intracranial EEG monitoring or a diagnostic trial of antiseizure medication therapy. In particularly challenging cases, neuroimaging or multimodality neuromonitoring may be a useful adjunct documenting metabolic crisis. Specialized uses of EEG as a prognostic biomarker have emerged in traumatic brain injury for predicting language function and covert consciousness, cardiac arrest for predicting coma recovery, and subarachnoid hemorrhage for predicting neurologic deterioration due to delayed cerebral ischemia.

Beneficial Effect of Sodium Nitrite on EEG Ischaemic Markers in Patients with Subarachnoid Haemorrhage

Subarachnoid haemorrhage (SAH) is associated with long-term disability, serious reduction in quality of life and significant mortality. Early brain injury (EBI) refers to the pathological changes in cerebral metabolism and blood flow that happen in the first few days after ictus and may lead on to delayed cerebral ischaemia (DCI). A disruption of the nitric oxide (NO) pathway is hypothesised as a key mechanism underlying EBI. A decrease in the alpha-delta power ratio (ADR) of the electroencephalogram has been related to cerebral ischaemia. In an experimental medicine study, we tested the hypothesis that intravenous sodium nitrite, an NO donor, would lead to increases in ADR. We studied 33 patients with acute aneurysmal SAH in the EBI phase. Participants were randomised to either sodium nitrite or saline infusion for 1 h. EEG measurements were taken before the start of and during the infusion. Twenty-eight patients did not develop DCI and five patients developed DCI. In the patients who did not develop DCI, we found an increase in ADR during sodium nitrite versus saline infusion. In the five patients who developed DCI, we did not observe a consistent pattern of ADR changes. We suggest that ADR power changes in response to nitrite infusion reflect a NO-mediated reduction in cerebral ischaemia and increase in perfusion, adding further evidence to the role of the NO pathway in EBI after SAH. Our findings provide the basis for future clinical trials employing NO donors after SAH.

EEG in the Pediatric Intensive Care Unit: An Irish Experience

INTRODUCTION

Evidence for continuous EEG monitoring in the pediatric intensive care unit (PICU) is increasing. However, 24/7 access to EEG is not routinely available in most centers, and clinical management is often informed by more limited EEG resources. The experience of EEG was reviewed in a tertiary PICU where 24/7 EEG cover is unavailable.

METHODS

Retrospective EEG and clinical review of 108 PICU patients. Correlations were carried out between EEG and clinical variables including mortality. The role of EEG in clinical decision making was documented.

RESULTS

One hundred ninety-six EEGs were carried out in 108 PICU patients over 2.5 years (434 hours of recording). After exclusion of 1 outlying patient with epileptic encephalopathy, 136 EEGs (median duration, 65 minutes; range, 20 minutes to 4 hours 40 minutes) were included. Sixty-two patients (57%) were less than 12 months old. Seizures were detected in 18 of 107 patients (17%); 74% of seizures were subclinical; 72% occurred within the first 30 minutes of recording. Adverse EEG findings were associated with high mortality. Antiepileptic drug use was high in the studied population irrespective of EEG seizure detection. Prevalence of epileptiform discharges and EEG seizures diminished with increasing levels of sedation.

CONCLUSIONS

EEG provides important diagnostic information in a large proportion of PICU patients. In the absence of 24/7 EEG availability, empirical antiepileptic drug utilization is high.

Quantitative EEG Changes Correlate With Post-Clamp Ischemia During Carotid Endarterectomy

INTRODUCTION

EEG monitoring is a critical tool for identifying cerebral ischemia during carotid endarterectomy (CEA). Quantitative EEG can be used to supplement visual EEG review, but which measures best predict post-clamp ischemia is unclear.

PURPOSE

To determine which quantitative EEG parameters reliably detect intraoperative ischemia during CEA.

METHODS

The authors identified patients who underwent carotid endarterectomy at Columbia University Medical Center from 2007 to 2014 with intraoperative EEG monitoring. Two masked physicians reviewed these EEGs retrospectively and determined whether there was post-clamp ischemia, categorizing patients into (1) ischemic-change and (2) no-ischemic-change groups. The authors then studied the performance of a battery of quantitative EEG measures (alpha, beta, theta, and delta power bands, alpha-delta ratio, beta-delta ratio, amplitude-integrated EEG, and 90% spectral edge frequency) against physician review as the gold standard.

RESULTS

Of 118 patients, 15 were included in the ischemic-change group and 103 in the no-ischemic-change group. Ipsilateral post-clamp trough values of all the quantitative EEG measures assessed were significantly decreased for patients in the ischemic-change group. Decreases in alpha, beta, and theta power of 52.1%, 41.6%, and 36.4% or greater, respectively, were most predictive of post-clamp ischemia.

CONCLUSIONS

Quantitative EEG monitoring during carotid endarterectomy, in addition to visual EEG monitoring, may improve the detection of cerebral ischemia and thus result in fewer perioperative strokes.

Alpha power decrease in quantitative EEG detects development of cerebral infarction after subarachnoid hemorrhage early

OBJECTIVE

In subarachnoid hemorrhage (SAH), transcranial Doppler/color-coded-duplex sonography (TCD/TCCS) is used to detect delayed cerebral ischemia (DCI). In previous studies, quantitative electroencephalography (qEEG) also predicted imminent DCI. This study aimed to compare and analyse the ability of qEEG and TCD/TCCS to early identify patients who will develop later manifest cerebral infarction.

METHODS

We analysed cohorts of two previous qEEG studies. Continuous six-channel-EEG with artefact rejection and a detrending procedure was applied. Alpha power decline of ≥ 40% for ≥ 5 hours compared to a 6-hour-baseline was defined as significant EEG event. Median reduction and duration of alpha power decrease in each channel was determined. Vasospasm was diagnosed by TCD/TCCS, identifying the maximum frequency and days of vasospasm in each territory.

RESULTS

34 patients were included (17 male, mean age 56 ± 11 years, Hunt and Hess grade: I-V, cerebral infarction: 9). Maximum frequencies in TCD/TCCS and alpha power reduction in qEEG were correlated (r = 0.43; p = 0.015). Patients with and without infarction significantly differed in qEEG parameters (maximum alpha power decrease: 78% vs 64%, p = 0.019; summed hours of alpha power decline: 236 hours vs 39 hours, p = 0.006) but showed no significant differences in TCD/TCCS parameters.

CONCLUSIONS

There was a moderate correlation of TCD/TCCS frequencies and qEEG alpha power reduction but only qEEG differentiated between patients with and without cerebral infarction.

SIGNIFICANCE

qEEG represents a non-invasive, continuous tool to identify patients at risk of cerebral infarction.

An overview of pharmacotherapy for cerebral vasospasm and delayed cerebral ischemia after subarachnoid hemorrhage

Introduction: Survival from aneurysmal subarachnoid hemorrhage has increased in the past few decades. However, functional outcome after subarachnoid hemorrhage is still suboptimal. Delayed cerebral ischemia (DCI) is one of the major causes of morbidity.Areas covered: Mechanisms underlying vasospasm and DCI after aneurysmal subarachnoid hemorrhage and pharmacological treatment are summarized in this review.Expert opinion: Oral nimodine, an L-type dihydropyridine calcium channel blocker, is the only FDA-approved drug for the prevention and treatment of neurological deficits after aneurysmal subarachnoid hemorrhage. Fasudil, a potent Rho-kinase inhibitor, has also been shown to improve the clinical outcome and has been approved in some countries for use in patients with aneurysmal subarachnoid hemorrhage. Although other drugs, including nicardipine, cilostazol, statins, clazosentan, magnesium and heparin, have been expected to have beneficial effects on DCI, there has been no convincing evidence supporting the routine use of those drugs in patients with aneurysmal subarachnoid hemorrhage in clinical practice. Further elucidation of the mechanisms underlying DCI and the development of effective therapeutic strategies for DCI, including combination therapy, are necessary to further improve the functional outcome and mortality after subarachnoid hemorrhage.

Blunt and Penetrating Severe Traumatic Brain Injury

Severe traumatic brain injury is a common problem. Current practices focus on the importance of early resuscitation, transfer to high-volume centers, and provider expertise across multiple specialties. In the emergency department, patients should receive urgent intracranial imaging and consideration for tranexamic acid. Close observation in the intensive care unit environment helps identify problems, such as seizure, intracranial pressure crisis, and injury progression. In addition to traditional neurologic examination, patients benefit from use of intracranial monitors. Monitors gather physiologic data on intracranial and cerebral perfusion pressures to help guide therapy. Brain tissue oxygenation monitoring and cerebromicrodialysis show promise in studies.

Continuous heart rate variability and electroencephalography monitoring in severe acute brain injury: a preliminary study

Background

Decreases in heart rate variability have been shown to be associated with poor outcomes in severe acute brain injury. However, it is unknown whether the changes in heart rate variability precede neurological deterioration in such patients. We explored the changes in heart rate variability measured by electrocardiography in patients who had neurological deterioration following severe acute brain injury, and examined the relationship between heart rate variability and electroencephalography parameters.

Methods

Retrospective analysis of 25 patients who manifested neurological deterioration after severe acute brain injury and underwent simultaneous electroencephalography plus electrocardiography monitoring.

Results

Eighteen electroencephalography channels and one simultaneously recorded electrocardiography channel were segmented into epochs of 120-second duration and processed to compute 10 heart rate variability parameters and three quantitative electroencephalography parameters. Raw electroencephalography of the epochs was also assessed by standardized visual interpretation and categorized based on their background abnormalities and ictalinterictal continuum patterns. The heart rate variability and electroencephalography parameters showed consistent changes in the 2-day period before neurological deterioration commenced. Remarkably, the suppression ratio and background abnormality of the electroencephalography parameters had significant reverse correlations with all heart rate variability parameters.

Conclusions

We observed a significantly progressive decline in heart rate variability from the day before the neurological deterioration events in patients with severe acute brain injury were first observed.

Cerebral ischemia detection using artificial intelligence (CIDAI)-A study protocol

BACKGROUND

The onset of cerebral ischemia is difficult to predict in patients with altered consciousness using the methods available. We hypothesize that changes in Heart Rate Variability (HRV), Near-Infrared Spectroscopy (NIRS), and Electroencephalography (EEG) correlated with clinical data and processed by artificial intelligence (AI) can indicate the development of imminent cerebral ischemia and reperfusion, respectively. This study aimed to develop a method that enables detection of imminent cerebral ischemia in unconscious patients, noninvasively and with the support of AI.

METHODS

This prospective observational study will include patients undergoing elective surgery for carotid endarterectomy and patients undergoing acute endovascular embolectomy for cerebral arterial embolism. HRV, NIRS, and EEG measurements and clinical information on patient status will be collected and processed using machine learning. The study will take place at Sahlgrenska University Hospital, Gothenburg, Sweden. Inclusion will start in September 2020, and patients will be included until a robust model can be constructed. By analyzing changes in HRV, EEG, and NIRS measurements in conjunction with cerebral ischemia or cerebral reperfusion, it should be possible to train artificial neural networks to detect patterns of impending cerebral ischemia. The analysis will be performed using machine learning with long short-term memory artificial neural networks combined with convolutional layers to identify patterns consistent with cerebral ischemia and reperfusion.

DISCUSSION

Early signs of cerebral ischemia could be detected more rapidly by identifying patterns in integrated, continuously collected physiological data processed by AI. Clinicians could then be alerted, and appropriate actions could be taken to improve patient outcomes.

Quantitative EEG After Subarachnoid Hemorrhage Predicts Long-Term Functional Outcome

PURPOSE

Delayed cerebral ischemia is a major complication after subarachnoid hemorrhage. Our previous study showed that alpha power reduction in continuous quantitative EEG predicts delayed cerebral ischemia. In this prospective cohort, we aimed to determine the prognostic value of alpha power in quantitative EEG for the long-term outcome of patients with subarachnoid hemorrhage.

METHODS

Adult patients with nontraumatic subarachnoid hemorrhage were included if admitted early enough for EEG to start within 72 hours after symptom onset. Continuous six-channel EEG was applied. Unselected EEG signals underwent automated artifact rejection, power spectral analysis, and detrending. Alpha power decline of ≥40% for ≥5 hours was defined as critical EEG event based on previous findings. Six-month outcome was obtained using the modified Rankin scale.

RESULTS

Twenty-two patients were included (14 male; mean age, 59 years; Hunt and Hess grade I-IV; duration of EEG monitoring, median 14 days). Poor outcome (modified Rankin scale, 2-5) was noted in 11 of 16 patients (69%) with critical EEG events. All six patients (100%) without EEG events achieved an excellent outcome (modified Rankin scale 0, 1) (P = 0.0062; sensitivity 100%, specificity 54.5%). Vasospasm detected with transcranial Doppler/Duplex sonography appeared 1.5 days after EEG events and showed weaker association with outcome (P = 0.035; sensitivity 100%, specificity 45.5%). There was no significant association between EEG events and ischemic lesions on imaging (P = 0.1). Also, no association between ischemic lesions and outcome was seen (P = 0.64).

CONCLUSIONS

Stable alpha power in quantitative EEG reflects successful therapy and predicts good functional outcome after subarachnoid hemorrhage. Critical alpha power reduction indicates an increased risk of poor functional outcome.

Predictive Value of Cerebral Autoregulation Impairment for Delayed Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage: A Meta-Analysis

OBJECTIVE

Delayed cerebral ischemia (DCI) happens in about 30% of patients with aneurysmal subarachnoid hemorrhage (SAH) and is related to higher mortality and disability. Some studies have shown cerebral autoregulation impairment can be a predictor of DCI in aneurysmal SAH. We conducted this meta-analysis to evaluate the predictive value of cerebral autoregulation impairment for DCI based on the current literature.

METHODS

A systematic literature search was performed in PubMed and Embase. According to inclusion and exclusion criteria, 2 authors screened the records and extracted data from the included studies. Pooled sensitivity, specificity, and their 95% confidence intervals (CIs) were obtained. To investigate the overall accuracy, a summary receiver operating characteristic (SROC) curve was built and the area under SROC curve was calculated. Deeks' linear regression was used to assess the publication bias. All statistical analyses were performed with Stata 14.0.

RESULTS

A total of 7 studies were finally included in this meta-analysis. The pooled sensitivity and specificity values of impaired cerebral autoregulation for DCI prediction were 0.79 (95% CI, 0.65-0.88) and 0.85 (95% CI, 0.615-0.96). Moreover, the area under the SROC curve of cerebral autoregulation impairment for DCI prediction was 0.87 (95% CI, 0.835-0.89). No obvious publication bias was found in Deeks' linear regression (P = 0.99).

CONCLUSIONS

Cerebral autoregulation impairment can be a helpful predictor of DCI in aneurysmal SAH. Its accuracy for DCI prediction should be verified by more studies in the future.

Predictive Accuracy of Alpha-Delta Ratio on Quantitative Electroencephalography for Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage: Meta-Analysis

OBJECTIVE

Delayed cerebral ischemia (DCI) is significantly related to death and unfavorable functional outcome in patients with aneurysmal subarachnoid hemorrhage (SAH). The association between alpha-delta ratio (ADR) on quantitative electroencephalography (EEG) and DCI has been reported in several previous studies, but their results are conflicting. This meta-analysis was conducted to assess the accuracy of ADR for DCI prediction in patients with aneurysmal SAH.

METHODS

PubMed and Embase were systematically searched for related records. Study selection and data collection were completed by 2 investigators. Sensitivity, specificity, and their 95% confidence intervals (CIs) were pooled. A summary receiver operating characteristic curve was plotted to show the pooled accuracy. Deeks funnel plot was used to evaluate publication bias.

RESULTS

Five studies were included in this meta-analysis. The pooled sensitivity and specificity of worsening ADR for DCI prediction in patients with aneurysmal SAH were 0.83 (95% CI 0.44-0.97) and 0.74 (95% CI 0.50-0.89), respectively. In addition, the area under the summary receiver operating characteristic curve was 0.84 (95% CI 0.81-0.87). No obvious publication bias was found using Deeks funnel plot (P = 0.29).

CONCLUSIONS

Worsening ADR on quantitative EEG is a reliable predictor of DCI in patients with aneurysmal SAH. Further studies are still needed to confirm the role of quantitative EEG in DCI prediction.

Vasospasm following aneurysmal subarachnoid hemorrhage: prediction, detection, and intervention

Background

Vasospasm of the cerebral blood vessels is a common complication of aneurysmal subarachnoid hemorrhage (aSAH) which results in delayed cerebral ischemia (DCI) and worsening of the outcome.

Methods

This study was performed on 41 aSAH patients diagnosed by non-contrast brain CT, CT angiography, and digital subtraction angiography followed by interventional aneurysmal embolization. Patients were followed up for 20 days by clinical assessment, EEG monitoring, and transcranial duplex studies (TCD) for early detection of vasospasm and DCI.

Results

The most common ruptured aneurysmal sites were middle cerebral, anterior communicating, posterior communicating, terminal internal carotid, and anterior cerebral arteries respectively. The incidence of vasospasm was 36.8% of the included cases; 57% progressed to DCI while 43% passed a spontaneous regressive course. The most common arteries undergoing vasospasm were the MCA followed by the ACA, ICA, and lastly the basilar arteries. The mean time of vasospasm development as detected by EEG monitoring and/or TCD was 8.4 ± 2.8 days which was earlier than clinical signs by 12.5 ± 5.3 h in those progressed to DCI.

Conclusion

Continuous EEG monitoring and TCD are valuable methods for early detection of vasospasm and they allow for early therapeutic intervention before irreversible ischemic neurological deficits take place.

Brain Injury During Transition in the Newborn With Congenital Heart Disease: Hazards of the Preoperative Period

Infants born with critical congenital heart disease are at risk for neurodevelopmental morbidities later in life. In-utero differences in fetal circulation lead to vulnerabilities which lead to an increased incidence of stroke, white matter injury, and brain immaturity. Recent work has shown these infants may be most vulnerable to brain injury during the early neonatal period when they are awaiting their cardiac surgeries. Novel imaging and monitoring modalities are being employed to investigate this crucial time period and elucidate the precise timing and cause of brain injury in this population.

Spectral and Multifractal Signature of Cortical Spreading Depolarisation in Aged Rats

Cortical spreading depolarisation (CSD) is a transient disruption of ion balance that propagates along the cortex. It has been identified as an important factor in the progression of cerebral damage associated with stroke or traumatic brain injury. We analysed local field potential signals during CSD in old and young rats to look for age-related differences. We compared CSDs elicited under physiological conditions (baseline), during ischaemia and during reperfusion. We applied short-time Fourier transform and a windowed implementation of multifractal detrended fluctuation analysis to follow the electrophysiological signature of CSD. Both in the time-dependent spectral profiles and in the multifractal spectrum width, CSDs appeared as transient dips, which we described on the basis of their duration, depression and recovery slope and degree of drop and rise. The most significant age-related difference we found was in the depression slope, which was significantly more negative in the beta band and less negative in the delta band of old animals. In several parameters, we observed an attenuation-regeneration pattern in reaction to ischaemia and reperfusion, which was absent in the old age group. The age-related deviation from the pattern took two forms: the rise parameter did not show any attenuation in ischaemic conditions for old animals, whilst the depression slope in most frequency bands remained attenuated during reperfusion and did not regenerate in this age group. Though the multifractal spectrum width proved to be a reliable indicator of events like CSDs or ischaemia onset, we failed to find any case where it would add extra detail to the information provided by the Fourier description.

Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: A preliminary study

OBJECTIVE

Delayed cerebral ischemia (DCI) is the most important and preventable morbidity cause after subarachnoid hemorrhage (SAH). Therefore, DCI early detection is a major challenge. Yet, neurological examination can be unreliable in poor grade SAH patients. EEG provides information from most superficial cortical area, with ischemia-related changes. This study aims at defining an alpha-theta/delta (AT/D) ratio decrease thresholds to detect DCI.

METHODS

We used EEG with a montage matching vascular territories (right and left anterior central and posterior) and compared them to follow-up brain imaging.

RESULTS

15 SAH patients (Fischer ≥ 3, World Federation of Neurological Surgeons scale ≥4, 9 DCI) were monitored during 6.4 [4-8] days (min = 2d, max = 13d). AT/D changes could follow three different patterns: (1) prolonged or (2) transient decrease and (3) no decrease or progressive increase. A regional 30% decrease outlasting 3.7 h reached 100% sensitivity and 88.9% specificity to detect DCI. Only 22.6% were in a zone of uncertain diagnosis (3.7-8.04 h). These prolonged decreases, with a loss of transient changes, started in cortical areas evolving toward DCI, and preceded intracranial changes when available.

CONCLUSION

Although this study has a small sample size, prolonged AT/D decrease seems to be a reliable biomarker of DCI.

SIGNIFICANCE

cEEG changes are likely to precede cerebral infarction and could be useful at the bedside to detect DCI before irreversible damage.

Continuous electroencephalography predicts delayed cerebral ischemia after subarachnoid hemorrhage: A prospective study of diagnostic accuracy

OBJECTIVE

Delayed cerebral ischemia (DCI) is a common, disabling complication of subarachnoid hemorrhage (SAH). Preventing DCI is a key focus of neurocritical care, but interventions carry risk and cannot be applied indiscriminately. Although retrospective studies have identified continuous electroencephalographic (cEEG) measures associated with DCI, no study has characterized the accuracy of cEEG with sufficient rigor to justify using it to triage patients to interventions or clinical trials. We therefore prospectively assessed the accuracy of cEEG for predicting DCI, following the Standards for Reporting Diagnostic Accuracy Studies.

METHODS

We prospectively performed cEEG in nontraumatic, high-grade SAH patients at a single institution. The index test consisted of clinical neurophysiologists prospectively reporting prespecified EEG alarms: (1) decreasing relative alpha variability, (2) decreasing alpha-delta ratio, (3) worsening focal slowing, or (4) late appearing epileptiform abnormalities. The diagnostic reference standard was DCI determined by blinded, adjudicated review. Primary outcome measures were sensitivity and specificity of cEEG for subsequent DCI, determined by multistate survival analysis, adjusted for baseline risk.

RESULTS

One hundred three of 227 consecutive patients were eligible and underwent cEEG monitoring (7.7-day mean duration). EEG alarms occurred in 96.2% of patients with and 19.6% without subsequent DCI (1.9-day median latency, interquartile range = 0.9-4.1). Among alarm subtypes, late onset epileptiform abnormalities had the highest predictive value. Prespecified EEG findings predicted DCI among patients with low (91% sensitivity, 83% specificity) and high (95% sensitivity, 77% specificity) baseline risk.

INTERPRETATION

cEEG accurately predicts DCI following SAH and may help target therapies to patients at highest risk of secondary brain injury. Ann Neurol 2018;83:958-969.

Noninvasive Neuromonitoring: Current Utility in Subarachnoid Hemorrhage, Traumatic Brain Injury, and Stroke

Noninvasive neuromonitoring is increasingly being used to monitor the course of primary brain injury and limit secondary brain damage of patients in the neurocritical care unit. Proposed advantages over invasive neuromonitoring methods include a lower risk of infection and bleeding, no need for surgical installation, mobility and portability of some devices, and safety. The question, however, is whether noninvasive neuromonitoring is practical and trustworthy enough already. We searched the recent literature and reviewed English-language studies on noninvasive neuromonitoring in subarachnoid hemorrhage, traumatic brain injury, and ischemic and hemorrhagic stroke between the years 2010 and 2015. We found 88 studies that were eligible for review including the methods transcranial ultrasound, electroencephalography, evoked potentials, near-infrared spectroscopy, bispectral index, and pupillometry. Noninvasive neuromonitoring cannot yet completely replace invasive methods in most situations, but has great potential being complementarily integrated into multimodality monitoring, for guiding management, and for limiting the use of invasive devices and in-hospital transports for imaging.

Novel minimally invasive multi-modality monitoring modalities in neurocritical care

Elevated intracranial pressure (ICP) following brain injury contributes to poor outcomes for patients, primarily by reducing the caliber of cerebral vasculature, and thereby reducing cerebral blood flow. Careful monitoring of ICP is critical in these patients in order to determine prognosis, implement treatment when ICP becomes elevated, and to judge responsiveness to treatment. Currently, the gold standard for monitoring is invasive pressure transducers, usually an intraventricular monitor, which presents significant risk of infection and hemorrhage. These risks made discovering non-invasive methods for monitoring ICP and cerebral perfusion a priority for researchers. Herein we sought to review recent publications on novel minimally invasive multi-modality monitoring techniques that provide surrogate data on ICP, cerebral oxygenation, metabolism and blood flow. While limitations in various forms preclude them from supplanting the use of invasive monitors, these modalities represent useful screening tools within our armamentarium that may be invaluable when the risks of invasive monitoring outweigh the associated benefits.