Continuous EEG detection of DCI and seizures following aSAH: a systematic review

Seizure Assessment and Forecasting With Efficient Rapid-EEG: A Retrospective Multicenter Comparative Effectiveness Study

BACKGROUND AND OBJECTIVES

Approximately 30% of critically ill patients have seizures, and more than half of these seizures do not have an overt clinical correlate. EEG is needed to avoid missing seizures and prevent overtreatment with antiseizure medications. Conventional-EEG (cEEG) resources are logistically constrained and unable to meet their growing demand for seizure detection even in highly developed centers. Brief EEG screening with the validated 2HELPS2B algorithm was proposed as a method to triage cEEG resources, but it is hampered by cEEG requirements, primarily EEG technologists. Seizure risk-stratification using reduced time-to-application rapid response-EEG (rrEEG) systems (∼5 minutes) could be a solution. We assessed the noninferiority of the 2HELPS2B score on a 1-hour rrEEG compared to cEEG.

METHODS

A multicenter retrospective EEG diagnostic accuracy study was conducted from October 1, 2021, to July 31, 2022. Chart and EEG review performed with consecutive sampling at 4 tertiary care centers, included records of patients ≥18 years old, from January 1, 2018, to June 20, 2022. Monte Carlo simulation power analysis yielded n = 500 rrEEG; for secondary outcomes n = 500 cEEG and propensity-score covariate matching was planned. Primary outcome, noninferiority of rrEEG for seizure risk prediction, was assessed per area under the receiver operator characteristic curve (AUC). Noninferiority margin (0.05) was based on the 2HELPS2B validation study.

RESULTS

A total of 240 rrEEG with follow-on cEEG were obtained. Median age was 64 (interquartile range 22); 42% were female. 2HELPS2B on a 1-hour rrEEG met noninferiority to cEEG (AUC 0.85, 95% CI 0.78-0.90, p = 0.001). Secondary endpoints of comparison with a matched contemporaneous cEEG showed no significant difference in AUC (0.89, 95% CI 0.83-0.94, p = 0.31); in false negative rate for the 2HELPS2B = 0 group (p = 1.0) rrEEG (0.021, 95% CI 0-0.062), cEEG (0.016, 95% CI 0-0.048); nor in survival analyses.

DISCUSSION

2HELPS2B on 1-hour rrEEG is noninferior to cEEG for seizure prediction. Patients with low-risk (2HELPS2B = 0) may be able to forgo prolonged cEEG, allowing for increased monitoring of at-risk patients.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that rrEEG is noninferior to cEEG in calculating the 2HELPS2B score to predict seizure risk.

The impact of perfusion computed tomography on the diagnosis and outcome of delayed cerebral ischemia after subarachnoid hemorrhage

BACKGROUND

Delayed cerebral ischemia (DCI) is a preventable cause of poor neurological outcome in aneurysmal subarachnoid hemorrhage (aSAH). Advances in radiological methods, such as cerebral perfusion computed tomography (CTP), could help diagnose DCI earlier and potentially improve outcomes in aSAH. The objective of this study was to assess whether the use of CTP to diagnose DCI early could reduce the risk of infarction related to DCI.

METHODS

Retrospective cohort study of patients in the intensive care unit of Erasme Hospital (Brussels, Belgium) between 2004 and 2021 with aSAH who developed DCI. Patients were classified as: "group 1" - DCI diagnosed based on clinical deterioration or "group 2" - DCI diagnosed using CTP. The primary outcome was the development of infarction unrelated to the initial bleeding or surgery.

RESULTS

211 aSAH patients were diagnosed with DCI during the study period: 139 (66%) in group 1 and 72 (34%) in group 2. In group 1, 109 (78%) patients developed a cerebral infarction, compared to 45 (63%) in group 2 (p = 0.02). The adjusted cumulative incidence of DCI over time was lower in group 2 than in group 1 [hazard ratio 0.65 (95% CI 0.48-0.94); p = 0.02]. The use of CTP to diagnose DCI was not independently associated with mortality or neurological outcome.

CONCLUSIONS

The use of CTP to diagnose DCI might help reduce the risk of developing cerebral infarction after aSAH, although the impact of such an approach on patient outcomes needs to be further demonstrated.

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.

Non-Convulsive Status Epilepticus in Aneurysmal Subarachnoid Hemorrhage: A Prognostic Parameter

A non-convulsive status epilepticus (ncSE) is a potentially fatal complication for patients in neurointensive care. In patients with aneurysmal subarachnoid hemorrhage (SAH), ncSE remains scarcely investigated. In this study, we aim to investigate the frequency and influence of non-convulsive status epilepticus on outcome in patients with SAH. We retrospectively analyzed data of consecutive patients with aneurysmal subarachnoid hemorrhage and evaluated clinical, radiological, demographical and electroencephalogram (EEG) data. Outcome was assessed according to the modified Rankin Scale (mRS) at 6 months and stratified into favorable (mRS 0-2) vs. unfavorable (mRS 3-6). We identified 171 patients with SAH, who received EEG between 01/2012 and 12/2020. ncSE was diagnosed in 19 patients (3.7%), only one of whom achieved favorable outcome. The multivariate regression analysis revealed four independent predictors of unfavorable outcome: presence of ncSE (p = 0.003; OR 24.1; 95 CI% 2.9-195.3), poor-grade SAH (p < 0.001; OR 14.0; 95 CI% 8.5-23.1), age (p < 0.001; OR 2.8; 95 CI% 1.6-4.6) and the presence of DIND (p < 0.003; OR 1.9; 95 CI% 1.2-3.1) as independent predictors for unfavorable outcome. According to our study, development of ncSE in patients suffering SAH might correlate with poor prognosis. Even when medical treatment is successful and no EEG abnormalities are detected, the long-term outcome remains poor.

Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States. A Data Driven Study

The growing number of depressive people and the overload in primary care services make it necessary to identify depressive states with easily accessible biomarkers such as mobile electroencephalography (EEG). Some studies have addressed this issue by collecting and analyzing EEG resting state in a search of appropriate features and classification methods. Traditionally, EEG resting state classification methods for depression were mainly based on linear or a combination of linear and non-linear features. We hypothesize that participants with ongoing depressive states differ from controls in complex patterns of brain dynamics that can be captured in EEG resting state data, using only nonlinear measures on a few electrodes, making it possible to develop cheap and wearable devices that could be even monitored through smartphones. To validate such a perspective, a resting-state EEG study was conducted with 50 participants, half with depressive state (DEP) and half controls (CTL). A data-driven approach was applied to select the most appropriate time window and electrodes for the EEG analyses, as suggested by Giacometti⁠, as well as the most efficient nonlinear features and classifiers, to distinguish between CTL and DEP participants. Nonlinear features showing temporo-spatial and spectral complexity were selected. The results confirmed that computing nonlinear features from a few selected electrodes in a 15 s time window are sufficient to classify DEP and CTL participants accurately. Finally, after training and testing internally the classifier, the trained machine was applied to EEG resting state data (CTL and DEP) from a publicly available database, validating the capacity of generalization of the classifier with data from different equipment, population, and environment obtaining an accuracy near 100%.

Health Care Expenditures Associated with Delayed Cerebral Ischemia Following Subarachnoid Hemorrhage: A Propensity-Adjusted Analysis

OBJECTIVE

The additional hospital costs associated with delayed cerebral ischemia (DCI) have not been well investigated in prior literature. In this study, the total hospital cost of DCI in aneurysmal subarachnoid hemmorhage (aSAH) patients treated at a single quaternary center was analyzed.

METHODS

All patients in the Post-Barrow Ruptured Aneurysm Trial treated for an aSAH between January 1, 2014, and July 31, 2019, were retrospectively analyzed. DCI was defined as cerebral infarction identified on computed tomography, magnetic resonance imaging, or autopsy after exclusion of procedure-related infarctions. The primary outcome was the difference in total cost (including hospital, discharge facility, and all follow-up) using a propensity-adjusted analysis. Propensity score covariate-adjusted linear regression analysis included age, sex, open versus endovascular treatment, Hunt and Hess score, and Charlson Comorbidity Index score.

RESULTS

Of the 391 patients included, 144 (37%) had DCI. Patients with DCI had a significantly greater cost compared to patients without DCI (mean standard deviation $112,081 [$54,022] vs. $86,159 [$38,817]; P < 0.001) and a significantly greater length of stay (21 days [11] vs. 18 days [8], P = 0.003, respectively). In propensity-adjusted linear regression analysis, both DCI (odds ratio, $13,871; 95% confidence interval, $7558-$20,185; P < 0.001) and length of stay (odds ratio, $3815 per day; 95% confidence interval, $3480-$4149 per day; P < 0.001) were found to significantly increase the cost.

CONCLUSIONS

The significantly higher costs associated with DCI further support the evidence that adverse effects associated with DCI in aSAH pose a significant burden to the health care system.

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).

Large practice variations in diagnosis and treatment of delayed cerebral ischemia after subarachnoid hemorrhage

BACKGROUND

Delayed cerebral ischemia (DCI) contributes to poor outcomes after subarachnoid hemorrhage (SAH). The pathophysiology of DCI is not fully understood, which has hindered the adoption of a uniform definition. Furthermore, a reliable diagnostic test and an effective evidence-based treatment are lacking. This could lead to variations in care.

METHODS

A web-based survey on the variations in the definition, diagnosis, and treatment of DCI was designed and sent to 314 intensivists, neurologists, and neurosurgeons of all 9 hospitals in the Netherlands who care for patients with SAH. The responders were categorized into physicians responsible for the coordination of SAH care and those who were not. For questions on the definition and diagnosis, only the responses from the coordinating physicians were evaluated. For the treatment questions, all the responses were evaluated.

RESULTS

The response rate was 34% (106 of 314). All 9 hospitals were represented. Of the responses, 27 did not provide answers for the definition, diagnosis, or treatment questions; 79 responses were used for analysis. Signs of vasospasm were required by 21 of the 47 coordinating physicians (44%) when considering DCI. Of the 47 coordinating physicians, 24 (51%) did not use a diagnostic test results for a positive diagnosis of DCI. When patients were discharged within 21 days, 33 of the 73 responders (45%) did not provide a prescription for nimodipine continuation. Finally, all but one hospital had treated DCI with hypertension induction.

CONCLUSIONS

We found large variations in the definition, diagnosis, and treatment of DCI in the Netherlands. In the absence of evidence-based treatment, standardization of management seems warranted in an effort to optimize DCI care.

Multimodality Monitoring for Delayed Cerebral Ischemia in Subarachnoid Hemorrhage: A Mini Review

Aneurysmal subarachnoid hemorrhage is a disease with high mortality and morbidity due in large part to delayed effects of the hemorrhage, including vasospasm, and delayed cerebral ischemia. These two are now recognized as overlapping yet distinct entities, and supportive therapies for delayed cerebral ischemia are predicated on identifying DCI as quickly as possible. The purpose of this overview is to highlight diagnostic tools that are being used in the identification of DCI in the neurocritical care settings.

How to diagnose delayed cerebral ischaemia and symptomatic vasospasm and prevent cerebral infarction in patients with subarachnoid haemorrhage

PURPOSE OF REVIEW

Delayed cerebral ischaemia (DCI) complicates the clinical course of patients with subarachnoid haemorrhage (SAH) in 20--30% and substantially worsens outcome. In this review, we describe a multimodal diagnostic approach based on underlying mechanisms of DCI and provide treatment options with a special focus on the most recently published literature.

RECENT FINDINGS

Symptomatic vasospasm refers to clinical deterioration in the presence of vasospasm whereas DCI constitutes multiple causes. Pathophysiologic mechanisms underlying DCI range beyond large vessel vasospasm from neuroinflammation, to microthromboembolism, impaired cerebral autoregulation, cortical spreading depolarizations and many others. The current definition of DCI can be challenged by these mechanisms. We propose a pragmatic approach using a combination of clinical examination, cerebral ultrasonography, neuroimaging modalities and multimodal neuromonitoring to trigger therapeutic interventions in the presence of DCI. In addition to prophylactic nimodipine and management principles to improve oxygen delivery and decrease the brain metabolic demand, other specific interventions include permissive hypertension, intra-arterial application of calcium channel blockers and in selected patients angioplasty.

SUMMARY

The complex pathophysiology underlying DCI urges for a multimodal diagnostic approach triggering targeted interventions. Novel treatment concepts still have to be proven in large trials.

Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury

PURPOSE OF REVIEW

Acute brain injury (ABI) is a broad category of pathologies, including traumatic brain injury, and is commonly complicated by seizures. Electroencephalogram (EEG) studies are used to detect seizures or other epileptiform patterns. This review seeks to clarify EEG findings relevant to ABI, explore practical barriers limiting EEG implementation, discuss strategies to leverage EEG monitoring in various clinical settings, and suggest an approach to utilize EEG for triage.

RECENT FINDINGS

Current literature suggests there is an increased morbidity and mortality risk associated with seizures or patterns on the ictal-interictal continuum (IIC) due to ABI. Further, increased use of EEG is associated with better clinical outcomes. However, there are many logistical barriers to successful EEG implementation that prohibit its ubiquitous use. Solutions to these limitations include the use of rapid EEG systems, non-expert EEG analysis, machine learning algorithms, and the incorporation of EEG data into prognostic models.