Continuous versus routine EEG in patients after cardiac arrest-Analysis of a randomized controlled trial (CERTA) - RESUS-D-22-00369

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.

Early focal electroencephalogram and neuroimaging findings predict epilepsy development after aneurysmal subarachnoid hemorrhage

INTRODUCTION

Seizures are a common complication of subarachnoid hemorrhage (SAH) in both acute and late stages: 10-20 % acute symptomatic seizures, 12-25 % epilepsy rate at five years. Our aim was to identify early electroencephalogram (EEG) and computed tomography (CT) findings that could predict long-term epilepsy after SAH.

MATERIAL AND METHODS

This is a multicenter, retrospective, longitudinal study of adult patients with aneurysmal SAH admitted to two tertiary care hospitals between January 2011 to December 2022. Routine 30-minute EEG recording was performed in all subjects during admission period. Exclusion criteria were the presence of prior structural brain lesions and/or known epilepsy. We documented the presence of SAH-related cortical involvement in brain CT and focal electrographic abnormalities (epileptiform and non-epileptiform). Post-SAH epilepsy was defined as the occurrence of remote unprovoked seizures ≥ 7 days from the bleeding.

RESULTS

We included 278 patients with a median follow-up of 2.4 years. The mean age was 57 (+/-12) years, 188 (68 %) were female and 49 (17.6 %) developed epilepsy with a median latency of 174 days (IQR 49-479). Cortical brain lesions were present in 189 (68 %) and focal EEG abnormalities were detected in 158 patients (39 epileptiform discharges, 119 non-epileptiform abnormalities). The median delay to the first EEG recording was 6 days (IQR 2-12). Multiple Cox regression analysis showed higher risk of long-term epilepsy in those patients with CT cortical involvement (HR 2.6 [1.3-5.2], p 0.009), EEG focal non-epileptiform abnormalities (HR 3.7 [1.6-8.2], p 0.002) and epileptiform discharges (HR 6.7 [2.8-15.8], p < 0.001). Concomitant use of anesthetics and/or antiseizure medication during EEG recording had no influence over its predictive capacity. ROC-curve analysis of the model showed good predictive capability at 5 years (AUC 0.80, 95 %CI 0.74-0.87).

CONCLUSIONS

Focal electrographic abnormalities (both epileptiform and non-epileptiform abnormalities) and cortical involvement in neuroimaging predict the development of long-term epilepsy. In-patient EEG and CT findings could allow an early risk stratification and facilitate a personalized follow-up and management of SAH patients.

Recent advances in clinical electroencephalography

PURPOSE OF REVIEW

Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients.

RECENT FINDINGS

Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field.

SUMMARY

This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.

Trend of Outcome Metrics in Recent Out-of-Hospital-Cardiac-Arrest Research: A Narrative Review of Clinical Trials

Cardiopulmonary resuscitation (CPR) research traditionally focuses on survival. In 2018, the International Liaison Committee on Resuscitation (ILCOR) proposed more patient-centered outcomes. Our narrative review assessed clinical trials after 2018 to identify the trends of outcome metrics in the field OHCA research. We performed a search of the PubMed database from 1 January 2019 to 22 September 2023. Prospective clinical trials involving adult humans were eligible. Studies that did not report any patient-related outcomes or were not available in full-text or English language were excluded. The articles were assessed for demographic information and primary and secondary outcomes. We included 89 studies for analysis. For the primary outcome, 31 (35%) studies assessed neurocognitive functions, and 27 (30%) used survival. For secondary outcomes, neurocognitive function was present in 20 (22%) studies, and survival was present in 10 (11%) studies. Twenty-six (29%) studies used both survival and neurocognitive function. Since the publication of the COSCA guidelines in 2018, there has been an increased focus on neurologic outcomes. Although survival outcomes are used frequently, we observed a trend toward fewer studies with ROSC as a primary outcome. There were no quality-of-life assessments, suggesting a need for more studies with patient-centered outcomes that can inform the guidelines for cardiac-arrest management.

Prognosis of Cardiac Arrest-Peri-arrest and Post-arrest Considerations

There has been only a small improvement in survival and neurologic outcomes in patients with cardiac arrest in recent decades. Type of arrest, length of total arrest time, and location of arrest alter the trajectory of survival and neurologic outcome. In the post-arrest phase, clinical markers such as blood markers, pupillary light response, corneal reflex, myoclonic jerking, somatosensory evoked potential, and electroencephalography testing can be used to help guide neurological prognostication. Most of the testing should be performed 72 hours post-arrest with special considerations for longer observation periods in patients who underwent TTM or who had prolonged sedation and/or neuromuscular blockade.

Relationship between serum neuron-specific enolase and EEG after cardiac arrest: A reappraisal

OBJECTIVE

Electroencephalogram (EEG) and serum neuron specific enolase (NSE) are frequently used prognosticators after cardiac arrest (CA). This study explored the association between NSE and EEG, considering the role of EEG timing, its background continuity, reactivity, occurrence of epileptiform discharges, and pre-defined malignancy degree.

METHODS

Retrospective analysis including 445 consecutive adults from a prospective registry, surviving the first 24 hours after CA and undergoing multimodal evaluation. EEG were interpreted blinded to NSE results.

RESULTS

Higher NSE was associated with poor EEG prognosticators, such as increasing malignancy, repetitive epileptiform discharges and lack of background reactivity, independently of EEG timing (including sedation and temperature). When stratified for background continuity, NSE was higher with repetitive epileptiform discharges, except in the case of suppressed EEGs. This relationship showed some variation according to the recording time.

CONCLUSIONS

Neuronal injury after CA, reflected by NSE, correlates with several EEG features: increasing EEG malignancy, lack of background reactivity, and presence of repetitive epileptiform discharges. The correlation between epileptiform discharges and NSE is influenced by underlying EEG background and timing.

SIGNIFICANCE

This study, describing the complex interplay between serum NSE and epileptiform features, suggests that epileptiform discharges reflect neuronal injury particularly in non-suppressed EEG.

Critical care EEG monitoring: improving access and unravelling potentially epileptic patterns

PURPOSE OF REVIEW

The major advances in critical care EEG have been the development of rapid response EEG, major revision of the American Clinical Neurophysiology Society's (ACNS) standardized critical care EEG terminology, and the commencement of treatment trials on rhythmic and periodic patterns (RPPs) that do not qualify as seizures.

RECENT FINDINGS

Rapid response EEG (rEEG) has proven an important supplement to full montage continuous EEG monitoring (cEEG). This EEG can be applied in a few minutes and provides excellent ability to exclude seizures, selecting those where conversion to cEEG would have the greatest diagnostic yield. Once cEEG has been commenced, the durations required to adequately exclude seizures have been refined. The ACNS provided major revision and expansion to the standardized critical care EEG terminology, which paved the way for determining with great accuracy the RPPs that are associated with seizures and that are capable of causing neurologic symptoms and/or secondary neuronal injury. The current limitations to multicenter treatment trials of these patterns have been highlighted.

SUMMARY

Novel methods of EEG in critical care have been expanding access to all patients where clinically indicated. Standardized EEG terminology has provided the framework to determine what patterns in which presenting causes warrant treatment vs. those that do not.

Brain monitoring after cardiac arrest

PURPOSE OF REVIEW

To describe the available neuromonitoring tools in patients who are comatose after resuscitation from cardiac arrest because of hypoxic-ischemic brain injury (HIBI).

RECENT FINDINGS

Electroencephalogram (EEG) is useful for detecting seizures and guiding antiepileptic treatment. Moreover, specific EEG patterns accurately identify patients with irreversible HIBI. Cerebral blood flow (CBF) decreases in HIBI, and a greater decrease with no CBF recovery indicates poor outcome. The CBF autoregulation curve is narrowed and right-shifted in some HIBI patients, most of whom have poor outcome. Parameters derived from near-infrared spectroscopy (NIRS), intracranial pressure (ICP) and transcranial Doppler (TCD), together with brain tissue oxygenation, are under investigation as tools to optimize CBF in patients with HIBI and altered autoregulation. Blood levels of brain biomarkers and their trend over time are used to assess the severity of HIBI in both the research and clinical setting, and to predict the outcome of postcardiac arrest coma. Neuron-specific enolase (NSE) is recommended as a prognostic tool for HIBI in the current postresuscitation guidelines, but other potentially more accurate biomarkers, such as neurofilament light chain (NfL) are under investigation.

SUMMARY

Neuromonitoring provides essential information to detect complications, individualize treatment and predict prognosis in patients with HIBI.

Prevalence, electroclinical spectrum and effect on the outcome of non-convulsive status epilepticus in critically ill patients; the utility of routine electroencephalogram

OBJECTIVES

To highlight the importance of routine electroencephalogram (rEEG) in detecting non-convulsive status epilepticus (NCSE), describing the electroclinical spectrum and effect on outcome in critically ill patients with altered mental status (CIPAMS).

METHODS

This retrospective study was conducted at King Fahd University Hospital. Clinical data and EEG recordings of CIPAMS to rule out NCSE were reviewed. All patients had at least 30 minutes of EEG recording. The Salzburg Consensus criteria (SCC) were applied to diagnose NCSE. The data analysis was performed using SPSS version 22.0. The chi-squared test was used to compare categorical variables such as etiologies, EEG findings, and functional outcomes. Multivariable analysis was performed to identify the predictors of unfavorable outcomes.

RESULTS

A total of 323 CIPAMS referred to rule out NCSE were enrolled (mean age 57.8 ± 20 years). Nonconvulsive status epilepticus was diagnosed in 54 (16.7%) patients. A significant association was found between subtle clinical features and NCSE (P =< 0.01). Acute ischemic stroke (18.5%), sepsis (18.5%), and hypoxic brain injury (22.2%) were the main etiologies. The previous history of epilepsy was significantly associated with NCSE (P = 0.01). Acute stroke, cardiac arrest, mechanical ventilation, and NCSE were statistically associated with unfavorable outcomes. Nonconvulsive status epilepticus was an independent predictor of unfavorable outcomes (P = 0.02, OR = 2.75, CI = 1.16-6.48) during multivariable analysis. Sepsis was associated with higher mortality (P =< 0.01, OR = 2.4, CI = 1.4-4.0).

SIGNIFICANCE

Our study findings suggest that the utility of rEEG in detecting NCSE in CIPAMS should not be underestimated. Important observations further indicate that repeating rEEG is advisable, as this would increase the likelihood of identifying NCSE. Thus, physicians should consider and repeat rEEG when evaluating CIPAMS in order to detect NCSE, which is an independent predictor of unfavorable outcomes. Nonetheless, further studies comparing rEEG and cEEG yields are required to augment the current understanding of the electroclinical spectrum and better describe NCSE in CIPAMS.

Epileptiform patterns predicting unfavorable outcome in postanoxic patients: A matter of time?

OBJECTIVE

Historically, epileptiform malignant EEG patterns (EMPs) have been considered to anticipate an unfavorable outcome, but an increasing amount of evidence suggests that they are not always or invariably associated with poor prognosis. We evaluated the prognostic significance of an EMP onset in two different timeframes in comatose patients after cardiac arrest (CA): early-EMPs and late-EMPs, respectively.

METHODS

We included all comatose post-CA survivors admitted to our intensive care unit (ICU) between 2016 and 2018 who underwent at least two 30-minute EEGs, collected at T0 (12-36 h after CA) and T1 (36-72 h after CA). All EEGs recordings were re-analyzed following the 2021 ACNS terminology by two senior EEG specialists, blinded to outcome. Malignant EEGs with abundant sporadic spikes/sharp waves, rhythmic and periodic patterns, or electrographic seizure/status epilepticus, were included in the EMP definition. The primary outcome was the cerebral performance category (CPC) score at 6 months, dichotomized as good (CPC 1-2) or poor (CPC 3-5) outcome.

RESULTS

A total of 58 patients and 116 EEG recording were included in the study. Poor outcome was seen in 28 (48%) patients. In contrast to late-EMPs, early-EMPs were associated with a poor outcome (p = 0.037), persisting after multiple regression analysis. Moreover, a multivariate binomial model coupling the timing of EMP onset with other EEG predictors such as T1 reactivity and T1 normal voltage background can predict outcome in the presence of an otherwise non-specific malignant EEG pattern with quite high specificity (82%) and moderate sensitivity (77%).

CONCLUSIONS

The prognostic significance of EMPs seems strongly time-dependent and only their early-onset may be associated with an unfavorable outcome. The time of onset of EMP combined with other EEG features could aid in defining prognosis in patients with intermediate EEG patterns.