Persistent generalized periodic discharges: A specific marker of fatal outcome in cerebral hypoxia

EEG Signal Processing Techniques and Applications

Electroencephalography (EEG) is a widely recognised non-invasive method for capturing brain electrophysiological activity [...].

Repetitive Electroencephalography as Biomarker for the Prediction of Survival in Patients with Post-Hypoxic Encephalopathy

Predicting survival in patients with post-hypoxic encephalopathy (HE) after cardiopulmonary resuscitation is a challenging aspect of modern neurocritical care. Here, continuous electroencephalography (cEEG) has been established as the gold standard for neurophysiological outcome prediction. Unfortunately, cEEG is not comprehensively available, especially in rural regions and developing countries. The objective of this monocentric study was to investigate the predictive properties of repetitive EEGs (rEEGs) with respect to 12-month survival based on data for 199 adult patients with HE, using log-rank and multivariate Cox regression analysis (MCRA). A total number of 59 patients (29.6%) received more than one EEG during the first 14 days of acute neurocritical care. These patients were analyzed for the presence of and changes in specific EEG patterns that have been shown to be associated with favorable or poor outcomes in HE. Based on MCRA, an initially normal amplitude with secondary low-voltage EEG remained as the only significant predictor for an unfavorable outcome, whereas all other relevant parameters identified by univariate analysis remained non-significant in the model. In conclusion, rEEG during early neurocritical care may help to assess the prognosis of HE patients if cEEG is not available.

A Cry for Survival? Rhythmic and Periodic EEG Discharges as Treatment Targets Following Cardiac Arrest

Treating Rhythmic and Periodic EEG Patterns in Comatose Survivors of Cardiac Arrest

Ruijter BJ, Keijzer HM, Tjepkema-Cloostermans MC, Blans MJ, Beishuizen A, Tromp SC, Scholten E, Horn J, van Rootselaar AF, Admiraal MM, van den Bergh WM, Elting JJ, Foudraine NA, Kornips FHM, van Kranen-Mastenbroek VHJM, Rouhl RPW, Thomeer EC, Moudrous W, Nijhuis FAP, Booij SJ, Hoedemaekers CWE, Doorduin J, Taccone FS, van der Palen J, van Putten MJAM, Hofmeijer J. N Engl J Med. 2022;386(8):724-734. doi:10.1056/NEJMoa2115998

Background:

Whether the treatment of rhythmic and periodic electroencephalographic (EEG) patterns in comatose survivors of cardiac arrest improves outcomes is uncertain.

Methods:

We conducted an open-label trial of suppressing rhythmic and periodic EEG patterns detected on continuous EEG monitoring in comatose survivors of cardiac arrest. Patients were randomly assigned in a 1:1 ratio to a stepwise strategy of antiseizure medications to suppress this activity for at least 48 consecutive hours plus standard care (antiseizure-treatment group) or to standard care alone (control group); standard care included targeted temperature management in both groups. The primary outcome was neurologic outcome according to the score on the Cerebral Performance Category (CPC) scale at 3 months, dichotomized as a good outcome (CPC score indicating no, mild, or moderate disability) or a poor outcome (CPC score indicating severe disability, coma, or death). Secondary outcomes were mortality, length of stay in the intensive care unit (ICU), and duration of mechanical ventilation.

Results:

We enrolled 172 patients, with 88 assigned to the antiseizure-treatment group and 84 to the control group. Rhythmic or periodic EEG activity was detected a median of 35 hours after cardiac arrest; 98 of 157 patients (62%) with available data had myoclonus. Complete suppression of rhythmic and periodic EEG activity for 48 consecutive hours occurred in 49 of 88 patients (56%) in the antiseizure-treatment group and in 2 of 83 patients (2%) in the control group. At 3 months, 79 of 88 patients (90%) in the antiseizure-treatment group and 77 of 84 patients (92%) in the control group had a poor outcome (difference, 2 percentage points; 95% confidence interval, −7 to 11; P = 0.68). Mortality at 3 months was 80% in the antiseizure-treatment group and 82% in the control group. The mean length of stay in the ICU and mean duration of mechanical ventilation were slightly longer in the antiseizure-treatment group than in the control group.

Conclusions:

In comatose survivors of cardiac arrest, the incidence of a poor neurologic outcome at 3 months did not differ significantly between a strategy of suppressing rhythmic and periodic EEG activity with the use of antiseizure medication for at least 48 hours plus standard care and standard care alone.

EEG patterns and their correlations with short- and long-term mortality in patients with hypoxic encephalopathy

OBJECTIVE

To analyze the association between electroencephalographic (EEG) patterns and overall, short- and long-term mortality in patients with hypoxic encephalopathy (HE).

METHODS

Retrospective, mono-center analysis of 199 patients using univariate log-rank tests (LR) and multivariate cox regression (MCR).

RESULTS

Short-term mortality, defined as death within 30-days post-discharge was 54.8%. Long-term mortality rates were 69.8%, 71.9%, and 72.9%, at 12-, 24-, and 36-months post-HE, respectively. LR revealed a significant association between EEG suppression (SUP) and short-term mortality, and identified low voltage EEG (LV), burst suppression (BSP), periodic discharges (PD) and post-hypoxic status epilepticus (PSE) as well as missing (aBA) or non-reactive background activity (nrBA) as predictors for overall, short- and long-term mortality. MCR indicated SUP, LV, BSP, PD, aBA and nrBA as significantly associated with overall and short-term mortality to varying extents. LV and BSP were significant predictors for long-term mortality in short-term survivors. Rhythmic delta activity, stimulus induced rhythmic, periodic or ictal discharges and sharp waves were not significantly associated with a higher mortality.

CONCLUSION

The presence of several specific EEG patterns can help to predict overall, short- and long-term mortality in HE patients.

SIGNIFICANCE

The present findings may help to improve the challenging prognosis estimation in HE patients.

[S2k guidelines: status epilepticus in adulthood : Guidelines of the German Society for Neurology]

This S2k guideline on diagnosis and treatment of status epilepticus (SE) in adults is based on the last published version from 2021. New definitions and evidence were included in the guideline and the clinical pathway. A seizures lasting longer than 5 minutes (or ≥ 2 seizures over more than 5 mins without intermittend recovery to the preictal neurological state. Initial diagnosis should include a cCT or, if possible, an MRI. The EEG is highly relevant for diagnosis and treatment-monitoring of non-convulsive SE and for the exclusion or diagnosis of psychogenic non-epileptic seizures. As the increasing evidence supports the relevance of inflammatory comorbidities (e.g. pneumonia) related clinical chemistry should be obtained and repeated over the course of a SE treatment, and antibiotic therapy initiated if indicated.Treatment is applied on four levels: 1. Initial SE: An adequate dose of benzodiazepine is given i.v., i.m., or i.n.; 2. Benzodiazepine-refractory SE: I.v. drugs of 1st choice are levetiracetam or valproate; 3. Refractory SE (RSE) or 4. Super-refractory SE (SRSE): I.v. propofol or midazolam alone or in combination or thiopental in anaesthetic doses are given. In focal non-convulsive RSE the induction of a therapeutic coma depends on the circumstances and is not mandatory. In SRSE the ketogenic diet should be given. I.v. ketamine or inhalative isoflorane can be considered. In selected cased electroconvulsive therapy or, if a resectable epileptogenic zone can be defined epilepsy surgery can be applied. I.v. allopregnanolone or systemic hypothermia should not be used.

Prognostic value of 'late' electroencephalography recordings in patients with cardiopulmonal resuscitation after cardiac arrest

Background

Electroencephalography (EEG) significantly contributes to the neuroprognostication after resuscitation from cardiac arrest. Recent studies suggest that the prognostic value of EEG is highest for continuous recording within the first days after cardiac arrest. Early continuous EEG, however, is not available in all hospitals. In this observational study, we sought to evaluate the predictive value of a ‘late’ EEG recording 5–14 days after cardiac arrest without sedatives.

Methods

We retrospectively analyzed EEG data in consecutive adult patients treated at the medical intensive care units (ICU) of the Charité—Universitätsmedizin Berlin. Outcome was assessed as cerebral performance category (CPC) at discharge from ICU, with an unfavorable outcome being defined as CPC 4 and 5.

Results

In 187 patients, a ‘late’ EEG recording was performed. Of these patients, 127 were without continuous administration of sedative agents for at least 24 h before the EEG recording. In this patient group, a continuously suppressed background activity < 10 µV predicted an unfavorable outcome with a sensitivity of 31% (95% confidence interval (CI) 20–45) and a specificity of 99% (95% CI 91–100). In patients with suppressed background activity and generalized periodic discharges, sensitivity was 15% (95% CI 7–27) and specificity was 100% (95% CI 94–100). GPDs on unsuppressed background activity were associated with a sensitivity of 42% (95% CI 29–46) and a specificity of 92% (95% CI 82–97).

Conclusions

A ‘late’ EEG performed 5 to 14 days after resuscitation from cardiac arrest can aide in prognosticating functional outcome. A suppressed EEG background activity in this time period indicates poor outcome.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-021-10549-y.

Presence of generalized periodic discharges and hospital mortality

BACKGROUND

Generalized periodic discharges (GPDs) are rare patterns that can be found in long-term electroencephalographic monitoring in critical patients. These patterns have been correlated with non-seizure crisis and non-convulsive status epilepticus, associated with poor prognosis.

OBJECTIVE

To compare the outcome between patients who developed GPDs and patients with other abnormalities in long-term electroencephalographic monitoring.

METHODS

A retrospective study was performed by analyzing the medical records of 112 patients over 18 years who developed GPDs during long-term electroencephalographic monitoring (12‒16 hours of monitoring) in the intensive care unit of a general hospital, compared with a group that had only nonspecific abnormalities in the monitoring.

RESULTS

Age and cardiorespiratory arrest (CA) were risk factors for death - OR 1.04 (95% CI 1,02 - 1,07) and p<0.001; OR 3.00 (95% CI 1,01 - 8,92) and p=0.046, respectively. It was not possible to evaluate if GPDs alone were associated with an unfavorable outcome or would be a bias for the development of CA in these patients. However, of the six isolated GPDs cases, 2/3 evolved to death, showing a tendency to worse prognosis. A significant difference (p=0.031) was observed for a worse outcome when comparing the group of 28 patients who presented GPD or CA with the other group which did not present any of these variables; of these 28 patients, 20 (71.4%) died.

CONCLUSIONS

The presence of post-CA GPDs was associated with worse prognosis, but it was not clear whether these patterns are independent factors of an unfavorable evolution.

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This article proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification accuracy, barring the group of participants below the age of 70, for resting state EEG recorded during eyes open. The developed approach is generic and can be used as a powerful tool to examine brain network characteristics and disruption in a user friendly and systematic way.

Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach

INTRODUCTION

The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability.

METHODS

We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns.

RESULTS

Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (p<0.005) and correlated with independently identified visual EEG patterns such as generalized periodic discharges (p<0.02). Receiver operating characteristic (ROC) analysis confirmed the predictive value of lower state space velocity for poor clinical outcome after cardiac arrest (AUC 80.8, 70% sensitivity, 15% false positive rate).

CONCLUSION

Model-based quantitative EEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy.