Electroencephalogram Monitoring in Critical Care

Status epilepticus: what's new for the intensivist

PURPOSE OF REVIEW

Status epilepticus (SE) is a common neurologic emergency affecting about 36.1/100 000 person-years that frequently requires intensive care unit (ICU) admission. There have been advances in our understanding of epidemiology, pathophysiology, and EEG monitoring of SE, and there have been large-scale treatment trials, discussed in this review.

RECENT FINDINGS

Recent changes in the definitions of SE have helped guide management protocols and we have much better predictors of outcome. Observational studies have confirmed the efficacy of benzodiazepines and large treatment trials indicate that all routinely used second line treatments (i.e., levetiracetam, valproate and fosphenytoin) are equally effective. Better understanding of the pathophysiology has indicated that nonanti-seizure medications aimed at underlying pathological processes should perhaps be considered in the treatment of SE; already immunosuppressant treatments are being more widely used in particular for new onset refractory status epilepticus (NORSE) and Febrile infection-related epilepsy syndrome (FIRES) that sometimes revealed autoimmune or paraneoplastic encephalitis. Growing evidence for ICU EEG monitoring and major advances in automated analysis of the EEG could help intensivist to assess the control of electrographic seizures.

SUMMARY

Research into the morbi-mortality of SE has highlighted the potential devastating effects of this condition, emphasizing the need for rapid and aggressive treatment, with particular attention to cardiorespiratory and neurological complications. Although we now have a good evidence-base for the initial status epilepticus management, the best treatments for the later stages are still unclear and clinical trials of potentially disease-modifying therapies are long overdue.

Classification of cognitive impairment in older adults based on brain functional state measurement data via hierarchical clustering analysis

Introduction

Cognitive impairment (CI) is a common degenerative condition in the older population. However, the current methods for assessing CI are not based on brain functional state, which leads to delayed diagnosis, limiting the initiatives towards achieving early interventions.

Methods

A total of one hundred and forty-nine community-dwelling older adults were recruited. Montreal Cognitive Assessment (MoCA) and Mini-Mental State Exam (MMSE) were used to screen for CI, while brain functional was assessed by brain functional state measurement (BFSM) based on electroencephalogram. Bain functional state indicators associated with CI were selected by lasso and logistic regression models (LRM). We then classified the CI participants based on the selected variables using hierarchical clustering analysis.

Results

Eighty-one participants with CI detected by MoCA were divided into five groups. Cluster 1 had relatively lower brain functional states. Cluster 2 had highest mental task-switching index (MTSi, 13.7 ± 3.4), Cluster 3 had the highest sensory threshold index (STi, 29.9 ± 7.7), Cluster 4 had high mental fatigue index (MFi) and cluster 5 had the highest mental refractory period index (MRPi), and external apprehension index (EAi) (21.6 ± 4.4, 35.4 ± 17.7, respectively). Thirty-three participants with CI detected by MMSE were divided into 3 categories. Cluster 1 had the highest introspective intensity index (IIi, 63.4 ± 20.0), anxiety tendency index (ATi, 67.2 ± 13.6), emotional resistance index (ERi, 50.2 ± 11.9), and hypoxia index (Hi, 41.8 ± 8.3). Cluster 2 had the highest implicit cognitive threshold index (ICTi, 87.2 ± 12.7), and cognitive efficiency index (CEi, 213.8 ± 72.0). Cluster 3 had higher STi. The classifications both showed well intra-group consistency and inter-group variability.

Conclusion

In our study, BFSM-based classification can be used to identify clinically and brain-functionally relevant CI subtypes, by which clinicians can perform personalized early rehabilitation.

Seeking the Light in Intensive Care Unit Sedation: The Optimal Sedation Strategy for Critically Ill Patients

The clinical approach to sedation in critically ill patients has changed dramatically over the last two decades, moving to a regimen of light or non-sedation associated with adequate analgesia to guarantee the patient’s comfort, active interaction with the environment and family, and early mobilization and assessment of delirium. Although deep sedation (DS) may still be necessary for certain clinical scenarios, it should be limited to strict indications, such as mechanically ventilated patients with Acute Respiratory Distress Syndrome (ARDS), status epilepticus, intracranial hypertension, or those requiring target temperature management. DS, if not indicated, is associated with prolonged duration of mechanical ventilation and ICU stay, and increased mortality. Therefore, continuous monitoring of the level of sedation, especially when associated with the raw EEG data, is important to avoid unnecessary oversedation and to convert a DS strategy to light sedation as soon as possible. The approach to the management of critically ill patients is multidimensional, so targeted sedation should be considered in the context of the ABCDEF bundle, a holistic patient approach. Sedation may interfere with early mobilization and family engagement and may have an impact on delirium assessment and risk. If adequately applied, the ABCDEF bundle allows for a patient-centered, multidimensional, and multi-professional ICU care model to be achieved, with a positive impact on appropriate sedation and patient comfort, along with other important determinants of long-term patient outcomes.

Magnetic Resonance Imaging Adds Prognostic Value to EEG After Pediatric Cardiac Arrest

AIM

To investigate how combined electrographic and radiologic data inform outcomes in children after cardiac arrest.

METHODS

Retrospective observational study of children admitted to the pediatric intensive care unit (PICU) of a tertiary children's hospital with diagnosis of cardiac arrest from 2009 to 2016. The first 20 min of electroencephalogram (EEG) background was blindly scored. Presence and location of magnetic resonance imaging (MRI) diffusion-weighted image (DWI) abnormalities were correlated with T2-weighted signal. Outcomes were categorized using Pediatric Cerebral Performance Category (PCPC) scores at hospital discharge, with "poor outcome" reflecting a PCPC score of 4-6. Logistic regression models examined the association of EEG and MRI variables with outcome.

RESULTS

41 children met inclusion criteria and had both post-arrest EEG monitoring within 72 hours after ROSC and brain MRI performed within 8 days. Among the 19 children with poor outcome, 10 children did not survive to discharge. Severely abnormal EEG background (p < 0.0001) and any diffusion restriction (p < 0.0001) were associated with poor outcome. The area under the ROC curve (AUC) for identifying outcome based on EEG background alone was 0.86, which improved to 0.94 with combined EEG and MRI data (p = 0.02).

CONCLUSION

Diffusion abnormalities on MRI within 8 days after ROSC add to the prognostic value of EEG background in children surviving cardiac arrest.