Developing a Standardized Approach to Grading the Level of Brain Dysfunction on EEG

Myoclonus After Cardiac Arrest did not Correlate with Cortical Response on Somatosensory Evoked Potentials

PurposeMyoclonus after anoxic brain injury is a marker of significant cerebral injury. Absent cortical signal (N20) on somatosensory evoked potentials (SSEPs) after cardiac arrest is a reliable predictor of poor neurological recovery when combined with an overall clinical picture consistent with severe widespread neurological injury. We evaluated a clinical question of if SSEP result could be predicted from other clinical and neurodiagnostic testing results in patients with post-anoxic myoclonus.MethodsRetrospective chart review of all adult patients with post-cardiac arrest myoclonus who underwent both electroencephalographic (EEG) monitoring and SSEPs for neuroprognostication. Myoclonus was categorized as "non-myoclonic movements," "myoclonus not captured on EEG," "myoclonus without EEG correlate," "myoclonus with EEG correlate," and "status myoclonus." SSEP results were categorized as all absent, all present, N18 and N20 absent bilaterally, and N20 only absent bilaterally. Cox proportional hazards with censoring was used to evaluate the association of myoclonus category, SSEP results, and confounding factors with survival.ResultsIn 56 patients, median time from arrest to either confirmed death or last follow up was 9 days. The category of myoclonus was not associated with SSEP result or length of survival. Absence of N20 s or N18 s was associated with shorter survival (N20 hazard ratio [HR] 4.4, p = 0.0014; N18 HR 5.5, p < 0.00001).ConclusionsCategory of myoclonus did not reliably predict SSEP result. SSEP result was correlated with outcome consistently, but goals of care transitioned to comfort measures only in all patients with present peripheral potentials and either absent N20 s only or absence of N18 s and N20 s. Our results suggest that SSEPs may retain prognostic value in patients with post-anoxic myoclonus.

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.

EEG in Epilepsy

OBJECTIVE

The purpose of this article is to review the fundamentals and limitations of EEG, guide the selection of EEG type to answer clinical questions, and provide instruction on the interpretation of results within the patient's clinical context.

LATEST DEVELOPMENTS

EEG is the single most useful ancillary test to support the clinical diagnosis of epilepsy, but if used incorrectly it can cause great harm. Misapplication of EEG findings can lead to misdiagnosis and long-term mental and physical health sequelae. Although all neurologists may not have sufficient training for independent EEG interpretation, most should be able to review and apply the findings from the report accurately to guide patient care. Longer-term EEGs with similar recording electrodes tend to have higher diagnostic yields. Common EEG findings are described in this article, along with diagnostic limitations of some classically described patterns. There is an updated definition for an epileptiform discharge, along with a consensus on EEG patterns in the critically ill.

ESSENTIAL POINTS

EEG continues to be the most useful ancillary test to assist in the diagnosis of epilepsy. Its application requires proper understanding of its limitations and variability of testing results.

Retrospective analysis of amantadine response and predictive factors in intensive care unit patients with non-traumatic disorders of consciousness

Background

Disorders of consciousness (DoC) in non-traumatic ICU-patients are often treated with amantadine, although evidence supporting its efficacy is limited.

Methods

This retrospective study analyzed non-traumatic DoC-patients treated with amantadine between January 2016 and June 2021. Data on patient demographics, clinical characteristics, treatment specifications, and outcomes were extracted from electronic medical records. Patients were classified as responders if their Glasgow Coma Scale (GCS) improved by ≥3 points within 5 days. Good outcome was defined as a modified Rankin Scale (mRS) of 0-2. Machine learning techniques were used to predict response to treatment.

Results

Of 442 patients (mean age 73.2 ± 10.7 years, 41.0% female), 267 (60.4%) were responders. Baseline characteristics were similar between groups, except that responders had lower baseline GCS (7 [IQR 5-9] vs. 8 [IQR 5-10], p = 0.030), better premorbid mRS (2 [IQR 1-2] vs. 2 [IQR 1-3], p < 0.001) and fewer pathological cerebral imaging findings (45.7% vs. 61.1%, OR 0.56, 95% CI: 0.36-0.86, p = 0.008). Responders exhibited significantly lower mortality at discharge (13.5% vs. 27.4%, OR 0.41, 95% CI: 0.25-0.67, p < 0.001) and follow-up (16.9% vs. 32.0%, OR 0.43, 95% CI: 0.24-0.77, p = 0.002). Good outcomes were more frequent in responders at follow-up (4.9% vs. 1.1%, OR 6.14, 95% CI: 1.35-28.01, p = 0.004). In multivariate analysis higher premorbid mRS (OR 0.719, 95% CI 0.590-0.875, p < 0.001), pathological imaging results (OR 0.546, 95% CI 0.342-0.871, p = 0.011), and experiencing cardiac arrest (OR 0.542, 95% CI 0.307-0.954, p = 0.034) were associated with lower odds of response. Machine learning identified key predictors of response, with the Stacking Classifier achieving the highest performance (accuracy 64.5%, precision 66.6%, recall 64.5%, F1 score 61.3%).

Conclusion

This study supports the potential benefits of intravenous amantadine in non-traumatic DOC-patients. Higher premorbid mRS, and pathological cerebral imaging were key predictors of non-response, offering potential avenues for patient selection and treatment customization. Findings from this study informed the design of our ongoing prospective study, which aims to further evaluate the long-term efficacy of amantadine.

Current Challenges in Neurocritical Care: A Narrative Review

Neurocritical care as a field aims to treat patients who are neurologically critically ill due to a variety of pathologies. As a recently developed subspecialty, the field faces challenges, several of which are outlined in this review. The authors discuss aneurysmal subarachnoid hemorrhage, status epilepticus, and traumatic brain injury as specific disease processes with opportunities for growth in diagnosis, management, and treatment, as well as disorders of consciousness that can arise as a result of many neurological injuries. They also address logistical challenges, such as the need for specialized resources needed to successfully run a neurosciences intensive care unit (neuro-ICU), the variations in training of those who staff neuro-ICUs, and different interdisciplinary team structures. Although an immense amount of data is collected in the neuro-ICU, leveraging the data for clinical research is an area with room for further innovation. Additionally, developing accurate basic science models for these disease processes is an ongoing area of exploration. Finally, the authors explore psychosocial challenges present in the care of neurologically critically ill patients, including limitations in prognostication and religious and cultural perceptions of brain death.

Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study

Background

Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).

Methods

We collected video data linked to electroencephalograms (video-EEG) from infants with corrected age less than 1 year at Mount Sinai Hospital in New York City, a level four urban NICU between February 1, 2021 and December 31, 2022. We trained a deep learning pose recognition algorithm on video feeds, labeling 14 anatomic landmarks in 25 frames/infant. We then trained classifiers on anatomic landmarks to predict cerebral dysfunction, diagnosed from EEG readings by an epileptologist, and sedation, defined by the administration of sedative medications.

Findings

We built the largest video-EEG dataset to date (282,301 video minutes, 115 infants) sampled from a diverse patient population. Infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants with respective receiver operating characteristic area under the curves (ROC-AUCs) 0.94, 0.83, 0.89. Median movement increased with age and, after accounting for age, was lower with sedative medications and in infants with cerebral dysfunction (all P < 5 × 10-3, 10,000 permutations). Sedation prediction had high performance on cross-validation, held-out intervals, and held-out infants (ROC-AUCs 0.90, 0.91, 0.87), as did prediction of cerebral dysfunction (ROC-AUCs 0.91, 0.90, 0.76).

Interpretation

We show that pose AI can be applied in an ICU setting and that an EEG diagnosis, cerebral dysfunction, can be predicted from video data alone. Deep learning with pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.

Funding

Friedman Brain Institute Fascitelli Scholar Junior Faculty Grant and Thrasher Research Fund Early Career Award (F.R.). The Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463.

Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG

Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.

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.