Generalized periodic discharges and 'triphasic waves': A blinded evaluation of inter-rater agreement and clinical significance

Generalized Periodic Discharges Associated With Catatonia and Delirium: A Case Series

OBJECTIVE

Generalized periodic discharges are a repeated and generalized electroencephalography (EEG) pattern that can be seen in the context of altered mental status. This article describes a series of five individuals with generalized periodic discharges who demonstrated signs and symptoms of catatonia, a treatable neuropsychiatric condition.

METHODS

Inpatients with a clinical diagnosis of catatonia, determined with the Bush-Francis Catatonia Rating Scale (BFCRS), and EEG recordings with generalized periodic discharges were analyzed in a retrospective case series.

RESULTS

Five patients with catatonia and generalized periodic discharges on EEG were evaluated from among 106 patients with catatonia and contemporaneous EEG measurements. Four of these patients showed an improvement in catatonia severity when treated with benzodiazepines, with an average reduction of 6.75 points on the BFCRS.

CONCLUSIONS

Among patients with generalized periodic discharges, catatonia should be considered, in the appropriate clinical context. Patients with generalized periodic discharges and catatonia may benefit from treatment with empiric trials of benzodiazepines.

Delayed Diagnosis of an Invisible Seizure: Cefepime-Induced Non-convulsive Status Epilepticus

Cefepime-induced non-convulsive status epilepticus (NCSE) is a recognized adverse event of cefepime. Risk factors for this adverse event include older age, underlying renal dysfunction, previous brain injury, diabetes, and severe infection. We present a case of a 79-year-old woman with no prior seizure history, who was admitted for Pseudomonas aeruginosa surgical wound infection for which she was on cefepime. She developed acute encephalopathy with associated, occasional, right-sided myoclonic facial twitches 11 days into her admission. Electroencephalogram (EEG) confirmed NCSE as evident by epileptiform activity described as generalized periodic discharges with predominantly triphasic morphology. Cefepime was substituted with piperacillin-tazobactam> 24 hours after symptom onset. NCSE completely resolved two days after the discontinuation of cefepime. This case highlights the fact that NCSE can occur even when precautions such as renal dosing of cefepime are observed. Clinicians need to have a high index of suspicion for the condition when taking care of at-risk patients on cefepime, as delayed diagnosis correlates with potentially fatal outcomes.

Potential Neuroprotective Role of Melatonin in Sepsis-Associated Encephalopathy Due to Its Scavenging and Anti-Oxidative Properties

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. The brain is one of the organs involved in sepsis, and sepsis-induced brain injury manifests as sepsis-associated encephalopathy (SAE). SAE may be present in up to 70% of septic patients. SAE has a very wide spectrum of clinical symptoms, ranging from mild behavioral changes through cognitive disorders to disorders of consciousness and coma. The presence of SAE increases mortality in the population of septic patients and may lead to chronic cognitive dysfunction in sepsis survivors. Therefore, therapeutic interventions with neuroprotective effects in sepsis are needed. Melatonin, a neurohormone responsible for the control of circadian rhythms, exerts many beneficial physiological effects. Its anti-inflammatory and antioxidant properties are well described. It is considered a potential therapeutic factor in sepsis, with positive results from studies on animal models and with encouraging results from the first human clinical trials. With its antioxidant and anti-inflammatory potential, it may also exert a neuroprotective effect in sepsis-associated encephalopathy. The review presents data on melatonin as a potential drug in SAE in the wider context of the pathophysiology of SAE and the specific actions of the pineal neurohormone.

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

PURPOSE

To assess variability in interpretation of electroencephalogram (EEG) background activity and qualitative grading of cerebral dysfunction based on EEG findings, including which EEG features are deemed most important in this determination.

METHODS

A web-based survey (Qualtrics) was disseminated to electroencephalographers practicing in institutions participating in the Critical Care EEG Monitoring Research Consortium between May 2017 and August 2018. Respondents answered 12 questions pertaining to their training and EEG interpretation practices and graded 40 EEG segments (15-second epochs depicting patients' most stimulated state) using a 6-grade scale. Fleiss' Kappa statistic evaluated interrater agreement.

RESULTS

Of 110 respondents, 78.2% were attending electroencephalographers with a mean of 8.3 years of experience beyond training. Despite 83% supporting the need for a standardized approach to interpreting the degree of dysfunction on EEG, only 13.6% used a previously published or an institutional grading scale. The overall interrater agreement was fair ( k = 0.35). Having Critical Care EEG Monitoring Research Consortium nomenclature certification (40.9%) or EEG board certification (70%) did not improve interrater agreement ( k = 0.26). Predominant awake frequencies and posterior dominant rhythm were ranked as the most important variables in grading background dysfunction, followed by continuity and reactivity.

CONCLUSIONS

Despite the preference for a standardized grading scale for background EEG interpretation, the lack of interrater agreement on levels of dysfunction even among experienced academic electroencephalographers unveils a barrier to the widespread use of EEG as a clinical and research neuromonitoring tool. There was reasonable agreement on the features that are most important in this determination. A standardized approach to grading cerebral dysfunction, currently used by the authors, and based on this work, is proposed.

Differences in functional network between focal onset nonconvulsive status epilepticus and toxic metabolic encephalopathy: application to machine learning models for differential diagnosis

We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

Periodic discharges in veterinary electroencephalography-A visual review

First described in human EEG over 60 years ago, there are very few examples of periodic discharges in the veterinary literature. They are associated with a wide variety of etiologies, both intracranial and systemic, making interpretation challenging. Whether these patterns are indicative of ictal, interictal, or postictal activity is a matter of debate and may vary depending on the clinical features in an individual patient. Periodic discharges have a repeated waveform occurring at nearly regular intervals, with varying morphology of individual discharges from simple sharp waves or slow waves to more complex events. Amplitudes, frequencies, and morphologies of the discharges can fluctuate, occasionally evolving, or resolving over time. This study presents a visual review of several veterinary cases with periodic discharges on EEG similar to those described in human EEG, and discusses the current known pathophysiology of these discharges.

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.

Adult Critical Care Electroencephalography Monitoring for Seizures: A Narrative Review

Electroencephalography (EEG) is an important and relatively inexpensive tool that allows intensivists to monitor cerebral activity of critically ill patients in real time. Seizure detection in patients with and without acute brain injury is the primary reason to obtain an EEG in the Intensive Care Unit (ICU). In response to the increased demand of EEG, advances in quantitative EEG (qEEG) created an approach to review large amounts of data instantly. Finally, rapid response EEG is now available to reduce the time to detect electrographic seizures in limited-resource settings. This review article provides a concise overview of the technical aspects of EEG monitoring for seizures, clinical indications for EEG, the various available modalities of EEG, common and challenging EEG patterns, and barriers to EEG monitoring in the ICU.

Clinical Neurophysiology of Zika Virus Encephalitis

SUMMARY

Zika virus (ZIKV) has been shown to be highly neurotropic; neurologic disorders are a common complication of this infection. Encephalitis-an inflammation of the brain parenchyma associated with neurologic dysfunction-is a rare complication of ZIKV infections. It affects patients from young to elderly ages. Clinical presentation of ZIKV encephalitis may be heterogeneous, including altered mental status (decreased or altered level of consciousness, lethargy, or personality change), seizures, and focal deficits. Complementary diagnostic investigation should include neuroimaging, lumbar puncture, and EEG. Neuroimaging findings in ZIKV encephalitis are not specific and may be diverse, including normal findings, hyperintense lesions on MRI involving cortical or subcortical structures, symmetric or asymmetric lesions involving supra or infratentorial regions, and more widespread involvement such as brain swelling. A remarkable scarcity of neurophysiological data on ZIKV encephalitis was found in the literature. In line with other diagnostic examinations, there are no neurophysiological findings suggestive or specific of the disease. EEG in ZIKV encephalitis showed different results: normal or diffuse disorganization of background activity, asymmetry with abnormal focal slow waves, focal epileptic discharges or generalized spike-wave and multispike-wave complexes, and periods of generalized voltage attenuation.

Atypical or Typical Triphasic Waves-Is There a Difference? A Review

SUMMARY

The entity of triphasic waves (TWs) and TW encephalopathy has derived from the subjective art of EEG interpretation. Indeed, there are few if any guidelines regarding many different aspects of TWs. The authors seek to shed light on the nature and the diagnostic characteristics of various types of TWs, differentiating "typical" from "atypical" forms. The authors conclude that morphologies in the form of bursts of well-formed, smoothly contoured, negative-positive-negative, bilateral, symmetrical and synchronous, regular, reactive, periodic or rhythmic, 1.5 to 2.0 Hz, fronto-central, triphasic complexes with fronto-occipital lag meet the criteria for typical TWs and are highly suggestive of toxic-metabolic encephalopathies. These are most frequently hepatic, uremic, or sepsis-associated encephalopathies with multi-organ failure. In such cases, atypical TWs (frontopolar or parieto-occipital maximum, negative-positive or negative-positive-negative, asymmetric and asynchronous, unreactive, irregular, multifocal, continuous with spatiotemporal evolution, sharper and without fronto-occipital/occipito-frontal lag, or triphasic delta waves) are rarely seen. Atypical TWs are encountered in Angelman syndrome, toxic encephalopathies, hyperthyroidism/hypothyroidism, Hashimoto encephalopathy, nonconvulsive status epilepticus, dementia, sepsis-associated encephalopathy, cerebrovascular disorders, and certain boundary syndromes. Investigations describing TWs with uncommon etiologies revealed few with typical TWs, suggesting that the term "TWs" has been overused in the past. Triphasic waves arise from the interaction of multiple factors including toxic, metabolic, infectious, and structural disorders that affect circuits between thalamus and cortex. The patient's metabolic status, presence of potentially neurotoxic drugs, cerebral atrophy, white matter disease, dementia, or seizures help differentiate typical from typical TWs. Future studies will determine whether this dichotomy is heuristically and clinically helpful.

Status Triphasicus Versus Status Epilepticus?

SUMMARY

Generalized periodic patterns with triphasic wave morphology, long referred to as triphasic waves [TWs], had been associated with metabolic encephalopathies, although other neurologic and systemic causes have since been identified. In a recent classification of periodic patterns, TWs were formally grouped with the generalized periodic discharges, which are often associated with ictal activity. The interpretation of generalized periodic patterns with TWs as nonictal can have significant implications in the management of comatose patients in nonconvulsive status epilepticus. Electrographic characteristics that help distinguish nonictal periodic patterns with TWs from generalized periodic discharge ictal patterns include (1) TWs in long runs of periodic bilaterally synchronous and symmetric discharges, maximal in frontocentral or posterior head regions with and without a frontal-to-occipital lag or posterior-to-anterior lag, respectively; (2) recurrent spontaneous and/or low-dose benzodiazepine-induced attenuation and/or suppression of the periodic pattern and replacement with a diffuse slow wave activity throughout a prolonged EEG recording; and (3) stimulation-induced activation and/or increase in frequency and/or organization of TWs. We coined the term of status triphasicus to describe the electrographic periodic pattern of TWs with these three distinct characteristics. In this article, we discuss the advantages and limitations of keeping the status triphasicus pattern as a distinct electrographic entity different from periodic ictal generalized periodic discharge patterns. We discuss the circumstances in which a status triphasicus pattern can be associated with ictal activity and propose a simple pragmatic classification of status triphasicus that encompasses the different clinical scenarios it can be associated with.

An EEG Voyage in Search of Triphasic Waves-The Sirens and Corsairs on the Encephalopathy/EEG Horizon: A Survey of Triphasic Waves

SUMMARY

Generalized periodic discharges with triphasic wave (TW) morphology, long referred to as TWs, are typical of many toxic, metabolic, infectious, and cerebral structural problems, often in concert. Identifying TWs has been challenging for the electroencephalographer and clinician, as has been their cause, significance, prognosis, and treatment. This review highlights the many different patterns of TWs with commentary on their various causes and etiologies, characteristics, different morbidities, differentiation from nonconvulsive status epilepticus, and their prognosis. The articles in this Journal of Clinical Neurophysiology special issue on TWs will review the many challenges the clinician face when TWs are sighted.

Can We Distinguish Triphasic Waves From Other Generalized Periodic Discharges? Do We Need to?

SUMMARY

Triphasic waves are intuitively distinctive waveforms that fall under the umbrella of generalized periodic discharges. The ability to distinguish these waveforms consistently could be helpful if a specific underlying pathophysiology could be identified. However, scalp EEG and clinical observation have been limited in their ability to elucidate the underlying cortical physiology that leads to triphasic waves. Evidence from intracranial physiologic data and computational modeling suggest that these and other periodic discharges should be viewed not as strictly ictal nor non-ictal but rather on the spectrum between these two. Triphasic waves in particular appear to result from an abnormal balance between cortical excitation and synaptic transmission with input from functionally connected brain networks, such as the thalamocortical pathways involved in arousal. The practical implication of triphasic waves begins with acknowledgement of uncertainty and a rational approach should ask whether the pattern-or its treatment-might be creating harm.

Are Triphasic Waves Different From Generalized Spike-Wave Discharges?

SUMMARY

Generalized periodic discharges with triphasic morphology were previously referred to as triphasic waves but have now been subsumed into the ACNS classification as generalized periodic discharges. Although triphasic waves and generalized spike-wave complexes may resemble each other and hence may be incorrectly identified in comatose critically ill patients, many authors believe that there are different entities, with definable morphologic and clinical differences attributable to each waveform. The occurrence of both patterns in the same patient is extremely rare with only a single prior case report. Here the authors report a patient with typical triphasic waves and generalized spike-wave complexes and highlight the morphologic and EEG differences between the two patterns. The occurrence of both waveforms in the same EEG recording supports the notion of different cerebral generators and pathways, further differentiating rather than merging these morphologies.

Do Triphasic Waves and Nonconvulsive Status Epilepticus Arise From Similar Mechanisms? A Computational Model

PURPOSE

Triphasic waves arising in patients with toxic metabolic encephalopathy (TME) are often considered different from generalized periodic discharges (GPDs) in patients with generalized nonconvulsive status epilepticus (GNCSE). The primary objective of this study was to investigate whether a common mechanism can explain key aspects of both triphasic waves in TME and GPDs in GNCSE.

METHOD

A neural mass model was used for the simulation of EEG patterns in patients with acute hepatic encephalopathy, a common etiology of TME. Increased neuronal excitability and impaired synaptic transmission because of elevated ammonia levels in acute hepatic encephalopathy patients were used to explain how triphasic waves and GNCSE arise. The effect of gamma-aminobutyric acid-ergic drugs on epileptiform activity, simulated with a prolonged duration of the inhibitory postsynaptic potential, was also studied.

RESULTS

The simulations show that a model that includes increased neuronal excitability and impaired synaptic transmission can account for both the emergence of GPDs and GNCSE and their suppression by gamma-aminobutyric acid-ergic drugs.

CONCLUSIONS

The results of this study add to evidence from other studies calling into question the dichotomy between triphasic waves in TME and GPDs in GNCSE and support the hypothesis that all GPDs, including those arising in TME patients, occur via a common mechanism.

Outcomes of seizures, status epilepticus, and EEG findings in critically ill patient with COVID-19

OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a myriad of neurological manifestations and its effects on the nervous system are increasingly recognized. Seizures and status epilepticus (SE) are reported in the novel coronavirus disease (COVID-19), both new onset and worsening of existing epilepsy; however, the exact prevalence is still unknown. The primary aim of this study was to correlate the presence of seizures, status epilepticus, and specific critical care EEG patterns with patient functional outcomes in those with COVID-19.

METHODS

This is a retrospective, multicenter cohort of COVID-19-positive patients in Southeast Michigan who underwent electroencephalography (EEG) from March 12th through May 15th, 2020. All patients had confirmed nasopharyngeal PCR for COVID-19. EEG patterns were characterized per 2012 ACNS critical care EEG terminology. Clinical and demographic variables were collected by medical chart review. Outcomes were divided into recovered, recovered with disability, or deceased.

RESULTS

Out of the total of 4100 patients hospitalized with COVID-19, 110 patients (2.68%) had EEG during their hospitalization; 64% were male, 67% were African American with mean age of 63 years (range 20-87). The majority (70%) had severe COVID-19, were intubated, or had multi-organ failure. The median length of hospitalization was 26.5 days (IQR = 15 to 44 days). During hospitalization, of the patients who had EEG, 21.8% had new-onset seizure including 7% with status epilepticus, majority (87.5%) with no prior epilepsy. Forty-nine (45%) patients died in the hospital, 46 (42%) recovered but maintained a disability and 15 (14%) recovered without a disability. The EEG findings associated with outcomes were background slowing/attenuation (recovered 60% vs recovered/disabled 96% vs died 96%, p < 0.001) and normal (recovered 27% vs recovered/disabled 0% vs died 1%, p < 0.001). However, these findings were no longer significant after adjusting for severity of COVID-19.

CONCLUSION

In this large multicenter study from Southeast Michigan, one of the early COVID-19 epicenters in the US, none of the EEG findings were significantly correlated with outcomes in critically ill COVID-19 patients. Although seizures and status epilepticus could be encountered in COVID-19, the occurrence did not correlate with the patients' functional outcome.

Source Analysis of Triphasic Waves Using Quantitative Neuroimaging

PURPOSE

Triphasic waves (TWs), a common EEG pattern, are considered a subtype of generalized periodic discharges. Most patients with TWs present with an altered level of consciousness, and the TW pattern is believed to represent thalamocortical dysfunction. However, the exact meaning and mechanism of TWs remain unclear. The objective of the current study was to evaluate the source of TWs using EEG source imaging and computerized tomography.

METHODS

Twenty-eight patients with TWs were investigated. Source analysis was performed on the averaged TWs for each individual, and source maps were extracted. Normalization and automatic segmentation of gray matter were performed on computerized tomography scans before analysis. Finally, voxelwise correlation analyses were conducted between EEG source maps and gray matter volumes.

RESULTS

Source analyses showed that the anterior cingulate cortex was mainly involved in TWs (16/28 patients, 57%). Correlation analyses showed moderate positive and negative correlations between source location and gray matter volumes for the posterior cingulate (T = 2.85; volume = 6,533 mm3; r = 0.53; P = 0.002) and the superior frontal gyrus (T = 2.54; volume = 18,167 mm3; r = -0.48; P < 0.0001), respectively.

CONCLUSIONS

The results suggest that the anterior cingulate is involved in the origin of TWs. Furthermore, the volumes of posterior brain regions were positively correlated with TWs, indicating a possible preservation of these structures. Conversely, the volumes of anterior regions were negatively correlated with TWs. These findings may indicate a structural pattern necessary for the generation of the abnormal network responsible for TWs.

Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns

BACKGROUND

Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG.

NEW METHOD

We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters.

RESULTS

Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency.

COMPARISON WITH EXISTING METHODS

Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods.

CONCLUSIONS

Our results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.

Acute Toxicity and Triphasic Waves-The Example of Cefepime

PURPOSE

Triphasic waves (TWs) have been associated with a host of medication toxicities, and cefepime has emerged recently as a frequently encountered offending agent. This investigation aims to evaluate cefepime-induced encephalopathy and to report the associated clinical, EEG expression with TWs, and the radiologic findings.

METHODS

A retrospective multicenter observational study examining adult patients with cefepime-induced encephalopathy with generalized periodic discharges on either routine or continuous EEG between January 2014 and January 2020. Clinical, electrographic, and radiologic data were collected. Patients in whom cefepime was not the sole causative factor for their encephalopathy were excluded.

RESULTS

Twenty-seven patients with cefepime-induced encephalopathy marked by generalized periodic discharges with triphasic morphology were identified at both centers, whereas no patients were presenting with generalized periodic discharges without TWs. Patients had a median age of 63 years (interquartile range, 56-73). Fifty-six percent of the cohort (15 patients) were <65 years of age. Eighteen patients (67%) had either acute or chronic kidney impairment (either acute kidney injury or chronic kidney disease or both), whereas 81% had preexisting white matter disease on brain imaging. Of these, 14 patients (51%) were classified as either moderate or severe. In the majority of the patients, TWs were either state-dependent or stimulus-sensitive, and in one third of them presented only as stimulus-induced pattern. All patients improved with discontinuation of cefepime.

CONCLUSIONS

Cefepime toxicity should be considered in the differential diagnosis in encephalopathic patients with TWs. The presence of preexisting white matter disease in these patients should heighten the degree of suspicion, especially in younger patients and patients without renal dysfunction.

High-Frequency Oscillations in the Scalp EEG of Intensive Care Unit Patients With Altered Level of Consciousness

PURPOSE

In comatose patients, distinguishing between nonconvulsive status epilepticus and diffuse structural or metabolic encephalopathies is often challenging. Both conditions can generate periodic discharges on EEG with similar morphology and periodicity. We investigated the occurrence of high-frequency oscillations-potential biomarkers of epileptogenesis-on scalp EEG of comatose patients with periodic discharges in the EEG.

METHODS

Fifteen patients were included. Patients were divided into three groups, according to underlying etiology: Group 1, seizure related; group 2, structural; group 3, nonstructural. EEG recordings were compared with respect to the presence and rates of gamma (30-80 Hz) and ripples (80-250 Hz).

RESULTS

Patients were 23 to 106 years old (median, 68 years); 60% were female. 206 channels were eligible for analysis (median, 15 channels/patient). Overall, 43% of channels showed gamma, and 24% had ripples. Group 2 showed the highest proportion of channels with gamma (47%), followed by group 1 (38%) and group 3 (36%). Mean gamma rates were higher in group 2 (4.65 gamma/min/channel) than in group 1 (1.52) and group 3 (1.44) (P < 0.001). Group 2 showed the highest proportion of channels with ripples (29.2%), followed by group 1 (15%) and group 3 (24.2%). Mean ripple rates were higher in group 2 (5.09 ripple/min/channel) than in group 1 (0.96) and group 3 (0.83) (P < 0.001).

CONCLUSIONS

Fast oscillations, including high-frequency oscillations, can be detected in scalp EEG of patients with altered consciousness. High rates of fast activity may suggest an underlying structural brain lesion. Future studies are needed to determine whether fast oscillations in the setting of acute/subacute brain lesions are a biomarker of subsequent development of human epilepsy.

Markers in Status Epilepticus Prognosis

Status epilepticus (SE) is a neurologic emergency with high morbidity and mortality. The assessment of a patient's prognosis is crucial in making treatment decisions. In this review, we discuss various markers that have been used to prognosticate SE in terms of recurrence, mortality, and functional outcome. These markers include demographic, clinical, electrophysiological, biochemical, and structural data. The heterogeneity of SE etiology and semiology renders development of prognostic markers challenging. Currently, prognostication in SE is limited to a few clinical scores. Future research should integrate clinical, genetic and epigenetic, metabolic, inflammatory, and structural biomarkers into prognostication models to approach "personalized medicine" in prognostication of outcomes after SE.

Teaching Important Basic EEG Patterns of Bedside Electroencephalography to Critical Care Staffs: A Prospective Multicenter Study

BACKGROUND

Continuous electroencephalography (cEEG) is commonly recommended for neurocritical care patients. Routine implementation of such monitoring requires the specific training of professionals. The aim of this research was to evaluate the effectiveness of a training program on initiation of the basic interpretation of cEEG for critical care staff in a prospective multicenter study.

METHODS

After completion of a pretest, participants (senior physicians, fellows, residents, medical students, and nurses) recruited in six French ICUs participated in a face-to-face electroencephalogram (EEG) training program followed by additional e-learning sessions at day 1 (post-course), day 15, day 30, and day 90, based on training tests followed by illustrated and commented answers. Each test was designed to evaluate knowledge and skills through correct recognition of ten predefined EEG sequences covering the most common normal and abnormal patterns. The primary objective was to achieve a success rate > 80% correct answers at day 90 by at least 75% of the participants.

RESULTS

Among 250 participants, 77/108 (71.3%) who completed the full training program achieved at least 80% correct answers at day 90. Paired comparisons between the scores obtained at each evaluation showed an increase over time. The rate of correct answers at day 90 was > 80% for all common predefined EEG sequences, except for the recognition of periodic and burst-suppression patterns and reactivity, which were identified in only 42.6% (95% CI 36.4-48.8), 60.2% (54.1-66.3), and 70.4% (64.7-76.1) of the tests, respectively.

CONCLUSIONS

A training strategy for the basic interpretation of EEG in ICUs, consisting of a face-to-face EEG course supplemented with reinforcement of knowledge by e-learning, was associated with significant resignation and an effectiveness of training allowing 71% of learners to accurately recognize important basic EEG patterns encountered in critically ill patients.

TRIAL REGISTRATION

ClinicalTrials.gov number: NCT03545776.

Etiological assessment of status epilepticus

Status epilepticus (SE) is a potentially serious condition that can affect vital and functional prognosis and requires urgent treatment. Etiology is a determining factor in the patient's functional outcome and in almost half of all cases justifies specific treatment to stop progression. Therefore, identifying and addressing the cause of SE is a key priority in SE management. However, the etiology can be difficult to identify among acute and remote causes, which can also be multiple and interrelated. The most common etiologies are the discontinuation of antiepileptic medication in patients with a prior history of epilepsy, and acute brain aggression in cases of new onset SE (cerebrovascular pathologies are the most common). The list of remaining possible etiologies includes heterogeneous pathological contexts. Refractory SE and especially New-Onset Refractory Status Epilepticus (NORSE) lead to an extension of the etiological assessment in the search for encephalitis of autoimmune or infectious origin in adults and in children, as well as a genetic pathology in children in particular. This is an overview of current knowledge of SE etiologies and a pragmatic approach for carrying out an etiological assessment based on the following steps: - Which etiological orientation is identified according to the field and clinical presentation?; - Which etiologies to look for in an inaugural SE?; - Which first-line assessment should be carried out? The place of the biological, EEG and imaging assessment is discussed; - Which etiologies to look for in case of refractory SE?

Post-acute symptomatic seizure (PASS) clinic: A continuity of care model for patients impacted by continuous EEG monitoring

Objective

We present a model for the outpatient care of patients undergoing continuous electroencephalography (cEEG) monitoring during a hospitalization, named the post‐acute symptomatic seizure (PASS) clinic. We investigated whether establishing this clinic led to improved access to epileptologist care.

Methods

As part of the PASS clinic initiative, electronic health record (EHR) provides an automated alert to the inpatient care team discharging adults on first time antiepileptic drug (AED) after undergoing cEEG monitoring. The alert explains the rationale and facilitates scheduling for a PASS clinic appointment, three‐month after discharge, along with a same‐day extended (75 minutes) EEG. We compared the initial epilepsy clinic visits by patients undergoing cEEG in 2017, before (“Pre‐PASS” period and cohort) and after (“PASS” period and cohort) the alert went live in the EHR.

Results

Of the 170 patients included, 68 (40%) suffered a seizure during the mean follow‐up of 20.9 ± 10 months. AEDs were stopped or reduced in 66 out of 148 (44.6%) patients discharged on AEDs. Pre‐PASS cohort included 45 patients compared to 145 patients in the PASS cohort, accounting for 5.8% and 9.9% of patients, respectively, who underwent cEEG during the corresponding periods (odds ratio [OR] = 1.8, 95% CI = 1.26‐2.54, P = .001). The two cohorts did not differ in terms of electrographic or clinical seizures. The PASS cohort was significantly more likely to be followed up within 1‐6 months of discharge (OR = 4.6, 95% CI = 2.1‐10.1, P < .001) and have a pre‐clinic EEG (51.2% vs 11.1%; OR = 8.39, 95% CI = 3.1‐22.67, P < .001).

Significance

PASS clinic, a unique outpatient transition of care model for managing patients at risk of acute symptomatic seizure led to an almost twofold increase in access to an epileptologist. Future research should address the wide knowledge gap about the best post‐hospital discharge management practices for these patients.

Septic-Associated Encephalopathy: a Comprehensive Review

Septic-associated encephalopathy (SAE) is a key manifestation of sepsis, ranging from delirium to coma and occurring in up to 70% of patients admitted to the ICU. SAE is associated with higher ICU and hospital mortality, and also with poorer long-term outcomes, including cognitive and functional outcomes. The pathophysiology of SAE is complex, and it may involve neurotransmitter dysfunction, inflammatory and ischemic lesions to the brain, microglial activation, and blood-brain barrier dysfunction. Delirium (which is included in the SAE spectrum) is mostly diagnosed with validated scales in the ICU population. There is no established treatment for SAE; benzodiazepines should generally be avoided in this setting. Nonpharmacological prevention and management is key for treating SAE; it includes avoiding oversedation (mainly with benzodiazepines), early mobilization, and sleep promotion.

A novel neural computational model of generalized periodic discharges in acute hepatic encephalopathy

Acute hepatic encephalopathy (AHE) due to acute liver failure is a common form of delirium, a state of confusion, impaired attention, and decreased arousal. The electroencephalogram (EEG) in AHE often exhibits a striking abnormal pattern of brain activity, which epileptiform discharges repeat in a regular repeating pattern. This pattern is known as generalized periodic discharges, or triphasic-waves (TPWs). While much is known about the neurophysiological mechanisms underlying AHE, how these mechanisms relate to TPWs is poorly understood. In order to develop hypotheses how TPWs arise, our work builds a computational model of AHE (AHE-CM), based on three modifications of the well-studied Liley model which emulate mechanisms believed central to brain dysfunction in AHE: increased neuronal excitability, impaired synaptic transmission, and enhanced postsynaptic inhibition. To relate our AHE-CM to clinical EEG data from patients with AHE, we design a model parameter optimization method based on particle filtering (PF-POM). Based on results from 7 AHE patients, we find that the proposed AHE-CM not only performs well in reproducing important aspects of the EEG, namely the periodicity of triphasic waves (TPWs), but is also helpful in suggesting mechanisms underlying variation in EEG patterns seen in AHE. In particular, our model helps explain what conditions lead to increased frequency of TPWs. In this way, our model represents a starting point for exploring the underlying mechanisms of brain dynamics in delirium by relating microscopic mechanisms to EEG patterns.

Electro-Radiological Observations of Grade III/IV Hepatic Encephalopathy Patients with Seizures

BACKGROUND

Neurological complications in liver failure are common. Often under-recognized neurological complications are seizures and status epilepticus. These may go unrecognized without continuous electroencephalography (CEEG). We highlight the observed electro-radiological changes in patients with grade III/IV hepatic encephalopathy (HE) found to have seizures and/or status epilepticus on CEEG and the associated neuroimaging.

METHODS

This study was a retrospective review of patients with West Haven grade III/IV HE and seizures/status epilepticus on CEEG.

RESULTS

Eleven patients were included. Alcohol was the most common cause of HE (54.5%). All patients were either stuporous/comatose. The most common CEEG pattern was diffuse slowing (100%) followed by generalized periodic discharges (GPDs; 36.4%) and lateralized periodic discharges (LPDs, 36.4%). The subtype of GPDs with triphasic morphology was only seen in 27.3%. All seizures and/or status epilepticus were without clinical signs. Magnetic resonance imaging (MRI) was available in six patients. Cortical hyperintensities on diffusion weighted imaging sequence were seen in all six patients. One patient had CEEG seizure concomitantly with the MRI. Seven patients died prior to discharge.

CONCLUSION

Seizures or status epilepticus in the setting of HE were without clinical findings and could go unrecognized without CEEG. The finding of cortical hyperintensity on MRI should lead to further evaluation for unrecognized seizure or status epilepticus.

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring

PURPOSE

Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns.

METHODS

Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients.

RESULTS

Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68).

CONCLUSIONS

This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

Cefepime-induced encephalopathy: Neural mass modeling of triphasic wave-like generalized periodic discharges with a high negative component (Tri-HNC)

AIM

Cefepime, a fourth-generation cephalosporin, acts as a GABA_A receptor antagonist. Cefepime-induced encephalopathy (CIE) is frequently overlooked. We aimed to clarify the clinical features, characteristic electroencephalography (EEG), and mechanisms of CIE to aid in its early recognition.

METHODS

CIE cases documented by a single-center consultation-liaison team between April 2015 and March 2017 were retrospectively reviewed. For further investigation, neural mass modeling was performed in silico.

RESULTS

Three patients with CIE refused medication/examination and showed overt pain, palilalia, and much greater deterioration of eye and verbal response than the motor response, which was possibly related to GABAergic dysfunction. Triphasic wave-like generalized periodic discharges with a high negative component (Tri-HNC) were identified on the EEG of all three cases. The simulation reproduced the characteristic feature of 2-3 Hz Tri-HNC and recovery course on EEG, and a possible involvement of individual differences in pharmacological intervention. It also suggested that auto-inhibition (synaptic inputs from interneuron to interneuron) dysregulation contributed to generating Tri-HNC in CIE.

CONCLUSION

As CIE is iatrogenic and continues unless cefepime is stopped, early recognition is crucial. Physicians should be vigilant about altered mental status, pain, and verbal changes in patients taking cefepime. Tri-HNC on EEG can expedite the diagnosis of CIE, and the association between Tri-HNC and CIE suggests that an excitatory and inhibitory imbalance due to the dysfunction of GABAergic interneurons is the underlying mechanism. This modeling may offer a new method of investigating disorders related to GABAergic dysfunction.

Electroencephalography and delirium in the postoperative period

Delirium commonly manifests in the postoperative period as a clinical syndrome resulting from acute brain dysfunction or encephalopathy. Delirium is characterized by acute and often fluctuating changes in attention and cognition. Emergence delirium typically presents and resolves within minutes to hours after termination of general anaesthesia. Postoperative delirium hours to days after an invasive procedure can herald poor outcomes. Easily recognized when patients are hyperactive or agitated, delirium often evades diagnosis as it most frequently presents with hypoactivity and somnolence. EEG offers objective measurements to complement clinical assessment of this complex fluctuating disorder. Although EEG features of delirium in the postoperative period remain incompletely characterized, a shift of EEG power into low frequencies is a typical finding shared among encephalopathies that manifest with delirium. In aggregate, existing data suggest that serial or continuous EEG in the postoperative period facilitates monitoring of delirium development and severity and assists in detecting epileptic aetiologies. Future studies are needed to clarify the precise EEG features that can reliably predict or diagnose delirium in the postoperative period, and to provide mechanistic insights into this pathologically diverse neurological disorder.

Detecting abnormal electroencephalograms using deep convolutional networks

OBJECTIVES

Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of contextual factors, including age and sleep stage. Our objectives were to validate prior work on an independent data set suggesting that deep learning methods can discriminate between normal vs abnormal EEGs, to understand whether age and sleep stage information can improve discrimination, and to understand what factors lead to errors.

METHODS

We train a deep convolutional neural network on a heterogeneous set of 8522 routine EEGs from the Massachusetts General Hospital. We explore several strategies for optimizing model performance, including accounting for age and sleep stage.

RESULTS

The area under the receiver operating characteristic curve (AUC) on an independent test set (n = 851) is 0.917 marginally improved by including age (AUC = 0.924), and both age and sleep stages (AUC = 0.925), though not statistically significant.

CONCLUSIONS

The model architecture generalizes well to an independent dataset. Adding age and sleep stage to the model does not significantly improve performance.

SIGNIFICANCE

Insights learned from misclassified examples, and minimal improvement by adding sleep stage and age suggest fruitful directions for further research.

Reevaluation of the Critically Ill Patients With Nonconvulsive Status Epilepticus by Using Salzburg Consensus Criteria

OBJECTIVE

We aimed to assess the usefulness of the Salzburg Consensus Criteria (SCC) for determining the prognosis of critically ill patients with nonconvulsive status epilepticus (NCSE).

METHODS

We retrospectively reviewed consecutive patients with unconsciousness followed up in the intensive care unit (ICU). Three clinical neurophysiologists, one of them blinded to clinical and laboratory data, reevaluated all EEG data independently and determined NCSE according to SCC. The incidence of NCSE and ictal EEG patterns and their relationship to clinical, laboratory, neuroradiological, and prognostic findings were assessed.

RESULTS

A total of 107 consecutive patients with mean age 68.2 ± 15.3 years (57 females) were enrolled in the study. Primary neuronal injury was detected in 59 patients (55.7%). Thirty-three patients (30.8%) were diagnosed as NCSE. While authors decided to treat 33 patients (30.8%), 32 patients (29.9%) had been treated in real-life evaluation. Clinical and EEG improvement were detected in 12 patients (11.3%) in real-life treatment group showing correlation with lack of intubation and ICU stay related to postsurgical event. Rate of mortality (45.8%) was high showing association with systemic-metabolic etiology, severity of coma and presence of "plus" modifiers in the EEG.

CONCLUSION AND SIGNIFICANCE

Our findings suggest that SCC is highly compatible with clinical practice in the decision for treatment of patients with NCSE. The presence of "plus" modifiers in the EEG was found to be associated with mortality in these patients and was a significant marker for the high mortality rate.

Health Care Apps Reported in Newspapers: Content Analysis

BACKGROUND

Newspapers are considered one of the most viewed and influential media sources in both the United Kingdom and United States. However, information about how newspapers portray health care apps to the readers has been lacking.

OBJECTIVE

This study investigated the reporting on health care apps in newspapers published in the United Kingdom and United States.

METHODS

The Nexis UK database was used to identify and select relevant articles. Systematic content analysis of the articles that met the inclusion criteria (articles of any format that contained reference to health care apps or medical apps) within the highest circulated newspapers in the United Kingdom and United States over a period of 10 years (2006-2015) was conducted. Interrater reliability of coding was established using a 10% sample of the chosen articles.

RESULTS

A total of 220 (151 UK and 69 US) relevant newspaper articles were retrieved. Health care apps were most frequently reported on in the Daily Mail and The Guardian (UK newspapers) and in the New York Times and the Washington Post (US newspapers). An exponential rise in published scientific articles (PubMed) on health care-related apps was noted during the study period. A total of 26.4% (58/220) and 19.1% (42/220) of the retrieved newspaper articles appeared in the features and main news sections, respectively. General information about health care apps was the main theme coved by the newspapers (45.9%, 101/220). Most of the articles represented a societal point of view (72.3%, 159/220). The main focus of the articles was on general health matters (48.2%, 106/220) and specific disease matters (36.8%, 81/220). Diabetes was the most frequently mentioned disease in the articles. A high proportion (91.4%, 201/220) of the articles mentioned benefits of using health care apps mainly for personalized care, whereas 24.1% (53/220) of the articles commented on related risks such as anxiety and confidentiality issues. Almost half (45.9%, 101/220) of the articles mentioned potential facilitators to the use of apps; less than 10% (16/220) discussed barriers. Most of the articles (83.6%, 184/220) were judged as having balanced judgment on the present topic and more than half (60.0%, 132/220) of the articles were judged to be of generally low quality.

CONCLUSIONS

Health care apps were not widely reported in newspaper articles in the United Kingdom and United States over the study period; however, there appeared to be much more recent interest. Characteristically, the articles focused more frequently on societal impact and on general health rather than on disease-specific apps.

Continuous Electroencephalography in the Critically Ill: Clinical and Continuous Electroencephalography Markers for Targeted Monitoring

PURPOSE

Electrographic seizures detected by continuous electroencephalography (CEEG) in critically ill patients with altered mental status is becoming increasingly recognized. Data guiding the appropriate selection of patients to be monitored on CEEG are lacking. The aims of this article were to study the incidence of seizures in the critical care setting and to evaluate for clinical predictors to improve the efficiency of CEEG monitoring.

METHODS

Retrospective review of the CEEG and clinical data on 1,123 consecutive patients who had continuous video EEG over a 24-month period.

RESULTS

Seizures were recorded in 215 patients on CEEG monitoring (19.1%). In total, 89.3% of these seizures occurred without clinical signs. Patients who were in a coma were more likely to have EEG seizures (odds ratio, 3.64; 95% confidence interval, 2.23-5.95) compared with those awake. The incidence of seizures was overrepresented in patients with extra-axial tumors (41.9%), multiple sclerosis (35.7%), and intra-axial tumors (33.0%). Lateralized periodic discharges were predictive (odds ratio, 8.27; 95% confidence interval, 5.52-12.46) of seizure occurrence compared with those with no epileptiform patterns. Only generalized periodic discharges with triphasic morphology had no increased odds of seizure (odds ratio, 1.02; 95% confidence interval, 0.24-3.03). When present, electroencephalography seizures were detected within 24 hours in 92% of monitored patients.

CONCLUSIONS

Continuous electroencephalography monitoring in the critical care setting demonstrates a linear increase in seizure incidence with declining mental status. Recognizing clinical conditions and electroencephalography markings may help in the appropriate selection of critically ill patients for CEEG monitoring.

Definition and Classification of Periodic and Rhythmic Patterns

The growing use of continuous video-EEG recording in the inpatient setting, in particular in patients with varying degrees of encephalopathy, has yielded a window to the brain with an excellent temporal resolution. This increasingly available tool has become more than an instrument to detect nonconvulsive seizures (its primary use), and clinical indications span from ischemia detection in acute brain injuries, neuroprognostication of comatose patients, to monitoring the degree of encephalopathy. In this context, abnormal findings such as periodic discharges and rhythmic delta activity were increasingly recognized; however, significant subjectivity remained in the interpretation of these findings pertaining to key features regarding their spatial involvement, prevalence of occurrence, duration, associated morphologic features, and behavior. In 2005, the American Clinical Neurophysiology Society proposed standardized definitions and classification of electroencephalographic rhythmic and periodic patterns. This was subsequently revised in 2011 and in 2012 and is now being used by centers worldwide, with the final version published in early 2013 as an official guideline of the ACNS. The resulting uniform terminology has allowed for significant advances in the understanding of the pathophysiology, epileptogenic potential, and overall clinical implication of these patterns. Investigators across multiple institutions are now able to collaborate while exploring diagnostic and therapeutic algorithms to these patterns, an effort that may soon provide definitive evidence guiding treating clinicians on the management of these patients.

Admission EEG findings in diverse paediatric cerebral malaria populations predict outcomes

BACKGROUND

Electroencephalography at hospital presentation may offer important insights regarding prognosis that can inform understanding of cerebral malaria (CM) pathophysiology and potentially guide patient selection and risk stratification for future clinical trials. Electroencephalogram (EEG) findings in children with CM in Uganda and Malawi were compared and associations between admission EEG findings and outcome across this diverse population were assessed. Demographic, clinical and admission EEG data from Ugandan and Malawian children admitted from 2009 to 2012 with CM were gathered, and survivors assessed for neurological abnormalities at discharge.

RESULTS

281 children were enrolled (Uganda n = 122, Malawi n = 159). The Malawian population was comprised only of retinopathy positive children (versus 72.5% retinopathy positive in Uganda) and were older (4.2 versus 3.7 years; p = 0.046), had a higher HIV prevalence (9.0 versus 2.8%; p = 0.042), and worse hyperlactataemia (7.4 versus 5.2 mmol/L; p < 0.001) on admission compared to the Ugandan children. EEG findings differed between the two groups in terms of average voltage and frequencies, reactivity, asymmetry, and the presence/absence of sleep architecture. In univariate analyses pooling EEG and outcomes data for both sites, higher average and maximum voltages, faster dominant frequencies, and retained reactivity were associated with survival (all p < 0.05). Focal slowing was associated with death (OR 2.93; 95% CI 1.77-7.30) and a lower average voltage was associated with neurological morbidity in survivors (p = 0.0032).

CONCLUSIONS

Despite substantial demographic and clinical heterogeneity between subjects in Malawi and Uganda as well as different EEG readers at each site, EEG findings on admission predicted mortality and morbidity. For CM clinical trials aimed at decreasing mortality or morbidity, EEG may be valuable for risk stratification and/or subject selection.

An update on the recognition and treatment of non-convulsive status epilepticus in the intensive care unit

INTRODUCTION

Non-convulsive status epilepticus (NCSE) is a complex and diverse condition which is often an under-recognised entity in the intensive care unit. When NCSE is identified the optimal treatment strategy is not always clear. Areas covered: This review is based on a literature review of the key literature in the field over the last 5-10 years. The articles were selected based on their importance to the field by the authors. Expert commentary: This review discusses the complex situations when a neurological consultation may occur in a critical care setting and provides an update on the latest evidence regarding the recognition of NCSE and the decision making around determining the aggressiveness of treatment. It also considers the ictal-interictal continuum of conditions which may be met with, particularly in the era of continuous EEG, and provides an approach for dealing with these. Suggestions for how the field will develop are discussed.

Population of the ictal-interictal zone: The significance of periodic and rhythmic activity

Seizures contribute to patient mortality and are usually treated aggressively. Rhythmic and periodic patterns - the "ictal-interictal continuum" - are often associated with seizures, yet the optimum method of treating these patterns is not known: should they be aggressively suppressed, or monitored without treatment? Understanding which patterns are more strongly associated with seizures and which are highly associated with mortality is important to help the clinician decide how to treat these findings. We present an overview of the etiologies, association with seizures, and mortality of periodic and rhythmic patterns, and one approach to treatment.

EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest

OBJECTIVE

Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures.

METHODS

94 comatose patients with EEG within 24h after CA were included. Clinical outcome was assessed at 3months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures×2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients.

RESULTS

The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3-5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81.

CONCLUSION

Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power.

SIGNIFICANCE

Quantitative methods might increase the prognostic yield of currently used multi-modal approaches.

Continuous Electroencephalography (cEEG) Monitoring and Outcomes of Critically Ill Patients

Background

It is not clear whether performing continuous EEG (cEEG) in critically ill patients during intensive care unit (ICU) treatment affects outcomes at discharge.

Material/Methods

We prospectively matched 234 patients who received cEEG (cases) by admission diagnosis and sex to 234 patients who did not receive cEEG (controls) and followed them until discharge. Patients admitted due to seizures were excluded. The primary measures of outcome were Glasgow Coma Scale at Discharge (GCSD) and disposition at discharge, and the secondary measures of outcome were AED modifications, Glasgow Outcomes Scale, and Modified-Rankin Scale. These outcomes were compared between the cases and controls.

Results

Some differences in primary outcome measures between the groups emerged on univariate analyses, but these differences were small and not significant after controlling for covariates. Cases had longer ICU stays (p=0.002) and lower admission GCS (p=0.01) but similar GCSD (p=0.10). Of the secondary outcome measures, the mean (SD) number of AED modifications for cases was 2.2±3.1 compared to 0.4±0.8 for controls (p<0.0001); 170 (72.6%) cases had at least 1 AED modification compared to only 56 (24.1%) of the controls (p<0.0001).

Conclusions

Performing cEEG did not improve discharge outcome but it significantly influenced AED prescription patterns. Further studies assessing long-term outcomes are needed to better define the role of cEEG in this patient population.