The Ictal-Interictal Continuum: To Treat or Not to Treat (and How)?

Emergent Management of Status Epilepticus

OBJECTIVE

Status epilepticus is a neurologic emergency that can be life- threatening. The key to effective management is recognition and prompt initiation of treatment. Management of status epilepticus requires a patient-specific-approach framework, consisting of four axes: (1) semiology, (2) etiology, (3) EEG correlate, and (4) age. This article provides a comprehensive overview of status epilepticus, highlighting the current treatment approaches and strategies for management and control.

LATEST DEVELOPMENTS

Administering appropriate doses of antiseizure medication in a timely manner is vital for halting seizure activity. Benzodiazepines are the first-line treatment, as demonstrated by three randomized controlled trials in the hospital and prehospital settings. Benzodiazepines can be administered through IV, intramuscular, rectal, or intranasal routes. If seizures persist, second-line treatments such as phenytoin and fosphenytoin, valproate, or levetiracetam are warranted. The recently published Established Status Epilepticus Treatment Trial found that all three of these drugs are similarly effective in achieving seizure cessation in approximately half of patients. For cases of refractory and super-refractory status epilepticus, IV anesthetics, including ketamine and γ-aminobutyric acid-mediated (GABA-ergic) medications, are necessary. There is an increasing body of evidence supporting the use of ketamine, not only in the early phases of stage 3 status epilepticus but also as a second-line treatment option.

ESSENTIAL POINTS

As with other neurologic emergencies, "time is brain" when treating status epilepticus. Antiseizure medication should be initiated quickly to achieve seizure cessation. There is a need to explore newer generations of antiseizure medications and nonpharmacologic modalities to treat status epilepticus.

Improving Clinician Performance in Classifying EEG Patterns on the Ictal-Interictal Injury Continuum Using Interpretable Machine Learning

BACKGROUND

In intensive care units (ICUs), critically ill patients are monitored with electroencephalography (EEG) to prevent serious brain injury. EEG monitoring is constrained by clinician availability, and EEG interpretation can be subjective and prone to interobserver variability. Automated deep-learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, existing uninterpretable (black-box) deep-learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of both trust and adoption by clinicians.

METHODS

We developed an interpretable deep-learning system that accurately classifies six patterns of potentially harmful EEG activity - seizure, lateralized periodic discharges (LPDs), generalized periodic discharges (GPDs), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), and other patterns - while providing faithful case-based explanations of its predictions. The model was trained on 50,697 total 50-second continuous EEG samples collected from 2711 patients in the ICU between July 2006 and March 2020 at Massachusetts General Hospital. EEG samples were labeled as one of the six EEG patterns by 124 domain experts and trained annotators. To evaluate the model, we asked eight medical professionals with relevant backgrounds to classify 100 EEG samples into the six pattern categories - once with and once without artificial intelligence (AI) assistance - and we assessed the assistive power of this interpretable system by comparing the diagnostic accuracy of the two methods. The model's discriminatory performance was evaluated with area under the receiver-operating characteristic curve (AUROC) and area under the precision-recall curve. The model's interpretability was measured with task-specific neighborhood agreement statistics that interrogated the similarities of samples and features. In a separate analysis, the latent space of the neural network was visualized by using dimension reduction techniques to examine whether the ictal-interictal injury continuum hypothesis, which asserts that seizures and seizure-like patterns of brain activity lie along a spectrum, is supported by data.

RESULTS

The performance of all users significantly improved when provided with AI assistance. Mean user diagnostic accuracy improved from 47 to 71% (P<0.04). The model achieved AUROCs of 0.87, 0.93, 0.96, 0.92, 0.93, and 0.80 for the classes seizure, LPD, GPD, LRDA, GRDA, and other patterns, respectively. This performance was significantly higher than that of a corresponding uninterpretable black-box model (with P<0.0001). Videos traversing the ictal-interictal injury manifold from dimension reduction (a two-dimensional representation of the original high-dimensional feature space) give insight into the layout of EEG patterns within the network's latent space and illuminate relationships between EEG patterns that were previously hypothesized but had not yet been shown explicitly. These results indicate that the ictal-interictal injury continuum hypothesis is supported by data.

CONCLUSIONS

Users showed significant pattern classification accuracy improvement with the assistance of this interpretable deep-learning model. The interpretable design facilitates effective human-AI collaboration; this system may improve diagnosis and patient care in clinical settings. The model may also provide a better understanding of how EEG patterns relate to each other along the ictal-interictal injury continuum. (Funded by the National Science Foundation, National Institutes of Health, and others.).

Ictal-Interictal Continuum in the Pediatric Intensive Care Unit

BACKGROUND

The ictal-interictal continuum (IIC) consists of several electroencephalogram (EEG) patterns that are common in critically ill adults. Studies focused on the IIC are limited in critically ill children and have focused primarily on associations with electrographic seizures (ESs). We report the incidence of the IIC in the pediatric intensive care unit (PICU). We then compare IIC patterns to rhythmic and periodic patterns (RPP) not meeting IIC criteria looking for associations with acute cerebral abnormalities, ES, and in-hospital mortality.

METHODS

This was a retrospective review of prospectively collected data for patients admitted to the PICU at Children's National Hospital from July 2021 to January 2023 with continuous EEG. We excluded patients with known epilepsy and cerebral injury prior to presentation. All patients were screened for RPP. The American Clinical Neurophysiology Society standardized Critical Care EEG terminology for the IIC was applied to each RPP. Associations between IIC and RPP not meeting IIC criteria, with clinical and EEG variables, were calculated using odds ratios (ORs).

RESULTS

Of 201 patients, 21% (42/201) had RPP and 12% (24/201) met IIC criteria. Among patients with an IIC pattern, the median age was 3.4 years (interquartile range (IQR) 0.6-12 years). Sixty-seven percent (16/24) of patients met a single IIC criterion, whereas the remainder met two criteria. ESs were identified in 83% (20/24) of patients and cerebral injury was identified in 96% (23/24) of patients with IIC patterns. When comparing patients with IIC patterns with those with RPP not qualifying as an IIC pattern, both patterns were associated with acute cerebral abnormalities (IIC OR 26 [95% confidence interval {CI} 3.4-197], p = 0.0016 vs. RPP OR 3.5 [95% CI 1.1-11], p = 0.03), however, only the IIC was associated with ES (OR 121 [95% CI 33-451], p < 0.0001) versus RPP (OR 1.3 [0.4-5], p = 0.7).

CONCLUSIONS

Rhythmic and periodic patterns and subsequently the IIC are commonly seen in the PICU and carry a high association with cerebral injury. Additionally, the IIC, seen in more than 10% of critically ill children, is associated with ES. The independent impact of RPP and IIC patterns on secondary brain injury and need for treatment of these patterns independent of ES requires further study.

Pitfalls in scalp EEG: Current obstacles and future directions

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study

BACKGROUND

Epileptiform activity is associated with worse patient outcomes, including increased risk of disability and death. However, the effect of epileptiform activity on neurological outcome is confounded by the feedback between treatment with antiseizure medications and epileptiform activity burden. We aimed to quantify the heterogeneous effects of epileptiform activity with an interpretability-centred approach.

METHODS

We did a retrospective, cross-sectional study of patients in the intensive care unit who were admitted to Massachusetts General Hospital (Boston, MA, USA). Participants were aged 18 years or older and had electrographic epileptiform activity identified by a clinical neurophysiologist or epileptologist. The outcome was the dichotomised modified Rankin Scale (mRS) at discharge and the exposure was epileptiform activity burden defined as mean or maximum proportion of time spent with epileptiform activity in 6 h windows in the first 24 h of electroencephalography. We estimated the change in discharge mRS if everyone in the dataset had experienced a specific epileptiform activity burden and were untreated. We combined pharmacological modelling with an interpretable matching method to account for confounding and epileptiform activity-antiseizure medication feedback. The quality of the matched groups was validated by the neurologists.

FINDINGS

Between Dec 1, 2011, and Oct 14, 2017, 1514 patients were admitted to Massachusetts General Hospital intensive care unit, 995 (66%) of whom were included in the analysis. Compared with patients with a maximum epileptiform activity of 0 to less than 25%, patients with a maximum epileptiform activity burden of 75% or more when untreated had a mean 22·27% (SD 0·92) increased chance of a poor outcome (severe disability or death). Moderate but long-lasting epileptiform activity (mean epileptiform activity burden 2% to <10%) increased the risk of a poor outcome by mean 13·52% (SD 1·93). The effect sizes were heterogeneous depending on preadmission profile-eg, patients with hypoxic-ischaemic encephalopathy or acquired brain injury were more adversely affected compared with patients without these conditions.

INTERPRETATION

Our results suggest that interventions should put a higher priority on patients with an average epileptiform activity burden 10% or greater, and treatment should be more conservative when maximum epileptiform activity burden is low. Treatment should also be tailored to individual preadmission profiles because the potential for epileptiform activity to cause harm depends on age, medical history, and reason for admission.

FUNDING

National Institutes of Health and National Science Foundation.

Prognosis of consciousness disorders in the intensive care unit

Assessments of consciousness are a critical part of prognostic algorithms for critically ill patients suffering from severe brain injuries. There have been significant advances in the field of coma science over the past two decades, providing clinicians with more advanced and precise tools for diagnosing and prognosticating disorders of consciousness (DoC). Advanced neuroimaging and electrophysiological techniques have vastly expanded our understanding of the biological mechanisms underlying consciousness, and have helped identify new states of consciousness. One of these, termed cognitive motor dissociation, can predict functional recovery at 1 year post brain injury, and is present in up to 15-20% of patients with DoC. In this chapter, we review several tools that are used to predict DoC, describing their strengths and limitations, from the neurological examination to advanced imaging and electrophysiologic techniques. We also describe multimodal assessment paradigms that can be used to identify covert consciousness and thus help recognize patients with the potential for future recovery and improve our prognostication practices.

Spectrum of Ictal-Interictal Continuum: The Significance of 2HELPS2B Score and Background Suppression

PURPOSE

The aims of this study were to identify (1) the spectrum of ictal-interictal continuum (IIC) using the two dimensions of 2HELPS2B score and background suppression and (2) the response to subsequent anti-seizure drugs depends on the spectrum of IIC.

METHODS

The study prospectively enrolled 62 patients with IIC on EEG. The diagnosis of nonconvulsive status epilepticus was attempted with Salzburg criteria as well as clinical and neuroimaging data. IICs were dichotomized into patients with nonconvulsive status epilepticus and coma-IIC. The 2HELPS2B score was evaluated as the original proposal. The suppression ratio was analyzed with Persyst software.

RESULTS

Forty-seven cases (75.8%) were nonconvulsive status epilepticus-IIC and 15 cases (24.2%) were coma-IIC. Multivariate analysis revealed that the 2HELPS2B score was the only significant variable dichotomizing the spectrum of IIC (odds ratio, 3.0; 95% confidence interval, 1.06-8.6; P = 0.03 for nonconvulsive status epilepticus-IIC). In addition, the suppression ratio was significantly negatively correlated with 2HELPS2B scores (Spearman coefficient = -0.37, P = 0.004 for left hemisphere and Spearman coefficient = -0.3, P = 0.02 for right hemisphere). Furthermore, patients with higher 2HELPS2B score (74% [14/19] in ≥2 points vs. 44% [14/32] in <2 points, P = 0.03 by χ 2 test) and lower suppression ratio (62% [23/37] in ≤2.18 vs. 35% [6/17] in >2.18, P = 0.06 by χ 2 test) seemed to be more responsive to subsequent anti-seizure drug.

CONCLUSIONS

The 2HELPS2B score and background suppression can be used to distinguish the spectrum of IIC and thereby predict the response to subsequent anti-seizure drug.

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.

Ride the Wave: Continuous Electroencephalography is Indicated in the Management of Traumatic Brain Injury

Introduction: Patients with traumatic brain injury (TBI) are at risk for seizures and other abnormalities that can have permanent adverse effects on the brain. We aimed to report the incidence of seizures and continuous EEG (cEEG) abnormalities after TBI and identify any risk factors associated with the development of these abnormalities. Materials and Methods: This retrospective study identified 245 adult patients with mild to severe TBI who had a cEEG performed within one week of admission to a Midwest Level 1 Trauma Center between July 2014 and July 2019. Trauma registry and electronic medical record (EPIC) data were extracted. Results: Twelve percent of patients with TBI developed seizures and an additional 23% demonstrated electrographic patterns that are correlated with risk for seizures (such as lateralized periodic patterns and sporadic epileptiform discharges). Fifty three percent of seizures would have been missed unless a cEEG was performed. Age, history of epilepsy or prior TBI, hypertension, bleeding disorder, and dementia were associated with an increased risk of developing seizures or higher risk patterns. Conclusions: Thirty-five percent of patients who presented with TBI were noted to have seizures or electrographic patterns associated with a higher risk of seizures. The incidence of cEEG abnormalities in this study is higher than previously reported and these patients are at risk for permanent neurological injury. We recommend the routine use of cEEG for all critically ill patients with TBI as over half of the seizures would have been missed if cEEG was not employed.

Sleep Spindles and K-Complexes Are Favorable Prognostic Biomarkers in Critically Ill Patients

SUMMARY

In this narrative review, we summarize recent research on the prognostic significance of biomarkers of sleep in continuous EEG and polysomnographic recordings in intensive care unit patients. Recent studies show the EEG biosignatures of non-rapid eye movement 2 sleep (sleep spindles and K-complexes) on continuous EEG in critically ill patients better predict functional outcomes and mortality than the ictal-interictal continuum patterns. Emergence of more complex and better organized sleep architecture has been shown to parallel neurocognitive recovery and correlate with functional outcomes in traumatic brain injury and strokes. Particularly interesting are studies which suggest intravenous dexmedetomidine may induce a more biomimetic non-rapid eye movement sleep state than intravenous propofol, potentially providing more restorative sleep and lessening delirium. Protocols to improve intensive care unit sleep and neurophysiological studies evaluating the effect of these on sleep and sleep architecture are here reviewed.

Antiseizure Medication Treatment and Outcomes in Patients with Subarachnoid Hemorrhage Undergoing Continuous EEG Monitoring

BACKGROUND

Patients with aneurysmal subarachnoid hemorrhage (aSAH) with electroencephalographic epileptiform activity (seizures, periodic and rhythmic patterns, and sporadic discharges) are frequently treated with antiseizure medications (ASMs). However, the safety and effectiveness of ASM treatment for epileptiform activity has not been established. We used observational data to investigate the effectiveness of ASM treatment in patients with aSAH undergoing continuous electroencephalography (cEEG) to develop a causal hypothesis for testing in prospective trials.

METHODS

This was a retrospective single-center cohort study of patients with aSAH admitted between 2011 and 2016. Patients underwent ≥ 24 h of cEEG within 4 days of admission. All patients received primary ASM prophylaxis until aneurysm treatment (typically within 24 h of admission). Treatment exposure was defined as reinitiation of ASMs after aneurysm treatment and cEEG initiation. We excluded patients with non-cEEG indications for ASMs (e.g., epilepsy, acute symptomatic seizures). Outcomes measures were 90-day mortality and good functional outcome (modified Rankin Scale scores 0-3). Propensity scores were used to adjust for baseline covariates and disease severity.

RESULTS

Ninety-four patients were eligible (40 continued ASM treatment; 54 received prophylaxis only). ASM continuation was not significantly associated with higher 90-day mortality (propensity-adjusted hazard ratio [HR] = 2.01 [95% confidence interval (CI) 0.57-7.02]). ASM continuation was associated with lower likelihood for 90-day good functional outcome (propensity-adjusted HR = 0.39 [95% CI 0.18-0.81]). In a secondary analysis, low-intensity treatment (low-dose single ASM) was not significantly associated with mortality (propensity-adjusted HR = 0.60 [95% CI 0.10-3.59]), although it was associated with a lower likelihood of good outcome (propensity-adjusted HR = 0.37 [95% CI 0.15-0.91]), compared with prophylaxis. High-intensity treatment (high-dose single ASM, multiple ASMs, or anesthetics) was associated with higher mortality (propensity-adjusted HR = 6.80 [95% CI 1.67-27.65]) and lower likelihood for good outcomes (propensity-adjusted HR = 0.30 [95% CI 0.10-0.94]) compared with prophylaxis only.

CONCLUSIONS

Our findings suggest the testable hypothesis that continuing ASMs in patients with aSAH with cEEG abnormalities does not improve functional outcomes. This hypothesis should be tested in prospective randomized studies.

Factors associated with the use of anesthetic drug infusion in patients with status epilepticus and their relation to outcome: a prospective study

Status epilepticus (SE) is one of the most dreadful neurological emergencies; unfortunately, studies targeting SE are still inadequate. This study aims to identify factors associated with the use of CIVAD in patients presenting with status epilepticus and detect those impact the clinical outcome. A prospective study involving 144 episodes of SE in 144 patients. Patients were categorized according to whether or not they received CIVAD. Subjects underwent clinical assessment, brain imaging, and EEG. The consciousness level was assessed using the Glasgow coma scale (GCS) and the Full outline of responsiveness (FOUR) scale. SE severity score (STESS) and Epidemiology-based mortality score (EMSE) were used as scales for outcome prediction. Continuous IV anesthetic drug infusion was initiated in 36% of patients (+ CIVAD). Such groups showed a significantly worse initial level of consciousness (< 0.001), an unstable course of seizure evolution (0.009), and all of them showed abnormal EEG patterns. A significantly higher number of patients (+ CIVAD) developed complications (< 0.001), had higher outcome prediction scores (< 0.001), and mortality rates (< 0.001) compared to those who did not need CIVAD (- CIVAD). Mortality was associated with acute symptomatic etiology and higher total doses of propofol. Among the study population, mortality among patients who received CIVAD was associated with acute symptomatic SE and prolonged propofol infusion rather than any clinical parameters or predictor scores.

Seizure Management in the Intensive Care Unit

Purpose of this review

This review presents current therapy for seizures in the intensive care unit. The reader is provided with recent evidence regarding the use of EEG in determining treatment for acute seizures. Proposed treatment approaches for seizures and status epilepticus are provided. Controversies and complexity of selecting treatments are discussed.

Recent findings

Critical Care EEG Monitoring Research Consortium analyzed the association of periodic and rhythmic electroencephalographic patterns with seizures and found that lateralized and generalized periodic discharges and lateralized rhythmic delta were associated with increased seizure risk. Applications using modified EEG techniques have demonstrated more rapid feedback to the ICU than was previously possible.

Summary

Accurate diagnosis and efficient treatment of seizures in the ICU is challenging due to patient factors, complexities of antiepileptic drug therapy, and the required expertise for EEG interpretation. Selection of optimally effective therapy for seizures or status epilepticus depends on multiple factors, making collaboration between neurophysiologists and the ICU team of paramount importance.

Lateralized Periodic Discharges: Which patterns are interictal, ictal, or peri-ictal?

There is an ongoing debate if Lateralized Periodic Discharges (LPDs) represent an interictal pattern reflecting non-specific but irritative brain injury, or conversely, is an ictal pattern. The challenge is: how to correctly manage these patients? Between this apparent dichotomous distinction, there is a pattern lying along the interictal-ictal continuum (IIC) that we may call "peri-ictal". Peri-ictal means that LPDs are temporally associated with epileptic seizures (although not necessarily in the same recording). Their recognition should lead to careful EEG monitoring and longer periods of video-EEG to detect seizure activity (clinical and/or subclinical seizures). In order to distinguish which kind of LPDs should be considered as representing interictal/irritative brain injury versus ictal/peri-ictal LPDs, a set of criteria, with both clinical/neuroimaging and EEG, is proposed. Among them, the dichotomy LPDs-proper versus LPDs-plus should be retained. Spiky or sharp LPDs followed by associated slow after-waves or periods of flattening giving rise to a triphasic morphology should be included in the definition of LPDs-plus. We propose defining a particular subtype of LPDs-plus that we call "LPDs-max". The LPDs-max pattern corresponds to an ictal pattern, and therefore, a focal non-convulsive status epilepticus, sometimes associated with subtle motor signs and epileptic seizures. LPDs-max include periodic polyspike-wave activity and/or focal burst-suppression-like patterns. LPDs-max have a posterior predominance over the temporo-parieto-occipital regions and are refractory to antiseizure drugs. Interpretations of EEGs in critically ill patients require a global clinical approach, not limited to the EEG patterns. The clinical context and results of neuroimaging play key roles.

Deep active learning for Interictal Ictal Injury Continuum EEG patterns

OBJECTIVES

Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as "ictal interictal injury continuum" (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear.

METHODS

We assembled >200,000 h of EEG from 1,454 hospitalized patients. From these, we collected 9,808 labeled and 120,000 unlabeled 10-second EEG segments. Labels included 6 IIIC patterns. In each AL iteration, a Dense-Net Convolutional Neural Network (CNN) learned vector representations for EEG segments using available labels, which were used to create a 2D embedding map. Nearest-neighbor label spreading within the embedding map was used to create additional pseudo-labeled data. A second Dense-Net was trained using real- and pseudo-labels. We evaluated several strategies for selecting candidate points for experts to label next. Finally, we compared two methods for class balancing within queries: standard balanced-based querying (SBBQ), and high confidence spread-based balanced querying (HCSBBQ).

RESULTS

Our results show: 1) Label spreading increased convergence speed for AL. 2) All query criteria produced similar results to random sampling. 3) HCSBBQ query balancing performed best. Using label spreading and HCSBBQ query balancing, we were able to train models approaching expert-level performance across all pattern categories after obtaining ∼7000 expert labels.

CONCLUSION

Our results provide guidance regarding the use of AL to efficiently label large EEG datasets in critically ill patients.

Systematic evaluation of rationally chosen multitargeted drug combinations: a combination of low doses of levetiracetam, atorvastatin and ceftriaxone exerts antiepileptogenic effects in a mouse model of acquired epilepsy

Epileptogenesis, the gradual process that leads to epilepsy after brain injury or genetic mutations, is a complex network phenomenon, involving a variety of morphological, biochemical and functional brain alterations. Although risk factors for developing epilepsy are known, there is currently no treatment available to prevent epilepsy. We recently proposed a multitargeted, network-based approach to prevent epileptogenesis by rationally combining clinically available drugs and provided first proof-of-concept that this strategy is effective. Here we evaluated eight novel rationally chosen combinations of 14 drugs with mechanisms that target different epileptogenic processes. The combinations consisted of 2-4 different drugs per combination and were administered systemically over 5 days during the latent epileptogenic period in the intrahippocampal kainate mouse model of acquired temporal lobe epilepsy, starting 6 h after kainate. Doses and dosing intervals were based on previous pharmacokinetic and tolerability studies in mice. The incidence and frequency of spontaneous electrographic and electroclinical seizures were recorded by continuous (24/7) video linked EEG monitoring done for seven days at 4 and 12 weeks post-kainate, i.e., long after termination of drug treatment. Compared to vehicle controls, the most effective drug combination consisted of low doses of levetiracetam, atorvastatin and ceftriaxone, which markedly reduced the incidence of electrographic seizures (by 60%; p<0.05) and electroclinical seizures (by 100%; p<0.05) recorded at 12 weeks after kainate. This effect was lost when higher doses of the three drugs were administered, indicating a synergistic drug-drug interaction at the low doses. The potential mechanisms underlying this interaction are discussed. We have discovered a promising novel multitargeted combination treatment for modifying the development of acquired epilepsy.

Continuous EEG in ICU: Not a Luxury After All

Continuous vs Routine Electroencephalogram in Critically Ill Adults With Altered Consciousness and No Recent Seizure: A Multicenter Randomized Clinical Trial

Rossetti AO, Schindler K, Sutter R, Rüegg S, Zubler F, Novy J, Oddo M, Warpelin-Decrausaz L, Alvarez V. JAMA Neurol. 2020;77(10):1-8. doi:10.1001/jamaneurol.2020.2264

Importance:

In critically ill patients with altered consciousness, continuous electroencephalogram (cEEG) improves seizure detection but is resource-consuming compared with routine EEG (rEEG). It is also uncertain whether cEEG has an effect on outcome.

Objective:

To assess whether cEEG is associated with reduced mortality compared with rEEG.

Design, Setting, and Participants:

The pragmatic multicenter Continuous EEG Randomized Trial in Adults was conducted between 2017 and 2018, with follow-up of 6 months. Outcomes were assessed by interviewers blinded to interventions. The study took place at 4 tertiary hospitals in Switzerland (intensive and intermediate care units). Depending on investigators’ availability, we pragmatically recruited critically ill adults having Glasgow Coma Scale scores of 11 or less or Full Outline of Responsiveness score of 12 or less, without recent seizures or status epilepticus. They had cerebral (eg, brain trauma, cardiac arrest, hemorrhage, or stroke) or noncerebral conditions (eg, toxic-metabolic or unknown etiology), and EEG was requested as part of standard care. An independent physician provided emergency informed consent.

Interventions:

Participants were randomized 1:1 to cEEG for 30 to 48 hours versus 2 rEEGs (20 minutes each), interpreted according to standardized American Clinical Neurophysiology Society guidelines.

Main Outcomes and Measures:

Mortality at 6 months represented the primary outcome. Secondary outcomes included interictal and ictal features detection and change in therapy.

Results:

We analyzed 364 (33% women; mean [SD] age, 63 [15] years) patients. At 6 months, mortality was 89 of 182 in those with cEEG and 88 of 182 in those with rEEG (adjusted relative risk [RR]: 1.02; 95% CI: 0.83-1.26; P = .85). Exploratory comparisons within subgroups stratifying patients according to age, premorbid disability, comorbidities on admission, deeper consciousness reduction, and underlying diagnoses revealed no significant effect modification. Continuous EEG was associated with increased detection of interictal features and seizures (adjusted RR: 1.26; 95% CI: 1.08-1.15; P = .004 and 3.37; 95% CI: 1.63-7.00; P = .001, respectively) and more frequent adaptations in anti-seizure therapy (RR: 1.84; 95% CI: 1.12-3.00; P = .01).

Conclusions and Relevance:

This pragmatic trial shows that in critically ill adults with impaired consciousness and no recent seizure, cEEG leads to increased seizure detection and modification of anti-seizure treatment but is not related to improved outcome compared with repeated rEEG. Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources.

Electroencephalogram Monitoring in Critical Care

Seizures are common in critically ill patients. Electroencephalogram (EEG) is a tool that enables clinicians to provide continuous brain monitoring and to guide treatment decisions-brain telemetry. EEG monitoring has particular utility in the intensive care unit as most seizures in this setting are nonconvulsive. Despite the increased use of EEG monitoring in the critical care unit, it remains underutilized. In this review, we summarize the utility of EEG and different EEG modalities to monitor patients in the critical care setting.

[Electroencephalography in acute stroke]

OBJECTIVE

To determine the incidence of non-convulsive status epilepticus, epileptiform activity, rhythmic and periodic patterns in patients with acute stroke.

MATERIAL AND METHOD

An analysis of electroencephalography (EEG) in 86 stroke patients in the neurointensive care unit of the tertiary medical center was performed. Criteria for starting EEG recording were epileptic seizures or clinical suspicion of uncontrolled epileptic status. The ictal-interictal continuum biomarkers and the diagnostic value of EEG for prediction of survival and recovery were assessed.

RESULTS

Pathological changes on EEG were recorded in 84% of patients. These patients showed the absence of the dominant occipital rhythm (66%) and hemispheric slowing (42%). Diffuse slowing below the theta range was observed in 41% of patients. EEG reactivity was absent in 20%. Sporadic epileptiform discharges were recorded in 36% of patients and rhythmic and periodic patterns in 26%. Reliable predictors of the unfavorable outcome were the absence of dominant occipital rhythm, lack of reactivity, and low amplitude of the background EEG. No association between the recording of epileptiform activity and the probability of death was shown.

CONCLUSION

The most useful EEG biomarkers for predicting survival are amplitude, dominant frequency of background EEG activity and reactivity to external stimulus. Sporadic epileptiform discharges, rhythmic, and periodic patterns are not mandatory associated with a negative prognosis in stroke patients.

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

INTRODUCTION

Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients' clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE).

METHODS

We retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ's real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists.

RESULTS

Majority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%.

DISCUSSION

Claritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity.

CONCLUSIONS

Ruling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ's high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.

Diagnostic Value of Electroencephalography with Ten Electrodes in Critically Ill Patients

BACKGROUND

In critical care settings, electroencephalography (EEG) with reduced number of electrodes (reduced montage EEG, rm-EEG) might be a timely alternative to the conventional full montage EEG (fm-EEG). However, past studies have reported variable accuracies for detecting seizures using rm-EEG. We hypothesized that the past studies did not distinguish between differences in sensitivity from differences in classification of EEG patterns by different readers. The goal of the present study was to revisit the diagnostic value of rm-EEG when confounding issues are accounted for.

METHODS

We retrospectively collected 212 adult EEGs recorded at Massachusetts General Hospital and reviewed by two epileptologists with access to clinical, trending, and video information. In Phase I of the study, we re-configured the first 4 h of the EEGs in lateral circumferential montage with ten electrodes and asked new readers to interpret the EEGs without access to any other ancillary information. We compared their rating to the reading of hospital clinicians with access to ancillary information. In Phase II, we measured the accuracy of the same raters reading representative samples of the discordant EEGs in full and reduced configurations presented randomly by comparing their performance to majority consensus as the gold standard.

RESULTS

Of the 95 EEGs without seizures in the selected fm-EEG, readers of rm-EEG identified 92 cases (97%) as having no seizure activity. Of 117 EEGs with "seizures" identified in the selected fm-EEG, none of the cases was labeled as normal on rm-EEG. Readers of rm-EEG reported pathological activity in 100% of cases, but labeled them as seizures (N = 77), rhythmic or periodic patterns (N = 24), epileptiform spikes (N = 7), or burst suppression (N = 6). When the same raters read representative epochs of the discordant EEG cases (N = 43) in both fm-EEG and rm-EEG configurations, we found high concordance (95%) and intra-rater agreement (93%) between fm-EEG and rm-EEG diagnoses.

CONCLUSIONS

Reduced EEG with ten electrodes in circumferential configuration preserves key features of the traditional EEG system. Discrepancies between rm-EEG and fm-EEG as reported in some of the past studies can be in part due to methodological factors such as choice of gold standard diagnosis, asymmetric access to ancillary clinical information, and inter-rater variability rather than detection failure of rm-EEG as a result of electrode reduction per se.

Ictal-interictal continuum: a review of recent advancements

Continuous electroencephalogram (cEEG) has become an indispensable technique in the management of critically ill patients for early detection and treatment of non-convulsive seizures (NCS) and non-convulsive status epilepticus (NCSE). It has also brought about a renaissance in a wide range of rhythmic and periodic patterns with heterogeneous frequency and morphology. These patterns share the rhythmic and sharp appearances of electrographic seizures, but often lack the necessary frequency, spatiotemporal evolution and clinical accompaniments to meet the definitive criteria for ictal patterns. They may be associated with cerebral metabolic crisis and neuronal injury, therefore not clearly interictal either, but lie along an intervening spectrum referred to as ictal-interictal continuum (IIC). Generally speaking, rhythmic and periodic patterns are categorized as interictal patterns when occurring at a rate of <1Hz, and are categorized as NCS and NCSE when occurring at a rate of >2.5 Hz with spatiotemporal evolution. As such, IIC commonly includes the rhythmic and periodic patterns occurring at a rate of 1–2.5 Hz without spatiotemporal evolution and clinical correlates. Currently there are no evidence-based guidelines on when and if to treat patients with IIC patterns, and particularly how aggressively to treat, presenting a challenging electrophysiological and clinical conundrum. In practice, a diagnostic trial with preferably a non-sedative anti-seizure medication (ASM) can be considered with the end point being both clinical and electrographic improvement. When available and necessary, correlation of IIC with biomarkers of neuronal injury, such as neuronal specific enolase (NSE), neuroimaging, depth electrode recording, cerebral microdialysis and oxygen measurement, can be assessed for the consideration of ASM treatment. Here we review the recent advancements in their clinical significance, risk stratification and treatment algorithm.

Clinical, Electroencephalographic Features and Prognostic Factors of Cefepime-Induced Neurotoxicity: A Retrospective Study

BACKGROUND

The incidence of cefepime-induced neurotoxicity (CIN) has been previously underestimated, and there have only been sporadic reports from critical neurological settings. The present study aimed to investigate the potential factors associated with disease development, electroencephalography (EEG) sub-classification, and outcome measures.

METHODS

The 10-year medical records of patients who underwent EEG between 2007 and 2016 at a tertiary medical center in Taiwan, and developed encephalopathy after cefepime therapy were retrospectively reviewed. Age- and sex-matched controls were included for further analysis. Demographic data, the occurrence of clinical seizures, non-convulsive status epilepticus (NCSE), use of antiepileptic drugs (AEDs), receiving maintenance or urgent hemodialysis, EEG findings, and functional outcomes were analyzed. The Chi-square test and a logistic regression model were applied to survey significant prognostic factors relating to mortality.

RESULTS

A total of 42 CIN patients were identified, including 25 patients from wards and 17 from intensive care units; their mean age was 75.8 ± 11.8 years. Twenty-one patients (50%) had chronic kidney disease, and 18 (43%) had acute kidney injury. Among these patients, 32 (76%) received appropriate cefepime dose adjustment. Three patients had a normal renal function at the time of CIN onset. The logistic regression model suggested that maintenance hemodialysis and longer duration of cefepime use were independently associated with the development of CIN, with odds ratios of 3.8 and 1.2, respectively. NCSE was frequently noted in the CIN patients (64%). Generalized periodic discharge with or without triphasic morphology was the most common EEG pattern (38%), followed by generalized rhythmic delta activity and generalized spike-and-waves. AEDs were administered to 86% of the patients. A total of 17 patients (40%) did not survive to hospital discharge. Adequate cefepime dose adjustment and early cefepime discontinuation led to a better prognosis.

CONCLUSIONS

CIN was associated with high mortality and morbidity rates. Neurotoxic symptoms could still occur when the cefepime dose was adjusted, or in patients with normal renal function. Patients with maintenance hemodialysis or a longer duration of cefepime therapy tended to develop CIN. Early recognition of abnormal EEG findings allowed for the withdrawal of the offending agent, resulting in clinical improvements and a better prognosis at discharge.

Electrographic seizures and ictal-interictal continuum (IIC) patterns in critically ill patients

Critical care long-term continuous electroencephalogram (cEEG) monitoring has expanded dramatically in the last several decades spurned by technological advances in EEG digitalization and several key clinical findings: 1-Seizures are relatively common in the critically ill-large recent observational studies suggest that around 20% of critically ill patients placed on cEEG have seizures. 2-The majority (~75%) of patients who have seizures have exclusively "electrographic seizures", that is, they have no overt ictal clinical signs. Along with the discovery of the unexpectedly high incidence of seizures was the high prevalence of EEG patterns that share some common features with archetypical electrographic seizures but are not uniformly considered to be "ictal". These EEG patterns include lateralized periodic discharges (LPDs) and generalized periodic discharges (GPDs)-patterns that at times exhibit ictal-like behavior and at other times behave more like an interictal finding. Dr. Hirsch and colleagues proposed a conceptual framework to describe this spectrum of patterns called the ictal-interictal continuum (IIC). In the following years, investigators began to answer some of the key pragmatic clinical concerns such as which patients are at risk of seizures and what is the optimal duration of cEEG use. At the same time, investigators have begun probing the core questions for critical care EEG-what is the underlying pathophysiology of these patterns, at what point do these patterns cause secondary brain injury, what are the optimal treatment strategies, and how do these patterns affect clinical outcomes such as neurological disability and the development of epilepsy. In this review, we cover recent advancements in both practical concerns regarding cEEG use, current treatment strategies, and review the evidence associating IIC/seizures with poor clinical outcomes.

Diagnostic Reliability of Headset-Type Continuous Video EEG Monitoring for Detection of ICU Patterns and NCSE in Patients with Altered Mental Status with Unknown Etiology

BACKGROUND/OBJECTIVE

Simplified continuous electroencephalogram (cEEG) monitoring has shown improvement in detecting seizures; however, it is insufficient in detecting abnormal EEG patterns, such as periodic discharges (PDs), rhythmic delta activity (RDA), spikes and waves (SW), and continuous slow wave (CS), as well as nonconvulsive status epilepticus (NCSE). Headset-type continuous video EEG monitoring (HS-cv EEG monitoring; AE-120A EEG Headset™, Nihon Kohden, Tokyo, Japan) is a recently developed easy-to-use technology with eight channels. However, its ability to detect abnormal EEG patterns with raw EEG data has not been comprehensively evaluated. We aimed to examine the diagnostic accuracy of HS-cv EEG monitoring in detecting abnormal EEG patterns and NCSE in patients with altered mental status (AMS) with unknown etiology. We also evaluated the time required to initiate HS-cv EEG monitoring in these patients.

METHODS

We prospectively observed and retrospectively examined patients who were admitted with AMS between January and December 2017 at the neurointensive care unit at Asakadai Central General Hospital, Saitama, Japan. We excluded patients whose data were missing for various reasons, such as difficulties in recording, and those whose consciousness had recovered between HS-cv EEG and conventional cEEG (C-cEEG) monitoring. For the included patients, we performed HS-cv EEG monitoring followed by C-cEEG monitoring. Definitive diagnosis was confirmed by C-cEEG monitoring with the international 10-20 system. As the primary outcome, we verified the sensitivity and specificity of HS-cv EEG monitoring in detecting abnormal EEG patterns including PDs, RDA, SW, and CS, in detecting the presence of PDs, and in detecting NCSE. As the secondary outcome, we calculated the time to initiate HS-cv EEG monitoring after making the decision.

RESULTS

Fifty patients (76.9%) were included in the final analyses. The median age was 72 years, and 66% of the patients were male. The sensitivity and specificity of HS-cv EEG monitoring for detecting abnormal EEG patterns were 0.974 (0.865-0.999) and 0.909 (0.587-0.998), respectively, and for detecting PDs were 0.824 (0.566-0.926) and 0.970 (0.842-0.999), respectively. We diagnosed 13 (26%) patients with NCSE using HS-cv EEG monitoring and could detect NCSE with a sensitivity and specificity of 0.706 (0.440-0.897) and 0.970 (0.842-0.999), respectively. The median time needed to initiate HS-cv EEG was 57 min (5-142).

CONCLUSIONS

HS-cv EEG monitoring is highly reliable in detecting abnormal EEG patterns, with moderate reliability for PDs and NCSE, and rapidly initiates cEEG monitoring in patients with AMS with unknown etiology.

Determinants and outcome of repeat continuous electroencephalogram monitoring-A case-control study

OBJECTIVE

A retrospective, single-center study to analyze the determinants of a repeat continuous EEG (cEEG) monitoring during hospitalization and its outcomes using a matched case-control study design.

METHODS

Adults with a repeat cEEG session (cases) were matched by age (±3 years), gender, and mental status to patients with a single cEEG (controls) during hospitalization. Several clinical and EEG characteristics were analyzed to identify predictors of repeat cEEG. Repeat cEEG outcomes were analyzed based on its yield of electrographic seizure. We investigated the predictors of finding increased epileptic potential (degree of association with electrographic seizures) on the repeat cEEG, a marker for possible anti-epileptic drugs (AEDs) management change.

RESULTS

A total of 213 (8.6% of all unique cEEG patients) cases were included. A multivariable conditional logistic regression model comparing cases and controls showed that the presence of acute brain insult [odds ratio (OR) = 3.36, 95% CI = 1.26-8.94, P = .015], longer hospital admission (OR = 1.11, 95% CI = 1.07-1.15, P < .001) and being on AEDs at the end of index cEEG (OR = 4.0, 95% CI = 1.8-8.87, P < .001) was determinants of a repeat cEEG. Among cases, 17 (8%) had electrographic seizures on repeat cEEG. Increased epileptic potential on repeat cEEG was noted in 34 (16%) cases. The latter is associated with change in etiology after the index cEEG (P = .03) and duration of repeat cEEG (P = .003) based on multivariable logistic regression model. AEDs were changed in 46 (21.6%) patients based on repeat cEEG findings.

SIGNIFICANCE

Repeat cEEG is not an uncommon practice. It leads to the diagnosis of electrographic seizures in a significant percentage of patients. With the potential of impacting AED management in 16%-21% patients, it should be considered in high-risk patients suffering acute brain insults undergoing prolonged hospitalization.

The Use of Continuous EEG Monitoring in Intensive Care Units in The Netherlands: A National Survey

Background

Currently, continuous electroencephalographic monitoring (cEEG) is the only available diagnostic tool for continuous monitoring of brain function in intensive care unit (ICU) patients. Yet, the exact relevance of routinely applied ICU cEEG remains unclear, and information on the implementation of cEEG, especially in Europe, is scarce. This study explores current practices of cEEG in adult Dutch ICU departments focusing on organizational and operational factors, development over time and factors perceived relevant for abstaining its use.

Methods

A national survey on cEEG in adults among the neurology and adult intensive care departments of all Dutch hospitals (n = 82) was performed.

Results

The overall institutional response rate was 78%. ICU cEEG is increasingly used in the Netherlands (in 37% of all hospitals in 2016 versus in 21% in 2008). Currently in 88% of university, 55% of teaching and 14% of general hospitals use ICU cEEG. Reasons for not performing cEEG are diverse, including perceived non-feasibility and lack of data on the effect of cEEG use on patient outcome. Mostly, ICU cEEG is used for non-convulsive seizures or status epilepticus and prognostication. However, cEEG is never or rarely used for monitoring cerebral ischemia and raised intracranial pressure in traumatic brain injury. Review and reporting practices differ considerably between hospitals. Nearly all hospitals perform non-continuous review of cEEG traces. Methods for moving toward continuous review of cEEG traces are available but infrequently used in practice.

Conclusions

cEEG is increasingly used in Dutch ICUs. However, cEEG practices vastly differ between hospitals. Future research should focus on uniform cEEG practices including unambiguous EEG interpretation to facilitate collaborative research on cEEG, aiming to provide improved standard patient care and robust data on the impact of cEEG use on patient outcome.

Electroencephalographic monitoring in the critically ill patient: What useful information can it contribute?

Monitoring is a crucial part of the care of the critically ill patient. It detects organ dysfunction and provides guidance on the therapeutic approach. Intensivists closely monitor the function of various organ systems, and the brain is no exception. Continuous EEG monitoring is a noninvasive and uninterrupted way of assessing cerebral cortical activity with good spatial and excellent temporal resolution. The diagnostic effectiveness of non-convulsive status epilepticus as a cause of unexplained consciousness disorder has increased the use of continuous EEG monitoring in the neurocritical care setting. However, non-convulsive status epilepticus is not the only indication for the assessment of cerebral cortical activity. This study summarizes the indications, usage and methodology of continuous EEG monitoring in the intensive care unit, with the aim of allowing practitioners to become familiarized the technique.

The Impact of Interictal Discharges on Performance

PURPOSE OF REVIEW

Our purpose is to review evidence relating to the concept that interictal epileptiform discharges (IEDs) impair brain performance.

RECENT FINDINGS

Sophisticated measures of motor and cognitive performance have clarified older observations, confirming that in both animals and humans, IEDs affect aspects of performance, IED morphology, frequency, anatomical distribution, and duration matter. However, we now know that it is difficult to draw a line between IEDs and seizures, not only by electrical criteria but even by metabolic and molecular measures. IEDs impair performance acutely and probably chronically. Thus, there are good theoretical reasons for suppressing them, but no consensus has been reached on how much effort this deserves. Many antiepileptic medications effective for control of clinical seizures have little effect on IEDs. Better methods of measuring outcomes may allow selection of individual patients for whom treatment aimed at IEDs is worthwhile.