Continuous EEG monitoring in Kenyan children with non-traumatic coma

EEG Monitoring in Critically Ill Children: Establishing High-Yield Subgroups

PURPOSE

Continuous EEG monitoring (CEEG) is increasingly used to identify electrographic seizures (ES) in critically ill children, but it is resource intense. We aimed to assess how patient stratification by known ES risk factors would impact CEEG utilization.

METHODS

This was a prospective observational study of critically ill children with encephalopathy who underwent CEEG. We calculated the average CEEG duration required to identify a patient with ES for the full cohort and subgroups stratified by known ES risk factors.

RESULTS

ES occurred in 345 of 1,399 patients (25%). For the full cohort, an average of 90 hours of CEEG would be required to identify 90% of patients with ES. If subgroups of patients were stratified by age, clinically evident seizures before CEEG initiation, and early EEG risk factors, then 20 to 1,046 hours of CEEG would be required to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation and EEG risk factors present in the initial hour of CEEG required only 20 (<1 year) or 22 (≥1 year) hours of CEEG to identify a patient with ES. Conversely, patients with no clinically evident seizures before CEEG initiation and no EEG risk factors in the initial hour of CEEG required 405 (<1 year) or 1,046 (≥1 year) hours of CEEG to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation or EEG risk factors in the initial hour of CEEG required 29 to 120 hours of CEEG to identify a patient with ES.

CONCLUSIONS

Stratifying patients by clinical and EEG risk factors could identify high- and low-yield subgroups for CEEG by considering ES incidence, the duration of CEEG required to identify ES, and subgroup size. This approach may be critical for optimizing CEEG resource allocation.

Late-Onset Findings During Extended EEG Monitoring Are Rare in Critically Ill Children

PURPOSE

Electrographic seizures (ES) are common in critically ill children undergoing continuous EEG (CEEG) monitoring, and previous studies have aimed to target limited CEEG resources to children at highest risk of ES. However, previous studies have relied on observational data in which the duration of CEEG was clinically determined. Thus, the incidence of late occurring ES is unknown. The authors aimed to assess the incidence of ES for 24 hours after discontinuation of clinically indicated CEEG.

METHODS

This was a single-center prospective study of nonconsecutive children with acute encephalopathy in the pediatric intensive care unit who underwent 24 hours of extended research EEG after the end of clinical CEEG. The authors assessed whether there were new findings that affected clinical management during the extended research EEG, including new-onset ES.

RESULTS

Sixty-three subjects underwent extended research EEG. The median duration of the extended research EEG was 24.3 hours (interquartile range 24.0-25.3). Three subjects (5%) had an EEG change during the extended research EEG that resulted in a change in clinical management, including an increase in ES frequency, differential diagnosis of an event, and new interictal epileptiform discharges. No subjects had new-onset ES during the extended research EEG.

CONCLUSIONS

No subjects experienced new-onset ES during the 24-hour extended research EEG period. This finding supports observational data that patients with late-onset ES are rare and suggests that ES prediction models derived from observational data are likely not substantially underrepresenting the incidence of late-onset ES after discontinuation of clinically indicated CEEG.

Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures

OBJECTIVE

Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates.

METHODS

The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed.

RESULTS

When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model.

CONCLUSIONS

Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.

Electrographic Seizure Characteristics and Electrographic Status Epilepticus Prediction

PURPOSE

We aimed to characterize electrographic seizures (ES) and electrographic status epilepticus (ESE) and determine whether a model predicting ESE exclusively could effectively guide continuous EEG monitoring (CEEG) utilization in critically ill children.

METHODS

This was a prospective observational study of consecutive critically ill children with encephalopathy who underwent CEEG. We used descriptive statistics to characterize ES and ESE, and we developed a model for ESE prediction.

RESULTS

ES occurred in 25% of 1,399 subjects. Among subjects with ES, 23% had ESE, including 37% with continuous seizures lasting >30 minutes and 63% with recurrent seizures totaling 30 minutes within a 1-hour epoch. The median onset of ES and ESE occurred 1.8 and 0.18 hours after CEEG initiation, respectively. The optimal model for ESE prediction yielded an area under the receiver operating characteristic curves of 0.81. A cutoff selected to emphasize sensitivity (91%) yielded specificity of 56%. Given the 6% ESE incidence, positive predictive value was 11% and negative predictive value was 99%. If the model were applied to our cohort, then 53% of patients would not undergo CEEG and 8% of patients experiencing ESE would not be identified.

CONCLUSIONS

ESE was common, but most patients with ESE had recurrent brief seizures rather than long individual seizures. A model predicting ESE might only slightly improve CEEG utilization over models aiming to identify patients at risk for ES but would fail to identify some patients with ESE. Models identifying ES might be more advantageous for preventing ES from evolving into ESE.

Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children

PURPOSE

Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children.

METHODS

This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort ( N = 719) in a new validation cohort ( N = 680). The model aimed to determine the CEEG duration at which there was <15%, <10%, <5%, or <2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors.

RESULTS

A model aiming to determine the CEEG duration at which a patient had <10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The <10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively.

CONCLUSIONS

This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.

Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective

Objectives

This article aims to critically review the literature on continuous electroencephalography (cEEG) monitoring in the intensive care unit (ICU) from an Australian and New Zealand perspective and provide recommendations for clinicians.

Design and review methods

A taskforce of adult and paediatric neurologists, selected by the Epilepsy Society of Australia, reviewed the literature on cEEG for seizure detection in critically ill neonates, children, and adults in the ICU. The literature on routine EEG and cEEG for other indications was not reviewed. Following an evaluation of the evidence and discussion of controversial issues, consensus was reached, and a document that highlighted important clinical, practical, and economic considerations regarding cEEG in Australia and New Zealand was drafted.

Results

This review represents a summary of the literature and consensus opinion regarding the use of cEEG in the ICU for detection of seizures, highlighting gaps in evidence, practical problems with implementation, funding shortfalls, and areas for future research.

Conclusion

While cEEG detects electrographic seizures in a significant proportion of at-risk neonates, children, and adults in the ICU, conferring poorer neurological outcomes and guiding treatment in many settings, the health economic benefits of treating such seizures remain to be proven. Presently, cEEG in Australian and New Zealand ICUs is a largely unfunded clinical resource that is subsequently reserved for the highest-impact patient groups. Wider adoption of cEEG requires further research into impact on functional and health economic outcomes, education and training of the neurology and ICU teams involved, and securement of the necessary resources and funding to support the service.

The Role of Electroencephalography in the Prognostication of Clinical Outcomes in Critically Ill Children: A Review

Electroencephalography (EEG) is a neurologic monitoring modality that allows for the identification of seizures and the understanding of cerebral function. Not only can EEG data provide real-time information about a patient’s clinical status, but providers are increasingly using these results to understand short and long-term prognosis in critical illnesses. Adult studies have explored these associations for many years, and now the focus has turned to applying these concepts to the pediatric literature. The aim of this review is to characterize how EEG can be utilized clinically in pediatric intensive care settings and to highlight the current data available to understand EEG features in association with functional outcomes in children after critical illness. In the evaluation of seizures and seizure burden in children, there is abundant data to suggest that the presence of status epilepticus during illness is associated with poorer outcomes and a higher risk of mortality. There is also emerging evidence indicating that poorly organized EEG backgrounds, lack of normal sleep features and lack of electrographic reactivity to clinical exams portend worse outcomes in this population. Prognostication in pediatric critical illness must be informed by the comprehensive evaluation of a patient’s clinical status but the utilization of EEG may help contribute to this assessment in a meaningful way.

Electrographic Seizures and Outcome in Critically Ill Children

OBJECTIVE

To determine the association between electroencephalographic seizure (ES) and electroencephalographic status epilepticus (ESE) exposure and unfavorable neurobehavioral outcomes in critically ill children with acute encephalopathy.

METHODS

This was a prospective cohort study of acutely encephalopathic critically ill children undergoing continuous EEG monitoring (CEEG). ES exposure was assessed as (1) no ES/ESE, (2) ES, or (3) ESE. Outcomes assessed at discharge included the Glasgow Outcome Scale-Extended Pediatric Version (GOS-E-Peds), Pediatric Cerebral Performance Category (PCPC), and mortality. Unfavorable outcome was defined as a reduction in GOS-E-Peds or PCPC score from preadmission to discharge. Stepwise selection was used to generate multivariate logistic regression models that assessed associations between ES exposure and outcomes while adjusting for multiple other variables.

RESULTS

Among 719 consecutive critically ill patients, there was no evidence of ES in 535 patients (74.4%), ES occurred in 140 patients (19.5%), and ESE in 44 patients (6.1%). The final multivariable logistic regression analyses included ES exposure, age dichotomized at 1 year, acute encephalopathy category, initial EEG background category, comatose at CEEG initiation, and Pediatric Index of Mortality 2 score. There was an association between ESE and unfavorable GOS-E-Peds (odds ratio 2.21, 95% confidence interval 1.07-4.54) and PCPC (odds ratio 2.17, 95% confidence interval 1.05-4.51) but not mortality. There was no association between ES and unfavorable outcome or mortality.

CONCLUSIONS

Among acutely encephalopathic critically ill children, there was an association between ESE and unfavorable neurobehavioral outcomes, but no association between ESE and mortality. ES exposure was not associated with unfavorable neurobehavioral outcomes or mortality.

Machine learning models to predict electroencephalographic seizures in critically ill children

OBJECTIVE

To determine whether machine learning techniques would enhance our ability to incorporate key variables into a parsimonious model with optimized prediction performance for electroencephalographic seizure (ES) prediction in critically ill children.

METHODS

We analyzed data from a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy who underwent clinically-indicated continuous EEG monitoring (CEEG). We implemented and compared three state-of-the-art machine learning methods for ES prediction: (1) random forest; (2) Least Absolute Shrinkage and Selection Operator (LASSO); and (3) Deep Learning Important FeaTures (DeepLIFT). We developed a ranking algorithm based on the relative importance of each variable derived from the machine learning methods.

RESULTS

Based on our ranking algorithm, the top five variables for ES prediction were: (1) epileptiform discharges in the initial 30 minutes, (2) clinical seizures prior to CEEG initiation, (3) sex, (4) age dichotomized at 1 year, and (5) epileptic encephalopathy. Compared to the stepwise selection-based approach in logistic regression, the top variables selected by our ranking algorithm were more informative as models utilizing the top variables achieved better prediction performance evaluated by prediction accuracy, AUROC and F1 score. Adding additional variables did not improve and sometimes worsened model performance.

CONCLUSION

The ranking algorithm was helpful in deriving a parsimonious model for ES prediction with optimal performance. However, application of state-of-the-art machine learning models did not substantially improve model performance compared to prior logistic regression models. Thus, to further improve the ES prediction, we may need to collect more samples and variables that provide additional information.

Guidelines for the diagnosis and treatment of acute encephalopathy in childhood

The cardinal symptom of acute encephalopathy is impairment of consciousness of acute onset during the course of an infectious disease, with duration and severity meeting defined criteria. Acute encephalopathy consists of multiple syndromes such as acute necrotizing encephalopathy, acute encephalopathy with biphasic seizures and late reduced diffusion and clinically mild encephalitis/encephalopathy with reversible splenial lesion. Among these syndromes, there are both similarities and differences. In 2016, the Japanese Society of Child Neurology published 'Guidelines for the Diagnosis and Treatment of Acute Encephalopathy in Childhood', which made recommendations and comments on the general aspects of acute encephalopathy in the first half, and on individual syndromes in the latter half. Since the guidelines were written in Japanese, this review article describes extracts from the recommendations and comments in English, in order to introduce the essence of the guidelines to international clinicians and researchers.

EEG Monitoring After Convulsive Status Epilepticus

After convulsive status epilepticus, patients of all ages may have ongoing EEG seizures identified by continuous EEG monitoring. Furthermore, high EEG seizure exposure has been associated with unfavorable neurobehavioral outcomes. Thus, recent guidelines and consensus statements recommend many patients with persisting altered mental status after convulsive status epilepticus undergo continuous EEG monitoring. This review summarizes the available epidemiologic data and related recommendations provided by recent guidelines and consensus statements.

Validation of a model to predict electroencephalographic seizures in critically ill children

OBJECTIVE

Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but identification requires extensive resources for continuous electroencephalographic monitoring (CEEG). In a previous study, we developed a clinical prediction rule using three clinical variables (age, acute encephalopathy category, clinically evident seizure[s] prior to CEEG initiation) and two electroencephalographic (EEG) variables (EEG background category and interictal discharges within the first 30 minutes of EEG) to identify patients at high risk for ESs for whom CEEG might be essential. In the current study, we aimed to validate the ES prediction model using an independent cohort.

METHODS

The prospectively acquired validation cohort consisted of 314 consecutive critically ill children treated in the Pediatric Intensive Care Unit of a quaternary care referral hospital with acute encephalopathy undergoing clinically indicated CEEG. We calculated test characteristics using the previously developed prediction model in the validation cohort. As in the generation cohort study, we selected a 0.10 cutpoint to emphasize sensitivity.

RESULTS

The incidence of ESs in the validation cohort was 22%. The generation and validation cohorts were alike in most clinical and EEG characteristics. The ES prediction model was well calibrated and well discriminating in the validation cohort. The model had a sensitivity of 90%, specificity of 37%, positive predictive value of 28%, and negative predictive value of 93%. If applied, the model would limit 31% of patients from undergoing CEEG while failing to identify 10% of patients with ESs. The model had similar performance characteristics in the generation and validation cohorts.

SIGNIFICANCE

A model employing five readily available clinical and EEG variables performed well when validated in a new consecutive cohort. Implementation would substantially reduce CEEG utilization, although some patients with ESs would not be identified. This model may serve a critical role in targeting limited CEEG resources to critically ill children at highest risk for ESs.

Epilepsy Research in Mali: A Pilot Pharmacokinetics Study on First-Line Antiepileptic Drug Treatment

Background and Purpose

The indication and benefit of plasma level of antiepileptic (AEDs) has been debating in the monitoring of people living with epilepsy and the epilepsy treatment gap has largely been documented in developed countries. This study was aimed to highlight the epilepsy treatment gap between rural and urban Mali.

Methods

We conducted a pilot study on AEDs treatment from September 2016 to May 2019. For 6 months, 120 children and young adults living with epilepsy (rural site, 90; urban site, 30) received phenobarbital, valproic acid and/or carbamazepine. At our rural study site, we determined the AED plasma levels, monitored the frequency, severity and the duration of seizure, and administered monthly the McGill quality of life questionnaire. At our urban study site, each patient underwent an electroencephalogram and brain computed tomography scan without close monitoring.

Results

At the rural study site, patients were mostly on monotherapy; AED levels at 1 month (M1) (n=90) and at 3 months (M3) (n=27) after inclusion were normal in 50% at M1 versus 55.6% at M3, low in 42.2% at M1 versus 33.3% at M3 and high in 7.8% at M1 versus 11.1% at M3. AED levels at M1 and at M3 were significantly different p<0.0001. By M3, seizures (n=90) were <1/month in 26.7%, and lasted less than 1 minute in 16.7%. After a yearlong follow up, all 90 patients reported a good or excellent quality of life. At our urban study site, patients (n=30) were on carbamazepine and valproid acid in 66.67% and monotherapy (carbamazepine) in 33.33%. By November 2018, only six out 30 patients (on bi-therapy) were still taking their medications.

Conclusions

Epilepsy diagnostic and treatment are a real concern in Mali. Our data showed appropriate AED treatment with close follow up resulted in a better quality of life of patients in rural Mali. We will promote the approach of personalized medicine in AED treatment in Mali.

Continuous EEG in a Pediatric Intensive Care Unit: Adherence to Monitoring Criteria and Barriers to Adequate Implementation

BACKGROUND

Subclinical seizures are common in critically ill children and are best detected by continuous EEG (cEEG) monitoring. Timely detection of seizures requires pediatric intensive care unit (PICU) physicians to identify patients at risk of seizures and request cEEG monitoring. A recent consensus statement from the American Clinical Neurophysiology Society (ACNS) outlines the indications for cEEG monitoring in critically ill patients. However, adherence to these cEEG monitoring criteria among PICU physicians is unknown. Our project had two goals: 1. To assess adherence to cEEG monitoring indications and barriers toward their implementation; 2. To improve compliance with the ACNS cEEG monitoring criteria in our PICU.

METHODS

This is a single-institution study. A total of 234 PICU admissions (183 unique patients) were studied. A 6-month retrospective chart review identified PICU patients meeting ACNS criteria for cEEG monitoring, and patients for whom monitoring was requested. This was followed by an 8-week quality improvement project. During this mentorship period, a didactic 15-min lecture and summary handouts regarding the ACNS indications for cEEG monitoring were provided to all PICU physicians. Requests for cEEG monitoring during the mentorship period were compared to baseline adherence to cEEG monitoring recommendations, and barriers toward timely cEEG monitoring were assessed.

RESULTS

Nearly every fifth PICU patient met cEEG monitoring indications, and prevalences of patients meeting those indications were similar in the retrospective and the prospective mentorship period (18% vs. 19%). Almost all patients (98%) requiring cEEG as per ACNS criteria met the indication for monitoring already at the time of their PICU admission. During the retrospective period, 23% of patients meeting ACNS criteria had a request for cEEG monitoring, which increased to 83% during the mentorship period. The median delay to cEEG initiation was 16.7 h during the mentorship period, largely due to limited hours of EEG technician availability. Electrographic seizures were identified in 36% of patients monitored, all within the first 120 min of cEEG recording. The majority (79%) of cEEGs informed clinical management.

CONCLUSIONS

A brief teaching intervention supplemented by pictographic handouts significantly increased adherence to cEEG monitoring recommendations, and cEEGs guided clinical management. However, there were long delays to cEEG initiation. In order to promptly recognize subclinical seizures in critically ill children, we strongly advocate for a routine screening for cEEG monitoring indications as part of the PICU admission process, and a care model allowing for cEEG initiation around-the-clock.

EEG monitoring duration to identify electroencephalographic seizures in critically ill children

OBJECTIVES

To determine the optimal duration of continuous EEG monitoring (CEEG) for electrographic seizure (ES) identification in critically ill children.

METHODS

We performed a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy. We evaluated baseline clinical risk factors (age and prior clinically evident seizures) and emergent CEEG risk factors (epileptiform discharges and ictal-interictal continuum patterns) using a multistate survival model. For each subgroup, we determined the CEEG duration for which the risk of ES was <5% and <2%.

RESULTS

ES occurred in 184 children (26%). Patients achieved <5% risk of ES after (1) 6 hours if ≥1 year without prior seizures or EEG risk factors; (2) 1 day if <1 year without prior seizures or EEG risks; (3) 1 day if ≥1 year with either prior seizures or EEG risks; (4) 2 days if ≥1 year with prior seizures and EEG risks; (5) 2 days if <1 year without prior seizures but with EEG risks; and (6) 2.5 days if <1 year with prior seizures regardless of the presence of EEG risks. Patients achieved <2% risk of ES at the same durations except patients without prior seizures or EEG risk factors would require longer CEEG (1.5 days if <1 year of age, 1 day if ≥1 year of age).

CONCLUSIONS

A model derived from 2 baseline clinical risk factors and emergent EEG risk factors would allow clinicians to implement personalized strategies that optimally target limited CEEG resources. This would enable more widespread use of CEEG-guided management as a potential neuroprotective strategy.

CLINICALTRIALSGOV IDENTIFIER

NCT03419260.

Continuous Electroencephalography Monitoring in Critically Ill Infants and Children

Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.

Development of a model to predict electroencephalographic seizures in critically ill children

OBJECTIVE

Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone.

METHODS

We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG.

RESULTS

The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES.

SIGNIFICANCE

A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy.

Electrographic seizure burden and outcomes following pediatric status epilepticus

Pediatric status epilepticus carries a substantial risk for morbidity and mortality, but the relationship between seizure burden, treatment, and outcome remains incompletely understood. This review summarizes the evidence linking seizure burden and outcomes among critically ill children in the intensive care unit (ICU), a population in whom accurate quantification of seizure burden is possible using continuous electroencephalographic monitoring. Several high-quality observational studies among critically ill children have reported an association between higher seizure burden and worse outcome, even after adjusting for potential confounders such as age, etiology, and illness severity. Although these studies support the hypothesis that seizures contribute to brain injury and worsen outcome, a causal link between seizures and outcome remains to be proven. The relationship between seizures and outcome is likely complex, and dependent on factors such as etiology, preexisting neurological disability, medication exposure, and possibly individual genetic factors. Studies attempting to define this complex relationship will need to measure and account for these factors in their analyses. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Towards Successes in the Management of Nonconvulsive Status Epilepticus: Tracing the Detection-to-Needle Trajectories

PURPOSE

Data on the timeliness of emergent medication delivery for nonconvulsive status epilepticus (NCSE) are currently lacking.

METHODS

Retrospective chart reviews (between 2015 and 2018) and analyses of all patients with NCSE were performed at the University of Nebraska Medical Center, a level 4 epilepsy center, to determine the latencies to order and administration of the first, second, and third antiepileptic drugs (AEDs). Recurrent NCSE cases were considered independently and classified as comatose and noncomatose.

RESULTS

There were 77 occurrences of NCSE in 53 patients. The first, second, and third AEDs were delivered with substantial delays at median times of 80 (25%-75% interquartile range, 44-166), 126 (interquartile range, 67-239), and 158 minutes (interquartile range, 89-295), respectively, from seizure detection. The median times to the order of the first and second AEDs were 33 and 134.5 minutes longer in comatose NCSE patients compared with those with noncomatose forms, respectively (P = 0.001 and 0.004, respectively). The median times between the AED orders and their administration in these two groups were the same (P = 0.60 and 0.37, respectively). With bivariate analysis, the median latencies to administration of the first, second, and third AEDs were significantly increased by 33, 109.5, and 173 minutes, respectively, in patients who died within 30 days compared with those who survived (P = 0.047, P = 0.02, P = 0.0007, respectively).

CONCLUSIONS

The administration of the first, second, and third AEDs for NCSE was delayed. Slow initiation of acute treatment in comatose patients was caused by delays in the placement of the medication order.

Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit

The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine learning algorithms for predicting mortality in a database of 414 critically ill children undergoing cEEG in the ICU. The area under the receiver operating characteristic curve (AUC) in the test subset was highest for stepwise selection/elimination models (AUC = 0.82) followed by least absolute shrinkage and selection operator (LASSO) and support vector machine with linear kernel (AUC = 0.79), and random forest (AUC = 0.71). The explanatory models had the poorest discriminative performance (AUC = 0.63 for the model without considering etiology and AUC = 0.45 for the model considering etiology). Using few variables and a relatively small number of patients, machine learning techniques added information to explanatory models for prediction of in-hospital mortality.

Outcomes following electrographic seizures and electrographic status epilepticus in the pediatric and neonatal ICUs

PURPOSE OF REVIEW

Increasing recognition of electrographic seizures and electrographic status epilepticus in critically ill neonates and children has highlighted the importance of identifying their potential contributions to neurological outcomes to guide optimal management.

RECENT FINDINGS

Recent studies in children and neonates have found an independent association between increasing seizure burden and worse short-term and long-term outcomes, even after adjusting for other important contributors to outcome such as seizure cause and illness severity. The risk of worse neurological outcome has been shown to increase above a seizure burden threshold of 12-13 min/h, which is considerably lower than the conventional definition of status epilepticus of 30 min/h. Randomized controlled trials in neonates have demonstrated that electroencephalography-targeted therapy can successfully reduce seizure burden, but due to their small size these trials have not been able to demonstrate that more aggressive electroencephalography-targeted treatment of both subclinical and clinical seizures results in improved outcome.

SUMMARY

Despite mounting evidence for an independent association between increasing seizure burden and worse outcome, further study is needed to determine whether early seizure identification and aggressive antiseizure treatment can improve neurodevelopmental outcomes.

Time to continuous electroencephalogram in repeated admissions to the pediatric intensive care unit

PURPOSE

Describe timing from intensive care unit (ICU) admission to initiation of continuous electroencephalogram (cEEG) in repeated ICU admissions.

METHOD

We performed a retrospective observational study in pediatric patients who underwent repeated ICU admissions with cEEG from 2011 to 2013. The main outcome measure was time from ICU admission to cEEG.

RESULTS

There were 41 patients (54% males) with at least 2 ICU admissions with cEEG (median (p25-p75) age at first admission: 3.3 (0.3-8.4) years, at second admission: 3.9 (1.1-9.4) years), 7 patients (57% males, 9.9 (2.9-11.5) years) with at least 3 ICU admissions, and 5 patients (60% males, 10.1 (4-10.5) years) with at least 4 ICU admissions. One patient had 21 ICU admissions. The median (p25-p75) time from ICU admission to cEEG was not different during the first and second ICU admissions [10.7 (1.9-22.9) hours versus 13 (0.2-36.7) hours; p=0.908]. Among patients with electrographic seizures on first admission, time to cEEG was not different during the first and second admissions [7.9 (0.5-23.4) hours versus 14.5 (-2 to 44.5) hours; p=0.636]. Among patients with status epilepticus during the first admission, time to cEEG was not different between the first and second admissions [15.3 (9-79) hours versus 40.7 (19.3-42.6) hours; p=0.75].

CONCLUSIONS

The time from ICU admission to the initiation of cEEG did not decrease in second or subsequent ICU admissions, even in patients with seizures or status epilepticus on the first admission.

Time to electroencephalography is independently associated with outcome in critically ill neonates and children

OBJECTIVE

To identify factors associated with in-hospital mortality in neonates and children undergoing continuous electroencephalography (cEEG) monitoring in the intensive care unit (ICU).

METHODS

We performed a retrospective observational study in patients from birth to 21 years of age who underwent clinically indicated cEEG in the ICU from 2011 to 2013. The main outcome measure was in-hospital mortality.

RESULTS

Six-hundred and twenty-five patients (54.2% male) met eligibility criteria, of whom 211 were neonates (55% male, 24.8% premature) and 414 were pediatric patients (53.9% male). Electrographic seizures occurred in 176 patients (28.2%) and status epilepticus (SE) occurred in 20 (11.4%). The time from ICU admission to cEEG initiation was 16.7 (5.1-94.4) h. Eighty-nine patients (14.2%) (30 [14.2%] neonates, and 59 [14.3%] pediatric patients) died in the hospital. In neonates-after controlling for gender and prematurity-independent factors associated with mortality were prematurity (odds ratio [OR] 2.63. 95% confidence interval [CI] 1.06-6.5, p = 0.037), presence of status epilepticus (SE); OR 8.82, 95% CI 1.74-44.57, p = 0.008), and time from ICU admission to initiation of cEEG (OR 1.002, 95% CI 1.001-1.004 per hour, p = 0.008]. In pediatric patients-after controlling for gender and age-independent factors associated with mortality were the absence of seizures factors associated with mortality were absence of seizures (OR = 4.3, (95% CI: 1.5-12.4), p = 0.007), the presence of SE (OR 7.76, 95% CI 1.47-40.91, p = 0.016), and the time from ICU admission to initiation of cEEG (OR 1.001, 95% CI 1.0002-1.001, per hour, p = 0.005].

SIGNIFICANCE

Both presence of electrographic SE and time from ICU admission to cEEG initiation were independent factors associated with mortality in neonates and pediatric patients with cEEG in the ICU.

Could EEG Monitoring in Critically Ill Children Be a Cost-effective Neuroprotective Strategy?

PURPOSE

Electrographic status epilepticus (ESE) in critically ill children is associated with unfavorable functional outcomes, but identifying candidates for ESE management requires resource-intense EEG monitoring. A cost-effectiveness analysis was performed to estimate how much ESE identification and management would need to improve patient outcomes to make EEG monitoring strategies a good value.

METHODS

A decision tree was created to examine the relationships among variables important to deciding whether to perform EEG monitoring. Variable costs were estimated from their component parts, outcomes were estimated in quality-adjusted life-years, and incremental cost-effectiveness ratios were calculated to compare the relative values using four alternative EEG monitoring strategies that varied by monitoring duration.

RESULTS

Forty-eight hours of EEG monitoring would be worth its cost if ESE identification and management improved patient outcomes by ≥7%. If ESE identification and management improved patient outcomes by 3% to 6%, then 24 or 48 hours of EEG monitoring would be worth the cost depending on how much decision makers were willing to pay per quality-adjusted life-year gained. If ESE identification and management improved outcomes by as little as 3%, then 24 hours of EEG monitoring would be worth the cost.

CONCLUSIONS

EEG monitoring has the potential to be cost-effective if ESE identification and management improves patient outcomes by as little as 3%.

Impact of an ICU EEG monitoring pathway on timeliness of therapeutic intervention and electrographic seizure termination

OBJECTIVES

We aimed to determine whether implementation of a structured multidisciplinary electroencephalography (EEG) monitoring pathway improved the timeliness of administration of antiseizure medication in response to electrographic seizures in encephalopathic critically ill children.

METHODS

A multidisciplinary team developed a pathway to standardize EEG monitoring and seizure management in encephalopathic critically ill children, aiming to decrease the time from electrographic seizure onset to antiseizure medication administration. Data were collected to inform the team of improvement opportunities, which were then provided by an institutional pathway, staff education, and streamlined communication. Measurements were obtained before and after pathway implementation to assess for improvement.

RESULTS

We collected data on 41 patients before and 21 after pathway implementation. There were no differences between the baseline and pathway groups in demographic characteristics, acute encephalopathy etiologies, or antiseizure medications utilized. The median duration [interquartile range, IQR] from seizure onset to antiseizure medication administration was shorter for patients treated with the pathway (64 min [50, 101]) compared to patients treated prior to pathway implementation (139 min [71, 189]; p = 0.0006). The median [IQR] interval from seizure onset to antiseizure medication order was shorter for the pathway group (31 min [20, 49]) than the baseline group (71 min [33, 131]; p = 0.003). The median [IQR] interval from antiseizure medication order to administration was shorter for the pathway group (30 min [19, 40]) than the baseline group (40 min [17, 68]) (p = 0.047). Seizure termination was more likely to occur following initial antiseizure medication administration in the pathway than baseline group (67% vs. 27%, p = 0.002).

SIGNIFICANCE

Implementation of the pathway resulted in a significant reduction in the duration between electrographic seizure onset and antiseizure medication administration, and a significant increase in the rate of electrographic seizure termination following an initial antiseizure medication. Further study is needed to determine whether these changes are associated with improved outcomes.

Consensus statement on continuous EEG in critically ill adults and children, part I: indications

INTRODUCTION

Critical Care Continuous EEG (CCEEG) is a common procedure to monitor brain function in patients with altered mental status in intensive care units. There is significant variability in patient populations undergoing CCEEG and in technical specifications for CCEEG performance.

METHODS

The Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society developed expert consensus recommendations on the use of CCEEG in critically ill adults and children.

RECOMMENDATIONS

The consensus panel recommends CCEEG for diagnosis of nonconvulsive seizures, nonconvulsive status epilepticus, and other paroxysmal events, and for assessment of the efficacy of therapy for seizures and status epilepticus. The consensus panel suggests CCEEG for identification of ischemia in patients at high risk for cerebral ischemia; for assessment of level of consciousness in patients receiving intravenous sedation or pharmacologically induced coma; and for prognostication in patients after cardiac arrest. For each indication, the consensus panel describes the patient populations for which CCEEG is indicated, evidence supporting use of CCEEG, utility of video and quantitative EEG trends, suggested timing and duration of CCEEG, and suggested frequency of review and interpretation.

CONCLUSION

CCEEG has an important role in detection of secondary injuries such as seizures and ischemia in critically ill adults and children with altered mental status.

How much does it cost to identify a critically ill child experiencing electrographic seizures?

OBJECTIVES

Electrographic seizures in critically ill children may be identified by continuous EEG monitoring. We evaluated the cost effectiveness of 4 electrographic seizure identification strategies (no EEG monitoring and EEG monitoring for 1 hour, 24 hours, or 48 hours).

METHODS

We created a decision tree to model the relationships among variables from a societal perspective. To provide input for the model, we estimated variable costs directly related to EEG monitoring from their component parts, and we reviewed the literature to estimate the probabilities of outcomes. We calculated incremental cost-effectiveness ratios to identify the trade-off between cost and effectiveness at different willingness-to-pay values.

RESULTS

Our analysis found that the preferred strategy was EEG monitoring for 1 hour, 24 hours, and 48 hours if the decision maker was willing to pay <$1,666, $1,666-$22,648, and >$22,648 per critically ill child identified with electrographic seizures, respectively. The 48-hour strategy only identified 4% more children with electrographic seizures at substantially higher cost. Sensitivity analyses found that all 3 strategies were acceptable at lower willingness-to-pay values when children with higher electrographic seizure risk were monitored.

CONCLUSIONS

The results of this study support monitoring of critically ill children for 24 hours because the cost to identify a critically ill child with electrographic seizures is modest. Further study is needed to predict better which children may benefit from 48 hours of EEG monitoring because the costs are much higher.

Electroencephalographic features of convulsive epilepsy in Africa: A multicentre study of prevalence, pattern and associated factors

•

Electroencephalographic abnormalities are common in Africans with epilepsy, with an adjusted prevalence of 2.7 (95% confidence interval, 2.5–2.9) per 1000 population.

•

Electroencephalographic abnormalities are associated with preventable factors such as adverse perinatal events and frequent seizures.

•

Electroencephalography is helpful in identifying focal epilepsy in Africa, where timing of focal aetiologies is problematic and there is a lack of neuroimaging services.

Objective

We investigated the prevalence and pattern of electroencephalographic (EEG) features of epilepsy and the associated factors in Africans with active convulsive epilepsy (ACE).

Methods

We characterized electroencephalographic features and determined associated factors in a sample of people with ACE in five African sites. Mixed-effects modified Poisson regression model was used to determine factors associated with abnormal EEGs.

Results

Recordings were performed on 1426 people of whom 751 (53%) had abnormal EEGs, being an adjusted prevalence of 2.7 (95% confidence interval (95% CI), 2.5–2.9) per 1000. 52% of the abnormal EEG had focal features (75% with temporal lobe involvement). The frequency and pattern of changes differed with site. Abnormal EEGs were associated with adverse perinatal events (risk ratio (RR) = 1.19 (95% CI, 1.07–1.33)), cognitive impairments (RR = 1.50 (95% CI, 1.30–1.73)), use of anti-epileptic drugs (RR = 1.25 (95% CI, 1.05–1.49)), focal seizures (RR = 1.09 (95% CI, 1.00–1.19)) and seizure frequency (RR = 1.18 (95% CI, 1.10–1.26) for daily seizures; RR = 1.22 (95% CI, 1.10–1.35) for weekly seizures and RR = 1.15 (95% CI, 1.03–1.28) for monthly seizures)).

Conclusions

EEG abnormalities are common in Africans with epilepsy and are associated with preventable risk factors.

Significance

EEG is helpful in identifying focal epilepsy in Africa, where timing of focal aetiologies is problematic and there is a lack of neuroimaging services.

Electrographic status epilepticus and neurobehavioral outcomes in critically ill children

PURPOSE

Electrographic seizures (ESs) and electrographic status epilepticus (ESE) are common in children with acute neurologic conditions in pediatric intensive care units (PICUs), and ESE is associated with worse functional and quality-of-life outcomes. As an exploratory study, we aimed to determine if ESE was associated with worse outcomes using more detailed neurobehavioral measures.

METHODS

Three hundred children with an acute neurologic condition and altered mental status underwent clinically indicated EEG monitoring and were enrolled in a prospective observational study. We obtained follow-up data from subjects who were neurodevelopmentally normal prior to PICU admission. We evaluated for associations between ESE and adaptive behavior (Adaptive Behavior Assessment System-II, ABAS-II), behavioral and emotional problems (Child Behavior Checklist, CBCL), and executive function (Behavior Rating Inventory of Executive Function, BRIEF) using linear regression analyses. A p-value of <0.05 was considered significant.

RESULTS

One hundred thirty-seven of 300 subjects were neurodevelopmentally normal prior to PICU admission. We obtained follow-up data from 36 subjects for the CBCL, 32 subjects for the ABAS-II, and 20 subjects for the BRIEF. The median duration from admission to follow-up was 2.6 years (IQR: 1.2-3.8). There were no differences in the acute care variables (age, sex, mental status category, intubation status, paralysis status, acute neurologic diagnosis category, seizure category, EEG background category, or short-term outcome) between subjects with and without follow-up data for any of the outcome measures. On univariate analysis, significant differences were not identified for CBCL total problem (ES coefficient: -4.1, p = 0.48; ESE coefficient: 8.9, p = 0.13) or BRIEF global executive function (ES coefficient: 2.1, p = 0.78; ESE coefficient: 14.1, p = 0.06) scores, although there were trends toward worse scores in subjects with ESE. On univariate analysis, ESs were not associated with worse scores (coefficient: -21.5, p = 0.051), while ESE (coefficient: -29.7, p = 0.013) was associated with worse ABAS-II adaptive behavioral global composite scores. On multivariate analysis, when compared to subjects with no seizures, both ESs (coefficient: -28, p=0.014) and ESE (coefficient: -36, p = 0.003) were associated with worse adaptive behavioral global composite scores.

DISCUSSION

Among previously neurodevelopmentally normal children with acute neurologic disorders, ESs and ESE were associated with worse adaptive behavior and trends toward worse behavioral-emotional and executive function problems. This was a small exploratory study, and the impact of ESs and ESE on these neurobehavioral measures may be clarified by subsequent larger studies. This article is part of a Special Issue entitled "Status Epilepticus".

Electrographic seizures and status epilepticus in critically ill children and neonates with encephalopathy

Electrographic seizures are seizures that are evident on EEG monitoring. They are common in critically ill children and neonates with acute encephalopathy. Most electrographic seizures have no associated clinical changes, and continuous EEG monitoring is necessary for identification. The effect of electrographic seizures on outcome is the focus of active investigation. Studies have shown that a high burden of electrographic seizures is associated with worsened clinical outcome after adjustment for cause and severity of brain injury, suggesting that a high burden of such seizures might independently contribute to secondary brain injury. Further research is needed to determine whether identification and management of electrographic seizures reduces secondary brain injury and improves outcome in critically ill children and neonates.

Electrographic status epilepticus and long-term outcome in critically ill children

OBJECTIVE

Electrographic seizures (ES) and electrographic status epilepticus (ESE) are common in children in the pediatric intensive care unit (PICU) with acute neurologic conditions. We aimed to determine whether ES or ESE was associated with worse long-term outcomes.

METHODS

Three hundred children with an acute neurologic condition and encephalopathy underwent clinically indicated EEG monitoring and were enrolled in a prospective observational study. We aimed to obtain follow-up data from 137 subjects who were neurodevelopmentally normal before PICU admission.

RESULTS

Follow-up data were collected for 60 of 137 subjects (44%) at a median of 2.7 years. Subjects with and without follow-up data were similar in clinical characteristics during the PICU admission. Among subjects with follow-up data, ES occurred in 12 subjects (20%) and ESE occurred in 14 subjects (23%). Multivariable analysis indicated that ESE was associated with an increased risk of unfavorable Glasgow Outcome Scale (Extended Pediatric Version) category (odds ratio 6.36, p = 0.01) and lower Pediatric Quality of Life Inventory scores (23 points lower, p = 0.001). Among subjects without prior epilepsy diagnoses ESE was associated with an increased risk of subsequently diagnosed epilepsy (odds ratio 13.3, p = 0.002). ES were not associated with worse outcomes.

CONCLUSIONS

Among children with acute neurologic disorders who were reported to be neurodevelopmentally normal before PICU admission, ESE but not ES was associated with an increased risk of unfavorable global outcome, lower health-related quality of life scores, and an increased risk of subsequently diagnosed epilepsy even after adjusting for neurologic disorder category, EEG background category, and age.

Density spectral array for seizure identification in critically ill children

PURPOSE

We evaluated the validity and interrater reliability of encephalographer interpretation of color density spectral array EEG for seizure identification was evaluated in critically ill children and explored predictors of accurate seizure identification.

METHODS

Conventional EEG tracings from 21 consecutive critically ill children were scored for electrographic seizures. Four 2-hour long segments from each subject were converted to 8-channel color density spectral array displays, yielding 84 images. Eight encephalographers received color density spectral array training and circled elements thought to represent seizures. Images were reviewed in random order (Group A) or with information regarding seizure presence in the initial 30 minutes and with subject images in order (Group B). Sensitivity, specificity, and interrater reliability were calculated. Factors associated with color density spectral array seizure identification were assessed.

RESULTS

Seizure prevalence was 43% on conventional EEG. Specificity was significantly higher for Group A than Group B (92.3% vs. 78.2%, P < 0.00). Sensitivity was not significantly different between Groups A and B (64.8% vs. 75%, P = 0.22). Interrater reliability was moderate in both groups. Ten percent of images were falsely classified as containing a seizure. Seizure duration ≥2 minutes predicted identification (P < 0.001).

CONCLUSIONS

Color density spectral array may be a useful screening tool for seizure identification by encephalographers, but it does not identify all seizures and false positives occur.

Clinical features, proximate causes, and consequences of active convulsive epilepsy in Africa

PURPOSE

Epilepsy is common in sub-Saharan Africa (SSA), but the clinical features and consequences are poorly characterized. Most studies are hospital-based, and few studies have compared different ecological sites in SSA. We described active convulsive epilepsy (ACE) identified in cross-sectional community-based surveys in SSA, to understand the proximate causes, features, and consequences.

METHODS

We performed a detailed clinical and neurophysiologic description of ACE cases identified from a community survey of 584,586 people using medical history, neurologic examination, and electroencephalography (EEG) data from five sites in Africa: South Africa; Tanzania; Uganda; Kenya; and Ghana. The cases were examined by clinicians to discover risk factors, clinical features, and consequences of epilepsy. We used logistic regression to determine the epilepsy factors associated with medical comorbidities.

KEY FINDINGS

Half (51%) of the 2,170 people with ACE were children and 69% of seizures began in childhood. Focal features (EEG, seizure types, and neurologic deficits) were present in 58% of ACE cases, and these varied significantly with site. Status epilepticus occurred in 25% of people with ACE. Only 36% received antiepileptic drugs (phenobarbital was the most common drug [95%]), and the proportion varied significantly with the site. Proximate causes of ACE were adverse perinatal events (11%) for onset of seizures before 18 years; and acute encephalopathy (10%) and head injury prior to seizure onset (3%). Important comorbidities were malnutrition (15%), cognitive impairment (23%), and neurologic deficits (15%). The consequences of ACE were burns (16%), head injuries (postseizure) (1%), lack of education (43%), and being unmarried (67%) or unemployed (57%) in adults, all significantly more common than in those without epilepsy.

SIGNIFICANCE

There were significant differences in the comorbidities across sites. Focal features are common in ACE, suggesting identifiable and preventable causes. Malnutrition and cognitive and neurologic deficits are common in people with ACE and should be integrated into the management of epilepsy in this region. Consequences of epilepsy such as burns, lack of education, poor marriage prospects, and unemployment need to be addressed.

Childhood acute non-traumatic coma: aetiology and challenges in management in resource-poor countries of Africa and Asia

OBJECTIVE

This review examines the best available evidence on the aetiology of childhood acute non-traumatic coma in resource-poor countries (RPCs), discusses the challenges associated with management, and explores strategies to address them.

METHODS

Publications in English and French which reported on studies on the aetiology of childhood non-traumatic coma in RPCs are reviewed. Primarily, the MEDLINE database was searched using the keywords coma, unconsciousness, causality, aetiology, child, malaria cerebral, meningitis, encephalitis, Africa, Asia, and developing countries.

RESULTS

14 records were identified for inclusion in the review. Cerebral malaria (CM) was the commonest cause of childhood coma in most of the studies conducted in Africa. Acute bacterial meningitis (ABM) was the second most common known cause of coma in seven of the African studies. Of the studies in Asia, encephalitides were the commonest cause of coma in two studies in India, and ABM was the commonest cause of coma in Pakistan. Streptococcus pneumoniae was the most commonly isolated organism in ABM. Japanese encephalitis, dengue fever and enteroviruses were the viral agents most commonly isolated.

CONCLUSION

Accurate diagnosis of the aetiology of childhood coma in RPCs is complicated by overlap in clinical presentation, limited diagnostic resources, disease endemicity and co-morbidity. For improved outcomes, studies are needed to further elucidate the aetiology of childhood coma in RPCs, explore simple and practical diagnostic tools, and investigate the most appropriate specific and supportive interventions to manage and prevent infectious encephalopathies.

Electroencephalography monitoring in critically ill children: current practice and implications for future study design

PURPOSE

Survey data indicate that continuous electroencephalography (EEG) (CEEG) monitoring is used with increasing frequency to identify electrographic seizures in critically ill children, but studies of current CEEG practice have not been conducted. We aimed to describe the clinical utilization of CEEG in critically ill children at tertiary care hospitals with a particular focus on variables essential for designing feasible prospective multicenter studies evaluating the impact of electrographic seizures on outcome.

METHODS

Eleven North American centers retrospectively enrolled 550 consecutive critically ill children who underwent CEEG. We collected data regarding subject characteristics, CEEG indications, and CEEG findings.

KEY FINDINGS

CEEG indications were encephalopathy with possible seizures in 67% of subjects, event characterization in 38% of subjects, and management of refractory status epilepticus in 11% of subjects. CEEG was initiated outside routine work hours in 47% of subjects. CEEG duration was <12 h in 16%, 12-24 h in 34%, and >24 h in 48%. Substantial variability existed among sites in CEEG indications and neurologic diagnoses, yet within each acute neurologic diagnosis category a similar proportion of subjects at each site had electrographic seizures. Electrographic seizure characteristics including distribution and duration varied across sites and neurologic diagnoses.

SIGNIFICANCE

These data provide a systematic assessment of recent CEEG use in critically ill children and indicate variability in practice. The results suggest that multicenter studies are feasible if CEEG monitoring pathways can be standardized. However, the data also indicate that electrographic seizure variability must be considered when designing studies that address the impact of electrographic seizures on outcome.

Electroencephalographic monitoring in the pediatric intensive care unit

Continuous electroencephalographic (CEEG) monitoring is used with increasing frequency in critically ill children to provide insight into brain function and to identify electrographic seizures. CEEG monitoring use often impacts clinical management, most often by identifying electrographic seizures and status epilepticus. Most electrographic seizures have no clinical correlate, and thus would not be identified without CEEG monitoring. There are increasing data showing that electrographic seizures and electrographic status epilepticus are associated with worse outcome. Seizure identification efficiency may be improved by further development of quantitative electroencephalography trends. This review describes the clinical impact of CEEG data, the epidemiology of electrographic seizures and status epilepticus, the impact of electrographic seizures on outcome, the utility of quantitative electroencephalographic trends for seizure identification, and practical considerations regarding CEEG monitoring.

Electrographic seizures in pediatric ICU patients: cohort study of risk factors and mortality

OBJECTIVES

We aimed to determine the incidence of electrographic seizures in children in the pediatric intensive care unit who underwent EEG monitoring, risk factors for electrographic seizures, and whether electrographic seizures were associated with increased odds of mortality.

METHODS

Eleven sites in North America retrospectively reviewed a total of 550 consecutive children in pediatric intensive care units who underwent EEG monitoring. We collected data on demographics, diagnoses, clinical seizures, mental status at EEG onset, EEG background, interictal epileptiform discharges, electrographic seizures, intensive care unit length of stay, and in-hospital mortality.

RESULTS

Electrographic seizures occurred in 162 of 550 subjects (30%), of which 61 subjects (38%) had electrographic status epilepticus. Electrographic seizures were exclusively subclinical in 59 of 162 subjects (36%). A multivariable logistic regression model showed that independent risk factors for electrographic seizures included younger age, clinical seizures prior to EEG monitoring, an abnormal initial EEG background, interictal epileptiform discharges, and a diagnosis of epilepsy. Subjects with electrographic status epilepticus had greater odds of in-hospital death, even after adjusting for EEG background and neurologic diagnosis category.

CONCLUSIONS

Electrographic seizures are common among children in the pediatric intensive care unit, particularly those with specific risk factors. Electrographic status epilepticus occurs in more than one-third of children with electrographic seizures and is associated with higher in-hospital mortality.

The genetic risk of acute seizures in African children with falciparum malaria

Purpose

It is unclear why some children with falciparum malaria develop acute seizures and what determines the phenotype of seizures. We sought to determine if polymorphisms of malaria candidate genes are associated with acute seizures.

Methods

Logistic regression was used to investigate genetic associations with malaria-associated seizures (MAS) and complex MAS (repetitive, prolonged, or focal seizures) in four MalariaGEN African sites, namely: Blantyre, Malawi; Kilifi, Kenya; Kumasi, Ghana; and Muheza, Tanzania. The analysis was repeated for five inheritance models (dominant, heterozygous, recessive, additive, and general) and adjusted for potential confounders and multiple testing.

Key Findings

Complex phenotypes of seizures constituted 71% of all admissions with MAS across the sites. MAS were strongly associated with cluster of differentiation-ligand-rs3092945 in females in Kilifi (p = 0.00068) and interleukin (IL)-17 receptor E-rs708567 in the pooled analysis across the sites (p = 0.00709). Complex MAS were strongly associated with epidermal growth factor module-containing mucin-like hormone receptor (EMR)1-rs373533 in Kumasi (p = 0.00033), but none in the pooled analysis. Focal MAS were strongly associated with IL-20 receptor A-rs1555498 in Muheza (p = 0.00016), but none in the pooled analysis. Prolonged MAS were strongly associated with complement receptor 1-rs17047660 in Kilifi (p = 0.00121) and glucose-6-phosphate dehydrogenase-rs1050828 in females in the pooled analysis (p = 0.00155). Repetitive MAS were strongly associated with EMR1-rs373533 in Kumasi (p = 0.00003) and cystic fibrosis transmembrane conductance receptor-rs17140229 in the pooled analysis (p = 0.00543). MAS with coma/cerebral malaria were strongly associated with EMR1-rs373533 in Kumasi (p = 0.00019) and IL10-rs3024500 in the pooled analysis across the sites (p = 0.00064).

Significance

We have identified a number of genetic associations that may explain the risk of seizures in >2,000 cases admitted to hospitals with MAS across four sites in Africa. These associations differed according to phenotype of seizures and site.