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Purpose: In 2011, the authors conducted a survey regarding continuous EEG (CEEG) utilization in critically ill children. In the interim decade, the literature has expanded, and guidelines and consensus statements have addressed CEEG utilization. Thus, the authors aimed to characterize current practice related to CEEG utilization in critically ill children., Methods: The authors conducted an online survey of pediatric neurologists from 50 US and 12 Canadian institutions in 2022., Results: The authors assessed responses from 48 of 62 (77%) surveyed institutions. Reported CEEG indications were consistent with consensus statement recommendations and included altered mental status after a seizure or status epilepticus, altered mental status of unknown etiology, or altered mental status with an acute primary neurological condition. Since the prior survey, there was a 3- to 4-fold increase in the number of patients undergoing CEEG per month and greater use of written pathways for ICU CEEG. However, variability in resources and workflow persisted, particularly regarding technologist availability, frequency of CEEG screening, communication approaches, and electrographic seizure management approaches., Conclusions: Among the surveyed institutions, which included primarily large academic centers, CEEG use in pediatric intensive care units has increased with some practice standardization, but variability in resources and workflow were persistent., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2023 by the American Clinical Neurophysiology Society.)

Background and Objectives: Biochemical testing of CSF for neurotransmitter metabolites and their cofactors is often used in the diagnostic evaluation of infants with neurologic disorders but requires an invasive, labor-intensive procedure with many potential sources of error. Our aim was to determine the diagnostic yield of CSF testing for biogenic amines (serotonin, norepinephrine, epinephrine, and dopamine) and their cofactors in identifying inborn errors of neurotransmitter metabolism among infants., Methods: We evaluated all infants aged 1 year or younger who underwent CSF biogenic amine neurotransmitter (CSFNT) testing at Children's Hospital of Philadelphia (CHOP) and Boston Children's Hospital (BCH) between 2008 and 2017 in this cross-sectional study. The primary outcome was the proportion of individuals who received a diagnostic result from CSFNT testing. Secondary assessments included the proportion of infants who obtained a diagnostic result from other types of diagnostic testing., Results: The cohort included 323 individuals (191 from CHOP and 232 from BCH). The median age at presentation was 110 days (range 36-193). The most common presenting features were seizures (71%), hypotonia (47%), and developmental delay (43%). The diagnostic yield of CSFNT testing was zero. When CSF pyridoxal-5-phosphate level was assayed with CSFNT testing, 1 patient had a diagnostic result. An etiologic diagnosis was identified in 163 patients (50%) of the cohort, with genetic testing having the highest yield (120 individuals, 37%)., Discussion: Our findings support the case for deimplementation of CSFNT testing as a standard diagnostic test of etiology in infants aged 1 year or younger presenting with neurologic disorders.

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., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2023 by the American Clinical Neurophysiology Society.)

Objectives: We aimed to identify clinical and EEG monitoring characteristics associated with generalized, lateralized, and bilateral-independent periodic discharges (GPDs, LPDs, and BIPDs) and to determine which patterns were associated with outcomes in critically ill children., Methods: We performed a prospective observational study of consecutive critically ill children undergoing continuous EEG monitoring, including standardized scoring of GPDs, LPDs, and BIPDs. We identified variables associated with GPDs, LPDs, and BIPDs and assessed whether each pattern was associated with hospital discharge outcomes including the Glasgow Outcome Scale-Extended Pediatric version (GOS-E-Peds), Pediatric Cerebral Performance Category (PCPC), and mortality., Results: PDs occurred in 7% (91/1,399) of subjects. Multivariable logistic regression indicated that patients with coma (odds ratio [OR], 3.45; 95% confidence interval [CI]: 1.55, 7.68) and abnormal EEG background category (OR, 6.85; 95% CI: 3.37, 13.94) were at increased risk for GPDs. GPDs were associated with mortality (OR, 3.34; 95% CI: 1.24, 9.02) but not unfavorable GOS-E-Peds (OR, 1.93; 95% CI: 0.88, 4.23) or PCPC (OR, 1.64; 95% CI: 0.75, 3.58). Patients with acute nonstructural encephalopathy did not experience LPDs, and LPDs were not associated with mortality or unfavorable outcomes. BIPDs were associated with mortality (OR, 3.68; 95% CI: 1.14, 11.92), unfavorable GOS-E-Peds (OR, 5.00; 95% CI: 1.39, 18.00), and unfavorable PCPC (OR, 5.96; 95% CI: 1.65, 21.46)., Significance: Patients with coma or more abnormal EEG background category had an increased risk for GPDs and BIPDs, and no patients with an acute nonstructural encephalopathy experienced LPDs. GPDs were associated with mortality and BIPDs were associated with mortality and unfavorable outcomes, but LPDs were not associated with unfavorable outcomes., (Copyright © 2022 by the American Clinical Neurophysiology Society.)

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., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Background and Objectives: EEG and MRI features are independently associated with pediatric cardiac arrest (CA) outcomes, but it is unclear whether their combination improves outcome prediction. We aimed to assess the association of early EEG background category with MRI ischemia after pediatric CA and determine whether addition of MRI ischemia to EEG background features and clinical variables improves short-term outcome prediction., Methods: This was a single-center retrospective cohort study of pediatric CA with EEG initiated ≤24 hours and MRI obtained ≤7 days of return of spontaneous circulation. Initial EEG background was categorized as normal, slow/disorganized, discontinuous/burst-suppression, or attenuated-featureless. MRI ischemia was defined as percentage of brain tissue with apparent diffusion coefficient (ADC) <650 × 10 -6 mm 2 /s and categorized as high (≥10%) or low (<10%). Outcomes were mortality and unfavorable neurologic outcome (Pediatric Cerebral Performance Category increase ≥1 from baseline resulting in ICU discharge score ≥3). The Kruskal-Wallis test evaluated the association of EEG with MRI. Area under the receiver operating characteristic (AUROC) curve evaluated predictive accuracy. Logistic regression and likelihood ratio tests assessed multivariable outcome prediction., Results: We evaluated 90 individuals. EEG background was normal in 16 (18%), slow/disorganized in 42 (47%), discontinuous/burst-suppressed in 12 (13%), and attenuated-featureless in 20 (22%) individuals. The median percentage of MRI ischemia was 5% (interquartile range 1-18); 32 (36%) individuals had high MRI ischemia burden. Twenty-eight (31%) individuals died, and 58 (64%) had unfavorable neurologic outcome. Worse EEG background category was associated with more MRI ischemia ( p < 0.001). The combination of EEG background and MRI ischemia burden had higher predictive accuracy than EEG alone (AUROC: mortality: 0.92 vs 0.87, p = 0.03) or MRI alone (AUROC: mortality: 0.92 vs 0.84, p = 0.02; unfavorable: 0.83 vs 0.73, p < 0.01). Addition of percentage of MRI ischemia to clinical variables and EEG background category improved prediction for mortality (χ 2 = 19.1, p < 0.001) and unfavorable neurologic outcome (χ 2 = 4.8, p = 0.03) and achieved high predictive accuracy (AUROC: mortality: 0.97; unfavorable: 0.92)., Discussion: Early EEG background category was associated with MRI ischemia after pediatric CA. Combining EEG and MRI data yielded higher outcome predictive accuracy than either modality alone. The addition of MRI ischemia to clinical variables and EEG background improved short-term outcome 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., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

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., Competing Interests: N. S. Abend: Funding from NIH (NINDS) K02NS096058 and Wolfson Foundation for this study. Other funding from PCORI (to institution), UCB Pharma (to institution), Epilepsy Foundation (consulting), and Demos Publishing (royalties). The remaining authors have no funding or conflicts of interest to disclose., (Copyright © 2022 by the American Clinical Neurophysiology Society.)

Background and Objectives: Epilepsy education has been transformed over the past 2 decades, leading to a need for structured formative assessment tools. The American Epilepsy Society developed the Epilepsy Fellowship In-Training Examination (EpiFITE) to provide high-quality formative assessment for fellows, to stimulate program improvement, and to guide future learning and teaching. The aim of this study was to explore validity evidence for the EpiFITE in meeting these goals., Methods: Validity evidence was sought from multiple sources. The content of the examination was linked to the American Board of Psychiatry and Neurology blueprint for initial certification in epilepsy, and items were developed by trained experts. Internal structure was studied using internal consistency and item analysis. Surveys of fellows and fellowship directors focused on the examination experience (response process) and how results influenced fellow assessment, future learning, and program improvement (relationship to other variables and consequences)., Results: The EpiFITE was first administered in 2020, with 172 examinees from 67 programs. By 2022 (year 3), the EpiFITE was completed by 195 epilepsy fellows from 77 programs. The overall mean score of the examination was stable from year to year, and the committee predicted the difficulty of individual items with a high degree of accuracy. The examination had high internal consistency (Cronbach α 0.76-0.81). The median item discrimination index ranged from 0.17 in 2020 to 0.21 in 2022. Discrimination indices were lower (mean ≤0.10) for items that were either very easy or very difficult and significantly higher (mean >0.20) for other items. Program directors and epilepsy fellows agreed the examination questions were appropriate and agreed that the EpiFITE helped them identify areas for self-directed learning. Program directors also found the examination helpful in identifying areas of strength and areas for improvement within their programs., Discussion: There are several sources of evidence of the quality and validity of the EpiFITE. By exploring this validity evidence, we have identified several best practices in the development and evaluation of a subspecialty examination, and this experience could be helpful for developers of in-training examinations in other subspecialties., Competing Interests: The authors report no disclosures relevant to the manuscript. Go to Neurology.org/NE for full disclosures., (© 2023 American Academy of Neurology.)

Objectives: We aimed to determine the incidence of periodic and rhythmic patterns (PRP), assess the interrater agreement between electroencephalographers scoring PRP using standardized terminology, and analyze associations between PRP and electrographic seizures (ES) in critically ill children., Methods: This was a prospective observational study of consecutive critically ill children undergoing continuous electroencephalographic monitoring (CEEG). PRP were identified by one electroencephalographer, and then two pediatric electroencephalographers independently scored the first 1-h epoch that contained PRP using standardized terminology. We determined the incidence of PRPs, evaluated interrater agreement between electroencephalographers scoring PRP, and evaluated associations between PRP and ES., Results: One thousand three hundred ninety-nine patients underwent CEEG. ES occurred in 345 (25%) subjects. PRP, ES + PRP, and ictal-interictal continuum (IIC) patterns occurred in 142 (10%), 81 (6%), and 93 (7%) subjects, respectively. The most common PRP were generalized periodic discharges (GPD; 43, 30%), lateralized periodic discharges (LPD; 34, 24%), generalized rhythmic delta activity (GRDA; 34, 24%), bilateral independent periodic discharges (BIPD; 14, 10%), and lateralized rhythmic delta activity (LRDA; 11, 8%). ES risk varied by PRP type (p < .01). ES occurrence was associated with GPD (odds ratio [OR] = 6.35, p < .01), LPD (OR = 10.45, p < .01), BIPD (OR = 6.77, p < .01), and LRDA (OR = 6.58, p < .01). Some modifying features increased the risk of ES for each of those PRP. GRDA was not significantly associated with ES (OR = 1.34, p = .44). Each of the IIC patterns was associated with ES (OR = 6.83-8.81, p < .01). ES and PRP occurred within 6 h (before or after) in 45 (56%) subjects., Significance: PRP occurred in 10% of critically ill children who underwent CEEG. The most common patterns were GPD, LPD, GRDA, BIPD, and LRDA. The GPD, LPD, BIPD, LRDA, and IIC patterns were associated with ES. GRDA was not associated with ES., (© 2021 International League Against Epilepsy.)

Purpose: Neonatal seizures are common and difficult to identify clinically because the majority are subclinical and correct identification of electroclinical seizures based on semiology is unreliable. Therefore, continuous EEG monitoring (CEEG) is critical for seizure identification in neonates and is recommended as the gold standard method in American Clinical Neurophysiology Society guidelines. Despite these recommendations, barriers to implementing widespread CEEG exist., Methods: To expand access to CEEG for at-risk neonates, a framework for providing remote CEEG was established at two network hospital neonatal intensive care units. Utilization and clinical impact were tracked as a quality improvement study., Results: In a 27-month period from June 2017 through September 2019, 76 neonates underwent CEEG between the two network neonatal intensive care units. Electrographic seizures occurred in about one quarter of records (18/76; 24%), though their incidence varied by CEEG indication. Care notes indicated that CEEG impacted clinical care in three quarters of cases (57/76; 75%). Continuous EEG impacted decisions to treat with anti-seizure medications in approximately one half of patients (impact: 28/57 [49%]; no impact 29/57 [51%]), and CEEG impacted prognostic discussions in approximately two thirds of patients (impact: 39/57 [68%]; no impact 18/57 [32%])., Conclusions: Establishment of a remote CEEG program for neonates is feasible, effective at identifying seizures, and improves the quality of care provided to neonates hospitalized at these network hospitals., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2020 by the American Clinical Neurophysiology Society.)

Aims: Assessment of brain injury severity early after cardiac arrest (CA) may guide therapeutic interventions and help clinicians counsel families regarding neurologic prognosis. We aimed to determine whether adding EEG features to predictive models including clinical variables and examination signs increased the accuracy of short-term neurobehavioral outcome prediction., Methods: This was a prospective, observational, single-center study of consecutive infants and children resuscitated from CA. Standardized EEG scoring was performed by an electroencephalographer for the initial EEG timepoint after return of spontaneous circulation (ROSC) and each 12-h segment from the time of ROSC up to 48 h. EEG Background Category was scored as: (1) normal; (2) slow-disorganized; (3) discontinuous or burst-suppression; or (4) attenuated-featureless. The primary outcome was neurobehavioral outcome at discharge from the Pediatric Intensive Care Unit. To develop the final predictive model, we compared areas under the receiver operating characteristic curves (AUROC) from models with varying combinations of Demographic/Arrest Variables, Examination Signs, and EEG Features., Results: We evaluated 89 infants and children. Initial EEG Background Category was normal in 9 subjects (10%), slow-disorganized in 44 (49%), discontinuous or burst suppression in 22 (25%), and attenuated-featureless in 14 (16%). The final model included Demographic/Arrest Variables (witnessed status, doses of epinephrine, initial lactate after ROSC) and EEG Background Category which achieved AUROC of 0.9 for unfavorable neurobehavioral outcome and 0.83 for mortality., Conclusions: The addition of standardized EEG Background Categories to readily available CA variables significantly improved early stratification of brain injury severity after pediatric CA., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (© 2021 American Academy of Neurology.)

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., (Copyright © 2021. Published by Elsevier Ltd.)

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., (© 2020 International League Against Epilepsy.)

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., (© 2020 American Academy of Neurology.)

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.

Rationale: Implementation of electronic health records may improve the quality, accuracy, timeliness, and availability of documentation. Thus, our institution developed a system that integrated EEG ordering, scheduling, standardized reporting, and billing. Given the importance of user perceptions for successful implementation, we performed a quality improvement study to evaluate electroencephalographer satisfaction with the new EEG report system., Methods: We implemented an EEG report system that was integrated in an electronic health record. In this single-center quality improvement study, we surveyed electroencephalographers regarding overall acceptability, report standardization, workflow efficiency, documentation quality, and fellow education using a 0 to 5 scale (with 5 denoting best)., Results: Eighteen electroencephalographers responded to the survey. The median score for recommending the overall system to a colleague was 5 (range 3-5), which indicated good overall satisfaction and acceptance of the system. The median scores for report standardization (4; 3-5) and workflow efficiency (4.5; 3-5) indicated that respondents perceived the system as useful and easy to use for documentation tasks. The median scores for quality of documentation (4.5; 1-5) and fellow education (4; 1-5) indicated that although most respondents believed the system provided good quality reports and helped with fellow education, a small number of respondents had substantially different views (ratings of 1)., Conclusions: Overall electroencephalographer satisfaction with the new EEG report system was high, as were the scores for perceived usefulness (assessed as standardization, documentation quality, and education) and ease of use (assessed as workflow efficiency). Future study is needed to determine whether implementation yields useful data for clinical research and quality improvement studies or improves EEG report standardization.

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., (Wiley Periodicals, Inc. © 2020 International League Against Epilepsy.)

Objective: Guidelines recommend that encephalopathic critically ill children undergo continuous electroencephalographic (CEEG) monitoring for electrographic seizure (ES) identification and management. However, limited data exist on antiseizure medication (ASM) safety for ES treatment in critically ill children., Methods: We performed a single-center prospective observational study of encephalopathic critically ill children undergoing CEEG. Clinical and EEG features and ASM utilization patterns were evaluated. We determined the incidence, types, and risk factors for adverse events associated with ASM administration., Results: A total of 472 consecutive critically ill children undergoing CEEG were enrolled. ES occurred in 131 children (28%). Clinicians administered ASM to 108 children with ES (82%). ES terminated after the initial ASM in 38% of patients who received one ASM, after the second ASM in 35% of patients who received two ASMs, after the third ASM in 50% of patients who received three ASMs, and after the fourth ASM in 53% of patients who received four ASMs. Thirty patients (28%) received anesthetic infusions for ES management. Adverse events occurred in 18 patients (17%). Adverse effects were expected and resolved in all patients, and they were generally serious (in 15 patients) and definitely related (in 12 patients). Adverse events were rare in patients with acute symptomatic seizures requiring only one to two ASMs for treatment, but were more common in children with epilepsy, ictal-interictal continuum EEG patterns, or patients requiring more extensive ASM management., Significance: ES ceased after one ASM in only 38% of critically ill children but ceased after two ASMs in 73% of critically ill children. Thus, ES management was often accomplished with readily available medications, but optimization of multistep ES management strategies might be beneficial. Adverse events were rare and manageable in children with acute symptomatic seizures requiring only one to two ASMs for treatment. Future studies are needed to determine whether management of acute symptomatic ES improves neurobehavioral outcomes., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

Purpose: We aimed to determine which early EEG features and feature combinations most accurately predicted short-term neurobehavioral outcomes and survival in children resuscitated after cardiac arrest., Methods: This was a prospective, single-center observational study of infants and children resuscitated from cardiac arrest who underwent conventional EEG monitoring with standardized EEG scoring. Logistic regression evaluated the marginal effect of each EEG variable or EEG variable combinations on the outcome. The primary outcome was neurobehavioral outcome (Pediatric Cerebral Performance Category score), and the secondary outcome was mortality. The authors identified the models with the highest areas under the receiver operating characteristic curve (AUC), evaluated the optimal models using a 5-fold cross-validation approach, and calculated test characteristics maximizing specificity., Results: Eighty-nine infants and children were evaluated. Unfavorable neurologic outcome (Pediatric Cerebral Performance Category score 4-6) occurred in 44 subjects (49%), including mortality in 30 subjects (34%). A model incorporating a four-level EEG Background Category (normal, slow-disorganized, discontinuous or burst-suppression, or attenuated-flat), stage 2 Sleep Transients (present or absent), and Reactivity-Variability (present or absent) had the highest AUC. Five-fold cross-validation for the optimal model predicting neurologic outcome indicated a mean AUC of 0.75 (range, 0.70-0.81) and for the optimal model predicting mortality indicated a mean AUC of 0.84 (range, 0.76-0.97). The specificity for unfavorable neurologic outcome and mortality were 95% and 97%, respectively. The positive predictive value for unfavorable neurologic outcome and mortality were both 86%., Conclusions: The specificity of the optimal model using a combination of early EEG features was high for unfavorable neurologic outcome and mortality in critically ill children after cardiac arrest. However, the positive predictive value was only 86% for both outcomes. Therefore, EEG data must be considered in overall clinical context when used for neuroprognostication early after cardiac arrest.

Purpose: Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns., Methods: Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients., Results: Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair-substantial; kappa = 0.375-0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate-substantial; kappa = 0.493-0.643), delta activity presence (fair-moderate; kappa = 0.369-0.432), theta activity presence (fair-moderate; kappa = 0.347-0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair-moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate-substantial; kappa = 0.43-0.67) and overall impression (moderate-substantial; kappa = 0.42-0.68)., Conclusions: This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

Purpose: Electroencephalographic seizures (ES) are common among neonates with hypoxic-ischemic encephalopathy (HIE), and they represent a treatable complication that might improve neurodevelopmental outcomes. We aimed to establish whether higher ES exposure was predictive of unfavorable outcomes while adjusting for other important clinical and electroencephalographic parameters., Methods: We performed a single-center, retrospective study of consecutive neonates with HIE managed with therapeutic hypothermia from June 2010 through December 2016. Neonates underwent continuous electroencephalographic (cEEG) monitoring during and after therapeutic hypothermia. Outcome measures included abnormal MRIs after rewarming and abnormal motor and language development., Results: Clinical data from the perinatal period were available for 116 neonates. Follow-up data were available for 93 of 116 (80%) neonates who survived to discharge, with a median follow-up period of 23 months (interquartile range 1236 months). Multivariate analysis demonstrated that high ES exposure (OR 5.2, 95% CI 1.3-21.2, p = 0.02) and moderate/severely abnormal EEG background (OR 8.3, 95% CI 1.6-43.9, p = 0.01) were independent predictors of abnormal motor development. High ES exposure was an independent predictor of abnormal language development (OR 4.2, 95% CI 1.1-15.9, p = 0.04). High ES exposure (OR 7.0, 95% CI 2.2-22.5, p = 0.01) and severe encephalopathy (OR 7.9, 95% CI 1.5-42.7, p = 0.02) were independent predictors of abnormal MRIs., Conclusions: Among neonates with HIE managed with therapeutic hypothermia, high ES exposure was the most important predictor of abnormal developmental and neuroimaging outcomes, even after adjustment for multiple clinical and EEG variables. Adequate identification and management of ES with judicious use of anti-seizure medications may optimize outcomes., (Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Background: Presurgical evaluation with antiseizure medication tapering in patients with refractory epilepsy places them at risk for seizure clustering or prolonged seizures. We looked at the occurrence of seizure clustering (3 or more seizures within 24h) and prolonged seizures and the factors that influence seizure clustering and affect length of stay (LOS) in pediatric patients during presurgical monitoring., Methods: We retrospectively reviewed the medical records of all consecutive admissions to the epilepsy monitoring unit (EMU) and included patients undergoing noninvasive presurgical evaluation. Data were extracted regarding demographics, seizure history, details of the EMU admission including occurrence of seizure clusters, prolonged seizures, status epilepticus, treatment, and LOS., Results: Sixty-nine patients met our inclusion criteria. Seizure clustering during monitoring was observed in 33 patients (48%). Prolonged seizures lasting >5min was observed in 14 (20%) patients including 2 with status epilepticus (3%). Seizure clusters necessitated rescue treatment in around 30%. History of seizure clustering at home was the only factor associated with the occurrence of seizure clustering during the EMU stay (p<0.0001). The LOS did not differ significantly between patients who had seizure clustering during monitoring versus those who did not (p=0.369)., Conclusions: Seizure clustering was common in children undergoing presurgical monitoring and seen especially in those with a history of seizure clustering at home. Occurrence of seizure clustering did not prolong the LOS but necessitated the use of rescue medications in about a third of the patients with seizure clusters due to multiple seizures., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Olfactory receptor neurons (ORNs) undergo caspase-mediated retrograde apoptosis after target removal (bulbectomy), in which axonal caspase-9 and caspase-3 activation leads to terminal apoptosis in ORN soma of the olfactory epithelium. Here, we show that caspase-8 can act as an initiator of ORN apoptosis after bulbectomy and also after synaptic instability is induced by NMDA-mediated excitotoxic death of ORN target neurons in the olfactory bulb. Caspase-8 and caspase-3 are sequentially activated within ORN presynaptic terminals, and caspase-8 complexes with dynactin p150Glued, (a retrograde motor protein) and is transported retrogradely, preceding axonal caspase-3 activation and apoptosis of ORN cell bodies. Focal in vivo inhibition of initiator caspase activation or microtubule-dependent transport (with Taxol) at the lesioned axon terminus results in a significant reduction in retrograde axonal caspase-8 and caspase-3 activation and inhibition of retrograde ORN death. Caspase-8 activation and retrograde transport after NMDA lesion is similarly reduced in mice null for p75, the low-affinity nerve growth factor receptor. The retrograde apoptosis of ORNs thus involves a novel mechanism that used p75 in the local activation of caspase-8. Once caspase-8 is maximally activated in the presynaptic terminal, it is transported retrogradely by the motor complex dynactin/dynein, a process that can be inhibited focally to inhibit ORN apoptosis after acute axonal lesion. These data have revealed a novel mechanism of retrograde apoptosis, in which caspase-8 complexes directly with axonal dynactin p150Glued to reveal a differential vulnerability of subpopulations of ORNs to undergo apoptosis after axonal damage and the loss of olfactory bulb target neurons.