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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.

ObjectivesTo determine the optimal duration of continuous EEG monitoring (CEEG) for electrographic seizure (ES) identification in critically ill children.MethodsWe 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 ResultsES occurred in 184 children (26%). Patients achieved ConclusionsA 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.ClinicalTrials.gov identifierNCT03419260.

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

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.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.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.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.

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

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 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.

Therapeutic hypothermia is being utilized as a neuro-protective strategy in neonates, children, and adults. The most common indications are hypoxic ischemic encephalopathy in neonates and post cardiac arrest in adults. Electroencephalographic monitoring use is increasing in critical care units, and is sometimes a component of therapeutic hypothermia clinical pathways. Monitoring may detect non-convulsive seizures or non-convulsive status epilepticus, and it may provide prognostic information. We review data regarding indications for therapeutic hypothermia and electroencephalographic monitoring in neonatal, pediatric, and adult critical care units, and discuss technical aspects related to such monitoring.

Background: Retrospective studies have reported the occurrence of nonconvulsive seizures in critically ill children. We aimed to prospectively determine the incidence and risk factors of nonconvulsive seizures in critically ill children using predetermined EEG monitoring indications and EEG interpretation terminology. Methods: Critically ill children (non-neonates) with acute encephalopathy underwent continuous EEG monitoring if they met institutional clinical practice criteria. Study enrollment and data collection were prospective. Logistic regression analysis was utilized to identify risk factors for seizure occurrence. Results: One hundred children were evaluated. Electrographic seizures occurred in 46 and electrographic status epilepticus occurred in 19. Seizures were exclusively nonconvulsive in 32. The only clinical risk factor for seizure occurrence was younger age ( p = 0.03). Of patients with seizures, only 52% had seizures detected in the first hour of monitoring, while 87% were detected within 24 hours. Conclusions: Seizures were common in critically ill children with acute encephalopathy. Most were nonconvulsive. Clinical features had little predictive value for seizure occurrence. Further study is needed to confirm these data in independent high-risk populations, to clarify which children are at highest risk for seizures so limited monitoring resources can be allocated optimally, and to determine whether seizure detection and management improves outcome.

Electroencephalographic (EEG) features may provide objective data regarding prognosis in children resuscitated from cardiac arrest (CA), but therapeutic hypothermia (TH) may impact its predictive value. We aimed to determine whether specific EEG features were predictive of short-term outcome in children treated with TH after CA, both during hypothermia and after return to normothermia.Thirty-five children managed with a standard clinical TH algorithm after CA were prospectively enrolled. EEG recordings were scored in a standardized manner and categorized. EEG category 1 consisted of continuous and reactive tracings. EEG category 2 consisted of continuous but unreactive tracings. EEG category 3 included those with any degree of discontinuity, burst suppression, or lack of cerebral activity. The primary outcome was unfavorable short-term outcome defined as Pediatric Cerebral Performance Category score of 4-6 (severe disability, vegetative, death) at hospital discharge. Univariate analyses of the association between EEG category and outcome was performed using logistic regression.For tracings obtained during hypothermia, patients with EEGs in categories 2 or 3 were far more likely to have poor outcome than those in category 1 (OR 10.7, P = 0.023 and OR 35, P = 0.004, respectively). Similarly, for tracings obtained during normothermia, patients with EEGs in categories 2 or 3 were far more likely to have poor outcomes than those in category 1 (OR 27, P = 0.006 and OR 18, P = 0.02, respectively).A simple EEG classification scheme has predictive value for short-term outcome in children undergoing TH after CA.

Continuous EEG (cEEG) monitoring is being used with increasing frequency in critically ill patients, most often to detect non-convulsive seizures. While cEEG is non-invasive and feasible in the critical care setting, it is also expensive and labor intensive, and there has been little study of its impact on clinical care. We aimed to determine prospectively the impact of cEEG on clinical management in critically ill children.Critically ill children (non-neonates) with acute encephalopathy underwent cEEG. Study enrollment and data collection were prospective.100 children were studied. EEG monitoring led to specific clinical management changes in 59 children. These included initiating or escalating anti-seizure medications in 43 due to seizure detection, demonstrating that a specific event (subtle movement or vital sign change) was not a seizure in 21, or obtaining urgent neuroimaging that led to a clinical change in 3. In the remaining 41 children, cEEG ruled out the presence of non-convulsive seizures but did not lead to a specific change in clinical management.EEG monitoring led to changes in clinical management in the majority of patients, suggesting it may have an important role in management of critically ill children. Further study is needed to determine whether the management changes elicited by cEEG improve outcome.

Twenty-nine boys with attention deficit disorder/hyperactivity were randomly assigned to receive desipramine (DMI; n = 17) or placebo (n = 12) for 14 days in a noncrossover, double-blind study. There was immediate behavioral improvement with DMI at day 3 that was sustained for 2 weeks; behavioral improvement did not correlate with plasma concentrations of DMI, hydroxy-DMI, or their sum at either days 3 or 14. There were no untoward side effects; there was a drug-induced increase in pulse and diastolic blood pressure. During drug therapy, the urinary excretion of norepinephrine, vanillymandelic acid, and 3-methoxy-4-hydroxyphenylglycol (MHPG) was decreased at both days 3 and 14. The plasma MHPG level was decreased at days 3 and 14 and (standing) plasma NE levels increased at day 14. The decreases in both urinary and plasma MHPG levels showed significant correlations with behavioral improvement during the second week. These data corroborate previous findings on sympathomimetic effects of tricyclic antidepressants in children and support a noradrenergic mechanism in the mediation of drug effects on attention deficit disorder/hyperactivity.

Hostile, impulsive, and aggressive behaviors have been associated with decreased urinary free cortisol (UFC) output. This is the first study to assess 24-hour UFC output in disruptive behavior disorders. The UFC of 19 boys with attention deficit and/or conduct disorder was compared with that of 19 age, race, and IQ matched normal controls studied as inpatients. Continuous Performance Test scores showed greater impulsivity in the disruptive group. UFC output did not differ between groups. However, UFC and difficulty were positively correlated on the continuous performance test in the form of longer interstimulus intervals and longer reaction times.

We study how classification accuracy can be improved when both different data preprocessing methods and computerized consensus diagnosis (CCD) are applied to 1H magnetic resonance (MR) spectra of astrocytomas, meningiomas, and epileptic brain tissue. The MR spectra (360 MHz, 37 degrees C) of tissue specimens (biopsies) from subjects with meningiomas (95; 26 cases), astrocytomas (74; 26 cases), and epilepsy (37; 8 cases) were preprocessed by several methods. Each data set was partitioned into training and validation sets. Robust classification was carried out via linear discriminant analysis (LDA), artificial neural nets (NN), and CCD, and the results were compared with histopathological diagnosis of the MR specimens. Normalization of the relevant spectral regions affects classification accuracy significantly. The spectra-based average three-class classification accuracies of LDA and NN increased from 81.7% (unnormalized data sets) to 89.9% (normalized). CCD increased the classification accuracy of the normalized sets to an average of 91.8%. CCD invariably decreases the fraction of unclassifiable spectra. The same trends prevail, with improved results, for case-based classification. Preprocessing the 1H MR spectra is essential for accurate and reliable classification of astrocytomas, meningiomas, and nontumorous epileptic brain tissue. CCD improves classification accuracy, with an attendant decrease in the fraction of unclassifiable spectra or cases.