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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.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 was15%,10%,5%, or2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors.A model aiming to determine the CEEG duration at which a patient had10% 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. The10% 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.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.

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.

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.

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.

Background: Hypoxic ischemic brain injury secondary to pediatric cardiac arrest (CA) may result in acute symptomatic seizures. A high proportion of seizures may be nonconvulsive, so accurate diagnosis requires continuous EEG monitoring. We aimed to determine the safety and feasibility of long-term EEG monitoring, to describe electroencephalographic background and seizure characteristics, and to identify background features predictive of seizures in children undergoing therapeutic hypothermia (TH) after CA. Methods: Nineteen children underwent TH after CA. Continuous EEG monitoring was performed during hypothermia (24 hours), rewarming (12–24 hours), and then an additional 24 hours of normothermia. The tolerability of these prolonged studies and the EEG background classification and seizure characteristics were described in a standardized manner. Results: No complications of EEG monitoring were reported or observed. Electrographic seizures occurred in 47% (9/19), and 32% (6/19) developed status epilepticus. Seizures were nonconvulsive in 67% (6/9) and electrographically generalized in 78% (7/9). Seizures commenced during the late hypothermic or rewarming periods (8/9). Factors predictive of electrographic seizures were burst suppression or excessively discontinuous EEG background patterns, interictal epileptiform discharges, or an absence of the expected pharmacologically induced beta activity. Background features evolved over time. Patients with slowing and attenuation tended to improve, whereas those with burst suppression tended to worsen. Conclusions: EEG monitoring in children undergoing therapeutic hypothermia after cardiac arrest is safe and feasible. Electrographic seizures and status epilepticus are common in this setting but are often not detectable by clinical observation alone. The EEG background often evolves over time, with milder abnormalities improving and more severe abnormalities worsening. BS = burst suppression; CA = cardiac arrest; CPR = cardiopulmonary resuscitation; DD = developmental delay; FEN = fentanyl; FOS = fosphenytoin; HIE = hypoxic ischemic encephalopathy; LEV = levetiracetam; LZP = lorazepam; MDZ = midazolam; NCS = nonconvulsive seizures; NCSE = nonconvulsive status epilepticus; NPV = negative predictive value; PB = phenobarbital; PED = periodic epileptiform discharge; PICU = pediatric intensive care unit; PPV = positive predictive value; SE = status epilepticus; SIDS = sudden infant death syndrome; sz = seizures; TH = therapeutic hypothermia; VEC = vecuronium; VPA = valproic acid; VT = ventricular tachycardia.