Electroencephalographic seizures in critically ill children: Management and adverse events

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.

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.

Survey of Pediatric ICU EEG Monitoring-Reassessment After a Decade

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.

Periodic Discharges in Critically Ill Children: Predictors and Outcome

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.

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.

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.

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.

Clinical Features and Management of Status Epilepticus in the Pediatric Intensive Care Unit

OBJECTIVES

Status epilepticus (SE) is associated with significant morbidity and mortality in children. SE in the pediatric intensive care unit (PICU) are not well characterized. The aim of this study is to retrospectively investigate the clinical features and treatment of seizures in children admitted to the PICU of our hospital.

METHODS

We retrospectively examined the clinical characteristics of patients aged between 1 month and 18 years who were admitted to our hospital with SE or who were diagnosed with SE after hospitalization and were followed up with continuous electroencephalographic monitoring between January 2015 and December 2019.

RESULTS

A total of 88 patients with SE, 50 (56.8%) boys and 38 (43.2%) girls, were included. The median age was 24 months (interquartile range, 12-80 months). When we evaluate the continuous electroencephalographic monitoring data, 27 (30.7%) were lateralized, 20 (22.7%) were multifocal, 30 (34.1%) were generalized, and 11 (12.5%) were bilateral independent epileptic activity. Seventy nine patients (89.8%) were evaluated as convulsive status epilepticus (CSE) and 9 (10.2%) as nonconvulsive status epilepticus (NCSE). Pediatric Risk of Mortality (PRISM III) score and mortality of patients with NCSE were higher ( P = 0.004 and P = 0.046, respectively). Thirteen eight patients (43.1%) were diagnosed as SE, 38 patients (43.1%) as refractory SE, and 12 patients (13.6%) as super-refractory SE. The overall mortality rate was 10.2%.

CONCLUSIONS

Status epilepticus is a neurological emergency that causes mortality and morbidity. Electroencephalographic monitoring is important for the recognition of seizures and rapid intervention. No superiority of second-line treatments or combined treatments was demonstrated in patients with SE.

Acute Neurologic Dysfunction in Critically Ill Children: The PODIUM Consensus Conference

CONTEXT

Acute neurologic dysfunction is common in critically ill children and contributes to outcomes and end of life decision-making.

OBJECTIVE

To develop consensus criteria for neurologic dysfunction in critically ill children by evaluating the evidence supporting such criteria and their association with outcomes.

DATA SOURCES

Electronic searches of PubMed and Embase were conducted from January 1992 to January 2020, by using a combination of medical subject heading terms and text words to define concepts of neurologic dysfunction, pediatric critical illness, and outcomes of interest.

STUDY SELECTION

Studies were included if the researchers evaluated critically ill children with neurologic injury, evaluated the performance characteristics of assessment and scoring tools to screen for neurologic dysfunction, and assessed outcomes related to mortality, functional status, organ-specific outcomes, or other patient-centered outcomes. Studies with an adult population or premature infants (≤36 weeks' gestational age), animal studies, reviews or commentaries, case series with sample size ≤10, and studies not published in English with an inability to determine eligibility criteria were excluded.

DATA EXTRACTION

Data were abstracted from each study meeting inclusion criteria into a standard data extraction form by task force members.

DATA SYNTHESIS

The systematic review supported the following criteria for neurologic dysfunction as any 1 of the following: (1) Glasgow Coma Scale score ≤8; (2) Glasgow Coma Scale motor score ≤4; (3) Cornell Assessment of Pediatric Delirium score ≥9; or (4) electroencephalography revealing attenuation, suppression, or electrographic seizures.

CONCLUSIONS

We present consensus criteria for neurologic dysfunction in critically ill children.

Periodic and rhythmic patterns in critically ill children: Incidence, interrater agreement, and seizures

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.

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.

KCC2 Chloride Transport Contributes to the Termination of Ictal Epileptiform Activity

Recurrent seizures intensely activate GABAA receptors (GABAA-Rs), which induces transient neuronal chloride ([Cl-]i) elevations and depolarizing GABA responses that contribute to the failure of inhibition that engenders further seizures and anticonvulsant resistance. The K+-Cl- cotransporter KCC2 is responsible for Cl- extrusion and restoration of [Cl-]i equilibrium (ECl) after synaptic activity, but at the cost of increased extracellular potassium which may retard K+-Cl- extrusion, depolarize neurons, and potentiate seizures. Thus, KCC2 may either diminish or facilitate seizure activity, and both proconvulsant and anticonvulsant effects of KCC2 inhibition have been reported. It is now necessary to identify the loci of these divergent responses by assaying both the electrographic effects and the ionic effects of KCC2 manipulation. We therefore determined the net effects of KCC2 transport activity on cytoplasmic chloride elevation and Cl- extrusion rates during spontaneous recurrent ictal-like epileptiform discharges (ILDs) in organotypic hippocampal slices in vitro, as well as the correlation between ionic and electrographic effects. We found that the KCC2 antagonist VU0463271 reduced Cl- extrusion rates, increased ictal [Cl-]i elevation, increased ILD duration, and induced status epilepticus (SE). In contrast, the putative KCC2 upregulator CLP257 improved chloride homeostasis and reduced the duration and frequency of ILDs in a concentration-dependent manner. Our results demonstrate that measuring both the ionic and electrographic effects of KCC2 transport clarify the impact of KCC2 modulation in specific models of epileptiform activity. Anticonvulsant effects predominate when KCC2-mediated chloride transport rather than potassium buffering is the rate-limiting step in restoring ECl and the efficacy of GABAergic inhibition during recurrent ILDs.

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.

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.