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.

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.

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

OBJECTIVE: Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. METHODS: We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. RESULTS: The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES. SIGNIFICANCE: A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy.

Electroencephalographic seizures in critically ill children: Management and adverse events.

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.

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.

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.

Video Ambulatory EEG in Children: A Quality Improvement Study.

PURPOSE: We implemented a video ambulatory EEG (VA-EEG) Program as an alternative to inpatient video EEG monitoring for some patients given potential benefits related to quicker access, greater convenience, and lower cost. To evaluate the newly initiated program, we performed a quality improvement study to assess whether VA-EEG yielded studies with interpretable EEG and video quality that generated clinically beneficial data. METHODS: This was a single-center prospective quality improvement study. We surveyed ordering clinicians, electroencephalographers, and caregivers regarding consecutive children who underwent clinically indicated VA-EEG. The primary outcome was the percentage of VA-EEG studies in which the ordering clinician reported that the study had answered the question of interest. RESULTS: We evaluated 74 consecutive children selected to undergo clinically indicated VA-EEG by their clinicians and caregivers. Ordering clinicians reported that 77% of studies answered the question of interest. Electroencephalographers reported that the quality of the EEG and video was excellent or adequate in 100% and 92% of patients, respectively. Additionally, 84% of caregivers reported preferring VA-EEG if EEG data were needed in the future. CONCLUSIONS: Video ambulatory EEG may be an effective diagnostic modality among children selected by clinicians and caregivers to undergo long-term EEG monitoring. Given it is effective as well as convenient, accessible, and lower cost than inpatient EEG monitoring, all of which align with our institution's quality goals, we intend to expand our VA-EEG Program.

Electrographic Seizures and Outcome in Critically Ill Children.

OBJECTIVE: To determine the association between electroencephalographic seizure (ES) and electroencephalographic (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 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 pre-admission 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 subjects, there was no evidence of ES in 535 subjects (74.4%), ES in 140 subjects (19.5%), and ESE in 44 subjects (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 the Pediatric Index of Mortality 2 score. There was an association between ESE and unfavorable GOS-E-Peds (Odds Ratio 2.21, 95%CI 1.07-4.54) and PCPC (Odds Ratio 2.17, 95%CI 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.

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.

Collodion Remover Can Degrade Plastic-Containing Medical Devices Commonly Used in the Intensive Care Unit.

Collodion remover, a solvent blend used to remove collodion glue after long-term video EEG monitoring, was implicated as a potential causative factor in patient safety events at our institution during which damage to plastic components of medical devices was noted in the intensive care unit. We sought to determine experimentally whether collodion remover could lead to degradation of multiple plastic-containing medical devices commonly used in the intensive care unit to determine whether workflow changes were needed during electrode removal. We exposed devices to collodion remover for brief, intermediate, and prolonged durations. We report that collodion remover is capable of degrading the hard plastic components of multiple medical devices after prolonged exposure; however, intermediate duration exposure was also capable of producing damage to clave connectors used with intravenous and central lines, which could plausibly lead to adverse events given the widespread use of these devices. These data suggest a pathway-based approach to collodion remover use might be beneficial in minimizing the potential impact of this solvent on plastic-containing medical devices.