Stability of Early EEG Background Patterns After Pediatric Cardiac Arrest

Neuromonitoring in the ICU: noninvasive and invasive modalities for critically ill children and neonates

PURPOSE OF REVIEW

Critically ill children are at risk of neurologic dysfunction and acquiring primary and secondary brain injury. Close monitoring of cerebral function is crucial to prevent, detect, and treat these complications.

RECENT FINDINGS

A variety of neuromonitoring modalities are currently used in pediatric and neonatal ICUs. These include noninvasive modalities, such as electroencephalography, transcranial Doppler, and near-infrared spectroscopy, as well as invasive methods including intracranial pressure monitoring, brain tissue oxygen measurement, and cerebral microdialysis. Each modality offers unique insights into neurologic function, cerebral circulation, or metabolism to support individualized neurologic care based on a patient's own physiology. Utilization of these modalities in ICUs results in reduced neurologic injury and mortality and improved neurodevelopmental outcomes.

SUMMARY

Monitoring of neurologic function can significantly improve care of critically ill children. Additional research is needed to establish normative values in pediatric patients and to standardize the use of these modalities.

Association of EEG Background With Diffusion-Weighted Magnetic Resonance Neuroimaging and Short-Term Outcomes After Pediatric Cardiac Arrest

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 mm2/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 (χ2 = 19.1, p < 0.001) and unfavorable neurologic outcome (χ2 = 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.

Perioperative Neuromonitoring in Children with Congenital Heart Disease

Although neonates and children with congenital heart disease are primarily hospitalized for cardiac and pulmonary diseases, they are also at an increased risk for neurologic injury due to both empiric differences that can exist in their nervous systems and acquired injury from cardiopulmonary pathology and interventions. Although early efforts in care focused on survival after reparative cardiac surgery, as surgical and anesthetic techniques have evolved and survival rates accordingly improved, the focus has now shifted to maximizing outcomes among survivors. Children and neonates with congenital heart disease experience seizures and poor neurodevelopmental outcomes at a higher rate than age-matched counterparts. The aim of neuromonitoring is to help clinicians identify patients at highest risk for these outcomes to implement strategies to mitigate these risks and to also help with neuroprognostication after an injury has occurred. The mainstays of neuromonitoring are (1) electroencephalographic monitoring to evaluate brain activity for abnormal patterns or changes and to identify seizures, (2) neuroimaging to reveal structural changes and evidence of physical injury in and around the brain, and (3) near-infrared spectroscopy to monitor brain tissue oxygenation and detect changes in perfusion. This review will detail the aforementioned techniques and their use in the care of pediatric patients with congenital heart disease.

Neuromonitoring after Pediatric Cardiac Arrest: Cerebral Physiology and Injury Stratification

BACKGROUND

Significant long-term neurologic disability occurs in survivors of pediatric cardiac arrest, primarily due to hypoxic-ischemic brain injury. Postresuscitation care focuses on preventing secondary injury and the pathophysiologic cascade that leads to neuronal cell death. These injury processes include reperfusion injury, perturbations in cerebral blood flow, disturbed oxygen metabolism, impaired autoregulation, cerebral edema, and hyperthermia. Postresuscitation care also focuses on early injury stratification to allow clinicians to identify patients who could benefit from neuroprotective interventions in clinical trials and enable targeted therapeutics.

METHODS

In this review, we provide an overview of postcardiac arrest pathophysiology, explore the role of neuromonitoring in understanding postcardiac arrest cerebral physiology, and summarize the evidence supporting the use of neuromonitoring devices to guide pediatric postcardiac arrest care. We provide an in-depth review of the neuromonitoring modalities that measure cerebral perfusion, oxygenation, and function, as well as neuroimaging, serum biomarkers, and the implications of targeted temperature management.

RESULTS

For each modality, we provide an in-depth review of its impact on treatment, its ability to stratify hypoxic-ischemic brain injury severity, and its role in neuroprognostication.

CONCLUSION

Potential therapeutic targets and future directions are discussed, with the hope that multimodality monitoring can shift postarrest care from a one-size-fits-all model to an individualized model that uses cerebrovascular physiology to reduce secondary brain injury, increase accuracy of neuroprognostication, and improve outcomes.

Magnetic Resonance Imaging Adds Prognostic Value to EEG After Pediatric Cardiac Arrest

AIM

To investigate how combined electrographic and radiologic data inform outcomes in children after cardiac arrest.

METHODS

Retrospective observational study of children admitted to the pediatric intensive care unit (PICU) of a tertiary children's hospital with diagnosis of cardiac arrest from 2009 to 2016. The first 20 min of electroencephalogram (EEG) background was blindly scored. Presence and location of magnetic resonance imaging (MRI) diffusion-weighted image (DWI) abnormalities were correlated with T2-weighted signal. Outcomes were categorized using Pediatric Cerebral Performance Category (PCPC) scores at hospital discharge, with "poor outcome" reflecting a PCPC score of 4-6. Logistic regression models examined the association of EEG and MRI variables with outcome.

RESULTS

41 children met inclusion criteria and had both post-arrest EEG monitoring within 72 hours after ROSC and brain MRI performed within 8 days. Among the 19 children with poor outcome, 10 children did not survive to discharge. Severely abnormal EEG background (p < 0.0001) and any diffusion restriction (p < 0.0001) were associated with poor outcome. The area under the ROC curve (AUC) for identifying outcome based on EEG background alone was 0.86, which improved to 0.94 with combined EEG and MRI data (p = 0.02).

CONCLUSION

Diffusion abnormalities on MRI within 8 days after ROSC add to the prognostic value of EEG background in children surviving cardiac arrest.

Multimodal monitoring including early EEG improves stratification of brain injury severity after pediatric cardiac arrest

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.

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.

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.

Continuous Electroencephalography Monitoring in Critically Ill Infants and Children

Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.

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.

Quantitative EEG predicts outcomes in children after cardiac arrest

OBJECTIVE

To determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.

METHODS

We performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.

RESULTS

The best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1-3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4-6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.

CONCLUSIONS

QEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.