Quantitative Electroencephalography (EEG) Predicting Acute Neurologic Deterioration in the Pediatric Intensive Care Unit: A Case Series

Abstract

Introduction: Continuous neurologic assessment in the pediatric intensive care unit is challenging. Current electroencephalography (EEG) guidelines support monitoring status epilepticus, vasospasm detection, and cardiac arrest prognostication, but the scope of brain dysfunction in critically ill patients is larger. We explore quantitative EEG in pediatric intensive care unit patients with neurologic emergencies to identify quantitative EEG changes preceding clinical detection. Methods: From 2017 to 2020, we identified pediatric intensive care unit patients at a single quaternary children's hospital with EEG recording near or during acute neurologic deterioration. Quantitative EEG analysis was performed using Persyst P14 (Persyst Development Corporation). Included features were fast Fourier transform, asymmetry, and rhythmicity spectrograms, “from-baseline” patient-specific versions of the above features, and quantitative suppression ratio. Timing of quantitative EEG changes was determined by expert review and prespecified quantitative EEG alert thresholds. Clinical detection of neurologic deterioration was defined pre hoc and determined through electronic medical record documentation of examination change or intervention. Results: Ten patients were identified, age 23 months to 27 years, and 50% were female. Of 10 patients, 6 died, 1 had new morbidity, and 3 had good recovery; the most common cause of death was cerebral edema and herniation. The fastest changes were on “from-baseline” fast Fourier transform spectrograms, whereas persistent changes on asymmetry spectrograms and suppression ratio were most associated with morbidity and mortality. Median time from first quantitative EEG change to clinical detection was 332 minutes (interquartile range: 201-456 minutes). Conclusion: Quantitative EEG is potentially useful in earlier detection of neurologic deterioration in critically ill pediatric intensive care unit patients. Further work is required to quantify the predictive value, measure improvement in outcome, and automate the process.