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

Abstract

AimsAssessment 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. MethodsThis 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. ResultsWe 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. ConclusionsThe addition of standardized EEG Background Categories to readily available CA variables significantly improved early stratification of brain injury severity after pediatric CA.