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Early EEG Features for Outcome Prediction After Cardiac Arrest in Children

Fung, France W.*,†; Topjian, Alexis A.‡,§; Xiao, Rui; Abend, Nicholas S.*,†,§

Journal of Clinical Neurophysiology: September 2019 - Volume 36 - Issue 5 - p 349–357
doi: 10.1097/WNP.0000000000000591
Original Research
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Purpose: We aimed to determine which early EEG features and feature combinations most accurately predicted short-term neurobehavioral outcomes and survival in children resuscitated after cardiac arrest.

Methods: This was a prospective, single-center observational study of infants and children resuscitated from cardiac arrest who underwent conventional EEG monitoring with standardized EEG scoring. Logistic regression evaluated the marginal effect of each EEG variable or EEG variable combinations on the outcome. The primary outcome was neurobehavioral outcome (Pediatric Cerebral Performance Category score), and the secondary outcome was mortality. The authors identified the models with the highest areas under the receiver operating characteristic curve (AUC), evaluated the optimal models using a 5-fold cross-validation approach, and calculated test characteristics maximizing specificity.

Results: Eighty-nine infants and children were evaluated. Unfavorable neurologic outcome (Pediatric Cerebral Performance Category score 4–6) occurred in 44 subjects (49%), including mortality in 30 subjects (34%). A model incorporating a four-level EEG Background Category (normal, slow-disorganized, discontinuous or burst-suppression, or attenuated-flat), stage 2 Sleep Transients (present or absent), and Reactivity–Variability (present or absent) had the highest AUC. Five-fold cross-validation for the optimal model predicting neurologic outcome indicated a mean AUC of 0.75 (range, 0.70–0.81) and for the optimal model predicting mortality indicated a mean AUC of 0.84 (range, 0.76–0.97). The specificity for unfavorable neurologic outcome and mortality were 95% and 97%, respectively. The positive predictive value for unfavorable neurologic outcome and mortality were both 86%.

Conclusions: The specificity of the optimal model using a combination of early EEG features was high for unfavorable neurologic outcome and mortality in critically ill children after cardiac arrest. However, the positive predictive value was only 86% for both outcomes. Therefore, EEG data must be considered in overall clinical context when used for neuroprognostication early after cardiac arrest.

*Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A.;

Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.;

Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A.;

§Department of Anesthesia & and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.; and

Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.

Address correspondence and reprint requests to Nicholas S. Abend, MD, MSCE, Children's Hospital of Philadelphia, Division of Neurology, CTRB 100163501, Civic Center Boulevard, Philadelphia, PA 19104, U.S.A.; e-mail: abend@email.chop.edu.

N.S. Abend receives funding from the National Institute of Health (National Institute of Neurological Disorders and Stroke), Patient-Centered Outcomes Research Institute, and Epilepsy Foundation of America, which are all paid to his institution. N.S. Abend is funded by the National Institute of Health K02NS096058. The remaining authors have no funding or conflicts of interest to disclose.

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© 2019 by the American Clinical Neurophysiology Society