Many neonates undergo electroencephalogram (EEG) monitoring to identify and manage acute symptomatic seizures. Information about brain function contained in the EEG background data may also help predict neurobehavioral outcomes. For EEG background features to be useful as prognostic indicators, the interpretation of these features must be standardized across electroencephalographers. We aimed at determining the interrater and intrarater agreement among electroencephalographers interpreting neonatal EEG background patterns.
Five neonatal electroencephalographers reviewed 5-to-7.5-minute epochs of EEG from full-term neonates who underwent continuous conventional EEG monitoring. The EEG assessment tool used to classify background patterns was based on the American Clinical Neurophysiology Society's guideline for neonatal EEG terminology. Interrater and intrarater agreement were measured using Kappa coefficients.
Interrater agreement was consistently highest for voltage (binary: substantial, kappa = 0.783; categorical: moderate, kappa = 0.562), seizure presence (fair–substantial; kappa = 0.375–0.697), continuity (moderate; kappa = 0.481), burst voltage (moderate; kappa = 0.574), suppressed background presence (moderate–substantial; kappa = 0.493–0.643), delta activity presence (fair–moderate; kappa = 0.369–0.432), theta activity presence (fair–moderate; kappa = 0.347–0.600), presence of graphoelements (fair; kappa = 0.381), and overall impression (binary: moderate, kappa = 0.495; categorical: fair–moderate, kappa = 0.347, 0.465). Agreement was poor or inconsistent for all other patterns. Intrarater agreement was variable, with highest average agreement for voltage (binary: substantial, kappa = 0.75; categorical: substantial, kappa = 0.714) and highest consistent agreement for continuity (moderate–substantial; kappa = 0.43–0.67) and overall impression (moderate–substantial; kappa = 0.42–0.68).
This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.
*Department of Pediatrics (Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A.;
†Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A.;
‡Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A.;
§Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A.; and
‖Department of Anesthesia and Critical Care, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A.
Address correspondence and reprint requests to Shavonne L. Massey, MD, Colket Translational Research Building Room 10011, 3501 Civic Center Boulevard, Philadelphia, PA 19104, U.S.A.; e-mail: email@example.com.
The authors have no funding or conflicts of interest to disclose.
This work was funded by NIH T32-NS061779 and K02-NS096058.
J. Farrar and N. S. Abend contributed equally as senior authors.
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