Abstracts

Rationale: Research using neonatal electroencephalography (EEG) has been stymied by the lack of universal classification and terminology for interpretation. In 2013, the American Clinical Neurophysiology Society (ACNS) published a guideline for standardized terminology and categorization for the description of continuous EEG monitoring in neonates. The aim of this study was to assess interrater agreement for this neonatal EEG categorization among a group of expert pediatric neurophysiologists. Methods: A total of 60 neonatal EEGs were collected from 3 institutions. Recordings were 3-hour segments from continuous EEG monitoring of term neonates receiving hypothermia for hypoxic-ischemic encephalopathy. Three pediatric neurophysiologists independently reviewed each record using a standardized scoring form based on the ACNS guideline. Unweighted kappa values were calculated for interrater agreement of categorical data across multiple observers. Results: Interrater agreement was very good for identification of seizures (κ= 0.93, p<0.0001), with agreement in 95% of records (57 of 60). Interrater agreement was moderate for identifying records as normal versus having any abnormality (κ= 0.49, p<0.0001), with consensus in 78% of records (47 of 60). Agreement was good in categorizing EEG backgrounds on a 5-category scale (normal, excessively discontinuous, burst suppression, status epilepticus, and electrocerebral inactivity) (κ= 0.70, p<0.0001), with agreement in 71% of records (43 of 60). More specific background features had lower interrater agreement, including voltage (κ= 0.41, p<0.0001), variability (κ= 0.35, p<0.0001), and symmetry (κ= 0.18, p=0.012). Interrater agreement for presence of pathologic negative and positive sharp waves was low (κ= 0.17, p=0.01 and κ= 0.13, p=0.02, respectively). Likewise, there was low agreement regarding identification of brief rhythmic discharges (κ= 0.19, p=0.007). Conclusions: We found good or very good interrater agreement using the ACNS neonatal terminology and classification guideline for identification of seizures and for categorization of overall EEG background. We found moderate agreement using this system to categorize neonatal EEGs as normal versus having any abnormality. More specific EEG background features had limited interrater agreement. These data support the use of the ACNS guidelines in identifying seizures and classifying backgrounds of neonatal EEGs. Our findings also suggest that there may be limited reproducibility for more specific details of EEG background and graphoelements as defined by the guidelines. Future detailed studies of neonatal EEG should account for the potentially poor agreement regarding specific EEG features. There may be a need to redefine or simplify these classification elements in future revisions of the ACNS terminology guidelines.