We evaluated the interobserver agreement in the interpretation of EEG samples in critically ill children who were comatose or obtunded after cardiac arrest. The greatest agreement was found for continuity state (continuous, discontinuous, or flat), burst suppression (present or absent), overall rating (severely abnormal or not), and sleep architecture (present or absent). Agreement was lower but still fair for seizure detection and more subtle EEG features such as fastest and predominant frequency, voltage, beta activity, and inter-ictal epileptiform discharge presence and type.
The issue of agreement in EEG interpretation has a long history. Investigators have examined EEG interpretation in critically ill patients (Gerber, et al. 2008
, Ronner, et al. 2009
), patients with new onset seizures (Stroink, et al. 2006
), and children with idiopathic epilepsy (Piccinelli, et al. 2005
). All described large variability in EEG interpretation. For example, a study of interobserver agreement in tracings from critically ill adults reported that agreement was substantial for the presence or absence and localization of rhythmic discharges but lower for more subtle features such as rhythmic discharge duration, persistence, and onset type (Gerber, et al. 2008
). In another study of interobserver agreement in seizure detection in critically ill adults, moderate agreement was demonstrated, but agreement was lower for more subtle seizure descriptors including their frequency, onset, and offset (Ronner, et al. 2009
). On the other hand, EEG scoring systems of broad categories, each composed of multiple interpretive features, have been studied in comatose adults and found to have nearly perfect or substantial agreement (Young, et al. 1997
). Our data fits well with these past studies. Following group discussion and review, improved agreement could be obtained for most tracings. This confirms a prior study that demonstrated agreement improved from the fair-substantial level to the almost perfect range after group discussion and implementation of rules (Azuma, et al. 2003
Following consensus discussion, several points that might lead to better agreement for some of the major EEG features studied. Careful use of time-locked video was important in distinguishing whether periodic epileptiform discharges were inter-ictal or ictal. Subtle myoclonic movements time-locked to the discharges establishes an ictal diagnosis. In addition to time locked video, adding other physiologic information such as EMG or respiratory pattern could further enhance the ability to differentiate between ictal and interictal discharges, but this information is currently not used in our ICU. Furthermore, within the limitations of an isolated 30 minute EEG snapshot, it was often unclear whether periodic epileptiform discharges represented an inter-ictal pattern or the middle of non-convulsive status epilepticus. Longer tracings and anticonvulsant response information may allow improved/more accurate categorization of these tracings. However, controversy persists regarding the management of periodic epileptiform discharges (Chong and Hirsch 2005
). Further study is needed to better categorize and understand the importance of periodic epileptiform discharges.
Providing accurate prognosis in comatose children after cardiac arrest, is clinically important but difficult (Abend and Licht 2008
). Historical and current clinical information can be unreliable. For example, arrest duration may be unknown and clinical examination findings may be confounded by pharmacologic intervention. Thus, utilizing non-invasive neurophysiologic testing at the bedside is appealing. EEG features reported to be predictive of poor outcome in children with hypoxic ischemic encephalopathy include low amplitude and electrocerebral silence (Tasker, et al. 1988
), discontinuity and lack of reactivity and epileptiform discharges (Mandel, et al. 2002
), and lack of reactivity and lack of normal sleep architecture (Cheliout-Heraut, et al. 1991
). Another study that created an EEG grade utilizing continuity, frequency, and voltage found an association between worse EEG grades and poor outcome (Nishisaki, et al. 2007
). While these studies suggest a prognostic role for EEG, our data suggest some of the features utilized for outcome prediction may not be interpreted in a standard manner. Future studies focused on prognostication may have improved accuracy and generalizability by focusing on EEG features with high inter-rater agreement. This will require clear and unambiguous definitions which can be reproducibly applied by many EEG readers and possibly by supplemented by employing quantitative analysis (Wennervirta, et al. 2009
Recent studies have observed that non-convulsive seizures are common in critically ill children (Abend and Dlugos 2007
, Abend, et al. 2009
, Alehan, et al. 2001
, Hosain, et al. 2005
, Hyllienmark and Amark 2007
, Jette, et al. 2006
, Saengpattrachai, et al. 2006
, Tay, et al. 2006
) and non-convulsive seizures may impact outcome in critically ill adults (Carrera, et al. 2008
, Oddo, et al. 2009
, Young, et al. 1996
). In addition to identifying EEG features with prognostic significance, attempts have also been made to identify EEG features that are predictive of seizures which would allow limited EEG monitoring resources to be directed to the highest risk patients. A study of critically ill children found that lateralized but not generalized or bilateral periodic epileptiform discharges predicted non-convulsive seizures (Jette, et al. 2006
). In another study of children undergoing therapeutic hypothermia after cardiac arrest, burst suppression or excessive discontinuity, inter-ictal spike/sharp waves, or the absence of expected pharmacologic beta activity were predictive of seizures (Abend, et al. 2009
). However, the current data suggest there may be wide variability in identifying some EEG features, and further definition of these terms or quantitative analysis may be needed. Further, the fact that inter-reader agreement for seizure detection was only moderate in our analysis suggests that these studies along with our clinical efforts to detect and treat non-convulsive seizures must be viewed in the context of variable detection since interpretation of the “gold standard” may not be perfectly standardized.
This study has several limitations. First, only 30 minute tracings were studied and it is possible that interpretation of some features of longer tracings may have worse agreement. On the other hand, in instances of periodic discharges, longer samples may have allowed interpreters to distinguish between the middle of status epilepticus and an inter-ictal periodic pattern. Second, this was a single institution study in which the participating neurophysiologists frequently review EEGs together and agreement may have been higher than if readers were from different institutions. Third, we exclusively studied EEGs obtained from children with diffuse brain injury after cardiac arrest so there was little opportunity to evaluate focal features. Fourth, readers were not provided data regarding subject age or medical exposure since they were asked to describe the EEG tracing and not to determine whether it was normal or not given a given age or pharmacologic exposure. However, this may impact the generalizability of our findings since in clinical practice readers have at least this basic clinical information which could impact clinical EEG interpretation.
In conclusion, we have demonstrated that in critically ill children certain inter-ictal EEG features (continuity, burst suppression) have high inter-reader agreement while other features have much lower agreement. The identification of EEG features that have high inter-rater reliability lays the ground-work to evaluate which EEG features are accurate and generalizable predictors of outcome.