Interrater Reliability of Experts in Identifying Interictal Epileptiform Discharges in Electroencephalograms.

Importance: The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias. Objective: To assess the reliability of experts in detecting IEDs in routine EEGs. Design, Setting, and Participants: This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016. Main Outcomes and Measures: Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts. Results: Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%). Conclusions and Relevance: This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.

Clinical Development and Implementation of an Institutional Guideline for Prospective EEG Monitoring and Reporting of Delayed Cerebral Ischemia.

Delayed cerebral ischemia (DCI) is the most common and disabling complication among patients admitted to the hospital for subarachnoid hemorrhage (SAH). Clinical and radiographic methods often fail to detect DCI early enough to avert irreversible injury. We assessed the clinical feasibility of implementing a continuous EEG (cEEG) ischemia monitoring service for early DCI detection as part of an institutional guideline. An institutional neuromonitoring guideline was designed by an interdisciplinary team of neurocritical care, clinical neurophysiology, and neurosurgery physicians and nursing staff and cEEG technologists. The interdisciplinary team focused on (1) selection criteria of high-risk patients, (2) minimization of safety concerns related to prolonged monitoring, (3) technical selection of quantitative and qualitative neurophysiologic parameters based on expert consensus and review of the literature, (4) a structured interpretation and reporting methodology, prompting direct patient evaluation and iterative neurocritical care, and (5) a two-layered quality assurance process including structured clinician interviews assessing events of neurologic worsening and an adjudicated consensus review of neuroimaging and medical records. The resulting guideline's clinical feasibility was then prospectively evaluated. The institutional SAH monitoring guideline used transcranial Doppler ultrasound and cEEG monitoring for vasospasm and ischemia monitoring in patients with either Fisher group 3 or Hunt-Hess grade IV or V SAH. Safety criteria focused on prevention of skin breakdown and agitation. Technical components included monitoring of transcranial Doppler ultrasound velocities and cEEG features, including quantitative alpha:delta ratio and percent alpha variability, qualitative evidence of new focal slowing, late-onset epileptiform activity, or overall worsening of background. Structured cEEG reports were introduced including verbal communication for findings concerning neurologic decline. The guideline was successfully implemented over 27 months, during which neurocritical care physicians referred 71 SAH patients for combined transcranial Doppler ultrasound and cEEG monitoring. The quality assurance process determined a DCI rate of 48% among the monitored population, more than 90% of which occurred during the duration of cEEG monitoring (mean 6.9 days) beginning 2.7 days after symptom onset. An institutional guideline implementing cEEG for SAH ischemia monitoring and reporting is feasible to implement and efficiently identify patients at high baseline risk of DCI during the period of monitoring.