Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs

Jin Jing, Wendong Ge, Aaron F. Struck, Marta Bento Fernandes, Shenda Hong, Sungtae An, Safoora Fatima, Aline Herlopian, Ioannis Karakis, Jonathan J. Halford, Marcus C. Ng, Emily L. Johnson, Brian L. Appavu, Rani A. Sarkis, Gamaleldin Osman, Peter W. Kaplan, Monica B. Dhakar, Lakshman Arcot Jayagopal, Zubeda Sheikh, Olga TaraschenkoSarah Schmitt, Hiba A. Haider, Jennifer A. Kim, Christa B. Swisher, Nicolas Gaspard, Mackenzie C. Cervenka, Andres A. Rodriguez Ruiz, Jong Woo Lee, Mohammad Tabaeizadeh, Emily J. Gilmore, Kristy Nordstrom, Ji Yeoun Yoo, Manisha G. Holmes, Susan T. Herman, Jennifer A. Williams, Jay Pathmanathan, Fábio A. Nascimento, Ziwei Fan, Samaneh Nasiri, Mouhsin M. Shafi, Sydney S. Cash, Daniel B. Hoch, Andrew J. Cole, Eric S. Rosenthal, Sahar F. Zafar, Jimeng Sun, M. Brandon Westover

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Background and Objectives The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as “ictal-interictal-injury continuum” (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. Methods This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as “seizure (SZ),” “lateralized periodic discharges (LPDs),” “generalized periodic discharges (GPDs),” “lateralized rhythmic delta activity (LRDA),” “generalized rhythmic delta activity (GRDA),” or “other.” EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. Results Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained (R2): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics (R2: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. Discussion Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. Classification of Evidence This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.

Original languageEnglish (US)
Pages (from-to)E1737-E1749
Issue number17
StatePublished - Apr 25 2023

ASJC Scopus subject areas

  • Clinical Neurology


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