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Interrater agreement for Critical Care EEG Terminology.

ABSTRACT: The interpretation of critical care electroencephalography (EEG) studies is challenging because of the presence of many periodic and rhythmic patterns of uncertain clinical significance. Defining the clinical significance of these patterns requires standardized terminology with high interrater agreement (IRA). We sought to evaluate IRA for the final, published American Clinical Neurophysiology Society (ACNS)-approved version of the critical care EEG terminology (2012 version). Our evaluation included terms not assessed previously and incorporated raters with a broad range of EEG reading experience.After reviewing a set of training slides, 49 readers independently completed a Web-based test consisting of 11 identical questions for each of 37 EEG samples (407 questions). Questions assessed whether a pattern was an electrographic seizure; pattern location (main term 1), pattern type (main term 2); and presence and classification of eight other key features ("plus" modifiers, sharpness, absolute and relative amplitude, frequency, number of phases, fluctuation/evolution, and the presence of "triphasic" morphology).IRA statistics (? values) were almost perfect (90-100%) for seizures, main terms 1 and 2, the +S modifier (superimposed spikes/sharp waves or sharply contoured rhythmic delta activity), sharpness, absolute amplitude, frequency, and number of phases. Agreement was substantial for the +F (superimposed fast activity) and +R (superimposed rhythmic delta activity) modifiers (66% and 67%, respectively), moderate for triphasic morphology (58%), and fair for evolution (21%).IRA for most terms in the ACNS critical care EEG terminology is high. These terms are suitable for multicenter research on the clinical significance of critical care EEG patterns. A PowerPoint slide summarizing this article is available for download in the Supporting Information section http://dx.doi.org/10.1111/epi.12653/supinfo.

SUBMITTER: Gaspard N

PROVIDER: S-EPMC4879939 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Interrater agreement for Critical Care EEG Terminology.

Gaspard Nicolas N Hirsch Lawrence J LJ LaRoche Suzette M SM Hahn Cecil D CD Westover M Brandon MB

Epilepsia 20140602 9

<h4>Objective</h4>The interpretation of critical care electroencephalography (EEG) studies is challenging because of the presence of many periodic and rhythmic patterns of uncertain clinical significance. Defining the clinical significance of these patterns requires standardized terminology with high interrater agreement (IRA). We sought to evaluate IRA for the final, published American Clinical Neurophysiology Society (ACNS)-approved version of the critical care EEG terminology (2012 version). ...[more]

PMID: 24888711

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Project description:OBJECTIVE:The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED. METHODS:IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization. RESULTS:Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11. CONCLUSIONS:The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10). SIGNIFICANCE:By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.

Project description:PURPOSE: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. METHODS: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. RESULTS: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). CONCLUSIONS:This study demonstrates substantial variability in neonatal EEG background interpretation across electroencephalographers, indicating a need for educational and technological strategies aimed at improving performance.

Project description:BackgroundAnterior cruciate ligament (ACL) rupture is the most common ligament injury treated surgically by orthopaedic surgeons. The gold standard for the treatment of the majority of primary ACL tears is ACL reconstruction. However, novel methods of repair, such as bridge-enhanced ACL repair (BEAR), are currently being investigated as alternatives to reconstruction. To assess patients for midsubstance repair suitability, clarify the prognostic implications of injury location and damage, and evaluate the results of a repair technique, it is important to have a baseline classification system or grading scale that is reproducible across surgeons, particularly for multicenter collaboration. Currently, no such system or scale exists.PurposeTo develop an arthroscopic ACL tear classification system and to evaluate its interobserver reliability.Study designCohort study (diagnosis); Level of evidence, 3.MethodsEleven fellowship-trained orthopaedic surgeon investigators reviewed 75 video clips containing arthroscopic evaluation of a torn ACL and then completed the 6-question ACL Pathology Evaluation Form. Agreement statistics including exact agreement, Fleiss ?, Gwet agreement coefficient 1 (AC1), and Gwet AC2 were then calculated to assess interobserver reliability.ResultsIn aggregate, the multiple assessments of observer reproducibility revealed that surgeon participants in this study, when evaluating the same injury, agreed roughly 80% of the time on whether (1) at least 50% of the tibial footprint remained, (2) the remaining tibial stump was ?10 mm, and (3) the injury was therefore reparable using the BEAR procedure. Participants also agreed roughly 60% of the time on exactly how many suturable bundles were available. These characteristics are believed to be most important, among those studied, in determining whether a torn ACL is amenable to midsubstance repair.ConclusionThis study is the first of its kind to demonstrate the interobserver reliability of arthroscopic classification of ACL tears. We have demonstrated that this classification system, though not ideally reproducible, is reliable enough across surgeons at multiple institutions for use in multicenter studies.RegistrationNCT03776162 (ClinicalTrials.gov identifier).

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Interrater agreement for Critical Care EEG Terminology.

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Interrater agreement for Critical Care EEG Terminology.

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