Interrater reliability of ICU EEG research terminology

Systematic Evaluation of Research Priorities in Critical Care Electroencephalography

PURPOSE

The Critical Care EEG Monitoring Research Consortium (CCEMRC) is an international research group focusing on critical care EEG and epilepsy. As CCEMRC grew to include 50+ institutions over the past decade, members met to establish research priorities.

METHODS

The authors used an analytical hierarchy process-based research prioritization method, adapted from an approach previously applied to a Department of Defense health-related research program. Forty-six CCEMRC members identified and scored a set of eight clinical problems (CPs) and 15 research topic areas (RTAs) at an annual CCEMRC meeting. Members scored CPs on three criteria using a five-point ordinal scale: Incidence, Impact, and Gap Size; and RTAs on four additional criteria: Niche, Feasibility, Scientific Importance, and Medical Importance, each of which was assigned a weight. The first three RTA criteria were scored using a five-point scale, and CPs were mapped to RTAs using a four-point scale. The Medical Importance score was a weighted average of its mapping scores and the CP score. Finally, a Priority score was calculated for each RTA as a product of the four RTA criteria scores.

RESULTS

The CPs with the highest scores were "Altered mental status" and "Long-term neurologic disability after hospital discharge." The RTAs with the highest priority scores were "Development of risk prediction tools," "Multicenter observational studies," and "Outcome prediction."

CONCLUSIONS

Research prioritization helped CCEMRC evaluate its current research trajectory, identify high-priority near-term research pursuits, and create a roadmap for future research plans aligned with its mission. This approach may be helpful to other academic consortia and research programs.

Heart rate variability for the evaluation of patients with disorders of consciousness

OBJECTIVE

Clinical responsiveness of patients with a Disorder of Consciousness (DoC) correlates to sympathetic/parasympathetic homeostatic balance. Heart Rate Variability (HRV) metrics result in non-invasive proxies of modulation capabilities of visceral states. In this work, our aim was to evaluate whether HRV measures could improve the differential diagnosis between Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS) with respect to multivariate models based on standard clinical electroencephalography (EEG) labeling only in a rehabilitation setting.

METHODS

A prospective observational study was performed consecutively enrolling 82 DoC patients. Polygraphic recordings were performed. HRV-metrics and EEG descriptors derived from the American Clinical Neurophysiology Society's Standardized Critical Care terminology were included. Descriptors entered univariate and then multivariate logistic regressions with the target set to the UWS/MCS diagnosis.

RESULTS

HRV measures resulted significantly different between UWS and MCS patients, with higher values being associated with better consciousness levels. Specifically, adding HRV-related metrics to ACNS EEG descriptors increased the Nagelkerke R² from 0.350 (only EEG descriptors) to 0.565 (HRV-EEG combination) with the outcome set to the consciousness diagnosis.

CONCLUSIONS

HRV changes across the lowest states of consciousness. Rapid changes in heart rate, occurring in better consciousness levels, confirm the mutual correlation between visceral state functioning patterns and consciousness alterations.

SIGNIFICANCE

Quantitative analysis of heart rate in patients with a DoC paves the way for the implementation of low-cost pipelines supporting medical decisions within multimodal consciousness assessments.

Personalized model to predict seizures based on dynamic and static continuous EEG monitoring data

BACKGROUND/OBJECTIVE

Early recognition of patients who may be at risk of developing acute symptomatic seizures would be useful. We aimed to determine whether continuous electroencephalography (cEEG) data using machine learning techniques such as neural networks and decision trees could predict seizure occurrence in hospitalized patients.

METHODS

This was a single center retrospective cohort analysis of cEEG data in patients aged 18-90 years who were admitted and underwent cEEG monitoring between 2010 and 2019 limited to 72 h excluding those who were seizing at the onset of recording. A total of 41,491 patients were reviewed; of these, 3874 were used to develop the static model and 1687 to develop the dynamic model (half with seizure and half without seizure in each cohort). Of these, 80% were randomly selected as derivation cohorts for each model and 20% were randomly selected as validation cohorts. Dynamic and static machine learning models (long short term memory (LSTM) and Extreme Gradient Boosting algorithm (XGBoost)) based on day-to-day dynamic EEG changes and binary static EEG features over the prior 72 h or until seizure, which ever was earlier, were used.

RESULTS

The static model was able to predict seizure occurrence based on cEEG data with sensitivity and specificity of 0.81 and 0.59, respectively, with an AUC of 0.70. The dynamic model was able to predict seizure occurrence with sensitivity and specificity of 0.72 and 0.80, respectively, and AUC of 0.81.

CONCLUSIONS

Machine learning models could be applied to cEEG data to predict seizure occurrence based on available cEEG data. Dynamic day-to-day EEG data are more useful in predicting seizures than binary static EEG data. These models could potentially be used to determine the need for ongoing cEEG monitoring and to prioritize resources.

The focused quantitative EEG bio-marker in studying childhood atrophic encephalopathy

Although it is a normal involution process in advanced age, brain atrophy—also termed atrophic encephalopathy—can also occur prematurely in childhood as a consequential effect of brain tissues injury through trauma or central nervous system infection, though in both normal and premature occurrences this condition always presents with loss of volume relative to the skull. A common tool for the functional study of brain activities is an electroencephalogram, but analyses of this have reportedly identified mismatches between qualitative and quantitative forms, particularly in the use of Delta-alpha ratio (DAR) indices, meaning that the values may be case dependent. The current study thus examines the value of Focused Occipital Beta-Alpha Ratio (FOBAR) as a modified biomarker for evaluating brain functional changes resulting from brain atrophy. This cross-sectional design study involves 260 patients under 18 years of age. Specifically, 207 patients with brain atrophy are compared with 53 control subjects with CT scan-proven normal brain volume. All the children underwent digital electroencephalography with brain mapping. Results show that alpha posterior dominant rhythm was present in 88 atrophic children and 44 controls. Beta as posterior dominant rhythm was present in an overwhelming 91.5% of atrophic subjects, with 0.009 p-values. The focused occipital Beta-alpha ratio correlated significantly with brain volume loss presented in diagonal brain fraction. The FOBAR and DAR values of the QEEG showed no significant correlation. This work concludes that QEEG cerebral dysfunctional studies may be etiologically and case dependent from the nature of the brain injury. Also, the focused Beta-alpha ratio of the QEEG is a prospective and potential biomarker of consideration in studying childhood atrophic encephalopathy.

Corticothalamic Connectivity in Aneurysmal Subarachnoid Hemorrhage: Relationship with Disordered Consciousness and Clinical Outcomes

BACKGROUND

We present an exploratory analysis of the occurrence of early corticothalamic connectivity disruption after aneurysmal subarachnoid hemorrhage (SAH) and its correlation with clinical outcomes.

METHODS

We conducted a retrospective study of patients with acute SAH who underwent continuous electroencephalography (EEG) for impairment of consciousness. Only patients undergoing endovascular aneurysm treatment were included. Continuous EEG tracings were reviewed to obtain artifact-free segments. Power spectral analyses were performed, and segments were classified as A (only delta power), B (predominant delta and theta), C (predominant theta and beta), or D (predominant alpha and beta). Each incremental category from A to D implies greater preservation of corticothalamic connectivity. We dichotomized categories as AB for poor connectivity and CD for good connectivity. The modified Rankin Scale score at follow-up and in-hospital mortality were used as outcome measures.

RESULTS

Sixty-nine patients were included, of whom 58 had good quality EEG segments for classification: 28 were AB and 30 were CD. Hunt and Hess and World Federation of Neurological Surgeons grades were higher and the initial Glasgow Coma Scale score was lower in the AB group compared with the CD group. AB classification was associated with an adjusted odds ratio of 5.71 (95% confidence interval 1.61-20.30; p < 0.01) for poor outcome (modified Rankin Scale score 4-6) at a median follow-up of 4 months (interquartile range 2-6) and an odds ratio of 5.6 (95% confidence interval 0.98-31.95; p = 0.03) for in-hospital mortality, compared with CD.

CONCLUSIONS

EEG spectral-power-based classification demonstrates early corticothalamic connectivity disruption following aneurysmal SAH and may be a mechanism involved in early brain injury. Furthermore, the extent of this disruption appears to be associated with functional outcome and in-hospital mortality in patients with aneurysmal SAH and appears to be a potentially useful predictive tool that must be validated prospectively.

Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists

We aim to prospectively investigate, in a large and heterogeneous population, the electroencephalogram (EEG)-reading performances of EEG technologists. A total of 8 EEG technologists and 5 certified neurophysiologists independently analyzed 20-min EEG recordings. Interrater agreement (IRA) for predefined EEG pattern identification between EEG technologists and neurophysiologits was assessed using percentage of agreement (PA) and Gwet-AC1. Among 1528 EEG recordings, the PA [95% confidence interval] and interrater agreement (IRA, AC1) values were as follows: status epilepticus (SE) and seizures, 97% [96–98%], AC1 kappa = 0.97; interictal epileptiform discharges, 78% [76–80%], AC1 = 0.63; and conclusion dichotomized as “normal” versus “pathological”, 83.6% [82–86%], AC1 = 0.71. EEG technologists identified SE and seizures with 99% [98–99%] negative predictive value, whereas the positive predictive values (PPVs) were 48% [34–62%] and 35% [20–53%], respectively. The PPV for normal EEGs was 72% [68–76%]. SE and seizure detection were impaired in poorly cooperating patients (SE and seizures; p < 0.001), intubated and older patients (SE; p < 0.001), and confirmed epilepsy patients (seizures; p = 0.004). EEG technologists identified ictal features with few false negatives but high false positives, and identified normal EEGs with good PPV. The absence of ictal features reported by EEG technologists can be reassuring; however, EEG traces should be reviewed by neurophysiologists before taking action.

Electroencephalography Technologist Inter-rater Agreement and Interpretation of Pediatric Critical Care Electroencephalography

OBJECTIVES

Electroencephalography (EEG) technologists commonly screen continuous EEG. Until now, the inter-rater agreement or sensitivity for important EEG findings has been unknown in this group.

METHODS

Twenty-nine EEG technologists and three clinical neurophysiologists interpreted 90 five-minute samples of pediatric critical care EEG. Inter-rater agreement was examined with Cohen's kappa and Fleiss' kappa for EEG findings. A gold-standard consensus agreement was developed for examining sensitivity and specificity for seizures or discontinuity. Kruskal-Wallis tests with Benjamani-Hochberg corrections for multiple comparisons were utilized to examine associations between correct scoring and certification status and years of experience.

RESULTS

Aggregate agreement was moderate for seizures and fair for EEG background continuity among EEG technologists. Individual agreement for seizures and continuity varied from slight to substantial. For individual EEG technologists, sensitivity for seizures ranged from 44 to 93% and sensitivity for continuity ranged from 81 to 100%. Raters with Certified Long Term Monitoring credentials were more likely to identify seizures correctly.

SIGNIFICANCE

This is the first study to evaluate inter-rater agreement and interpretation correctness among EEG technologists interpreting pediatric critical care EEG. EEG technologists demonstrated better aggregate agreement for seizure detection than other EEG findings, yet individual results and internal consistency varied widely. These data provide important insight into the common practice of utilizing EEG technologists for screening critical care EEG.

Übersetzung der Standardisierten Terminologie für EEG bei Intensivstationspatienten der American Clinical Neurophysiological Society: Version 2012 (Hirsch et al. American Clinical Neurophysiology Society’s Standardized Critical Care EEG Terminology: 2012 version. J Clin Neurophysiol 2013; 30: 1–27)

2012 erarbeitete eine EEG-Expertengruppe der American Clinical Neurophysiology Society (ACNS) eine standardisierte Terminologie für EEG Muster, die bei kritisch kranken Patienten häufig sind. Bis dahin existierte keine einheitlich akzeptierte Nomenklatur für diese EEG Muster, wie zum Beispiel periodische Entladungen, fluktuierende rhythmische Muster und Kombinationen der beiden. Dabei bestand auch kein Konsens welche Muster mit welchem neuronalen Schaden korrelieren, welche Muster behandelt werden müssen oder wie aggressiv die Behandlung erfolgen sollte. Um diese Fragen anzugehen entwickelte diese Gruppe eine standardisierte Terminologie zunächst für den wissenschaftlichen Einsatz. Ziel war es die Kommunikation zu erleichtern, indem Begriffe mit klinischer Konnotation vermieden wurden und um damit multizentrische Forschung zu erleichtern.

Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns

BACKGROUND

Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG.

NEW METHOD

We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters.

RESULTS

Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency.

COMPARISON WITH EXISTING METHODS

Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods.

CONCLUSIONS

Our results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.

A standardized nomenclature for spectrogram EEG patterns: Inter-rater agreement and correspondence with common intensive care unit EEG patterns

OBJECTIVE

To determine the inter-rater agreement (IRA) of a standardized nomenclature for EEG spectrogram patterns, and to estimate the probability distribution of ictal-interictal continuum (IIC) patterns vs. other EEG patterns within each category in this nomenclature.

METHODS

We defined seven spectrogram categories: "Solid Flames", "Irregular Flames", "Broadband-monotonous", "Narrowband-monotonous", "Stripes", "Low power", and "Artifact". Ten electroencephalographers scored 115 spectrograms and the corresponding raw EEG samples. Gwet's agreement coefficient was used to calculate IRA.

RESULTS

Solid Flames represented seizures or IIC patterns 69.4% of the time. Irregular Flames represented seizures or IIC patterns 38.7% of the time. Broadband-monotonous primarily corresponded with seizures or IIC (54.3%) and Narrowband-monotonous with focal or generalized slowing (43.8%). Stripes were associated with burst-suppression (37.2%) and generalized suppression (34.4%). Low Power category was associated with generalized suppression (94%). There was "near perfect" agreement for Solid Flames (κ = 94.36), Low power (κ = 92.61), and Artifact (κ = 93.72). There was "substantial agreement" for all other categories (κ = 74.65-79.49).

CONCLUSIONS

This EEG spectrogram nomenclature has high IRA among electroencephalographers.

SIGNIFICANCE

The nomenclature can be a useful tool for EEG screening. Future studies are needed to determine if using this nomenclature shortens time to IIC identification, and how best to use it in practice to reduce time to intervention.

Early EEG Features for Outcome Prediction After Cardiac Arrest in Children

PURPOSE

We aimed to determine which early EEG features and feature combinations most accurately predicted short-term neurobehavioral outcomes and survival in children resuscitated after cardiac arrest.

METHODS

This was a prospective, single-center observational study of infants and children resuscitated from cardiac arrest who underwent conventional EEG monitoring with standardized EEG scoring. Logistic regression evaluated the marginal effect of each EEG variable or EEG variable combinations on the outcome. The primary outcome was neurobehavioral outcome (Pediatric Cerebral Performance Category score), and the secondary outcome was mortality. The authors identified the models with the highest areas under the receiver operating characteristic curve (AUC), evaluated the optimal models using a 5-fold cross-validation approach, and calculated test characteristics maximizing specificity.

RESULTS

Eighty-nine infants and children were evaluated. Unfavorable neurologic outcome (Pediatric Cerebral Performance Category score 4-6) occurred in 44 subjects (49%), including mortality in 30 subjects (34%). A model incorporating a four-level EEG Background Category (normal, slow-disorganized, discontinuous or burst-suppression, or attenuated-flat), stage 2 Sleep Transients (present or absent), and Reactivity-Variability (present or absent) had the highest AUC. Five-fold cross-validation for the optimal model predicting neurologic outcome indicated a mean AUC of 0.75 (range, 0.70-0.81) and for the optimal model predicting mortality indicated a mean AUC of 0.84 (range, 0.76-0.97). The specificity for unfavorable neurologic outcome and mortality were 95% and 97%, respectively. The positive predictive value for unfavorable neurologic outcome and mortality were both 86%.

CONCLUSIONS

The specificity of the optimal model using a combination of early EEG features was high for unfavorable neurologic outcome and mortality in critically ill children after cardiac arrest. However, the positive predictive value was only 86% for both outcomes. Therefore, EEG data must be considered in overall clinical context when used for neuroprognostication early after cardiac arrest.

Electroencephalographic seizures in critically ill children: Management and adverse events

OBJECTIVE

Guidelines recommend that encephalopathic critically ill children undergo continuous electroencephalographic (CEEG) monitoring for electrographic seizure (ES) identification and management. However, limited data exist on antiseizure medication (ASM) safety for ES treatment in critically ill children.

METHODS

We performed a single-center prospective observational study of encephalopathic critically ill children undergoing CEEG. Clinical and EEG features and ASM utilization patterns were evaluated. We determined the incidence, types, and risk factors for adverse events associated with ASM administration.

RESULTS

A total of 472 consecutive critically ill children undergoing CEEG were enrolled. ES occurred in 131 children (28%). Clinicians administered ASM to 108 children with ES (82%). ES terminated after the initial ASM in 38% of patients who received one ASM, after the second ASM in 35% of patients who received two ASMs, after the third ASM in 50% of patients who received three ASMs, and after the fourth ASM in 53% of patients who received four ASMs. Thirty patients (28%) received anesthetic infusions for ES management. Adverse events occurred in 18 patients (17%). Adverse effects were expected and resolved in all patients, and they were generally serious (in 15 patients) and definitely related (in 12 patients). Adverse events were rare in patients with acute symptomatic seizures requiring only one to two ASMs for treatment, but were more common in children with epilepsy, ictal-interictal continuum EEG patterns, or patients requiring more extensive ASM management.

SIGNIFICANCE

ES ceased after one ASM in only 38% of critically ill children but ceased after two ASMs in 73% of critically ill children. Thus, ES management was often accomplished with readily available medications, but optimization of multistep ES management strategies might be beneficial. Adverse events were rare and manageable in children with acute symptomatic seizures requiring only one to two ASMs for treatment. Future studies are needed to determine whether management of acute symptomatic ES improves neurobehavioral outcomes.

Interrater and Intrarater Agreement in Neonatal Electroencephalogram Background Scoring

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.

Quantitative EEG predicts outcomes in children after cardiac arrest

OBJECTIVE

To determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.

METHODS

We performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.

RESULTS

The best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1-3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4-6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.

CONCLUSIONS

QEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.

Definition and Classification of Periodic and Rhythmic Patterns

The growing use of continuous video-EEG recording in the inpatient setting, in particular in patients with varying degrees of encephalopathy, has yielded a window to the brain with an excellent temporal resolution. This increasingly available tool has become more than an instrument to detect nonconvulsive seizures (its primary use), and clinical indications span from ischemia detection in acute brain injuries, neuroprognostication of comatose patients, to monitoring the degree of encephalopathy. In this context, abnormal findings such as periodic discharges and rhythmic delta activity were increasingly recognized; however, significant subjectivity remained in the interpretation of these findings pertaining to key features regarding their spatial involvement, prevalence of occurrence, duration, associated morphologic features, and behavior. In 2005, the American Clinical Neurophysiology Society proposed standardized definitions and classification of electroencephalographic rhythmic and periodic patterns. This was subsequently revised in 2011 and in 2012 and is now being used by centers worldwide, with the final version published in early 2013 as an official guideline of the ACNS. The resulting uniform terminology has allowed for significant advances in the understanding of the pathophysiology, epileptogenic potential, and overall clinical implication of these patterns. Investigators across multiple institutions are now able to collaborate while exploring diagnostic and therapeutic algorithms to these patterns, an effort that may soon provide definitive evidence guiding treating clinicians on the management of these patients.

Interrater Agreement of EEG Interpretation After Pediatric Cardiac Arrest Using Standardized Critical Care EEG Terminology

PURPOSE

We evaluated interrater agreement of EEG interpretation in a cohort of critically ill children resuscitated after cardiac arrest using standardized EEG terminology.

METHODS

Four pediatric electroencephalographers scored 10-minute EEG segments from 72 consecutive children obtained 24 hours after return of circulation using the American Clinical Neurophysiology Society's (ACNS) Standardized Critical Care EEG terminology. The percent of perfect agreement and the kappa coefficient were calculated for each of the standardized EEG variables and a predetermined composite EEG background category.

RESULTS

The overall background category (normal, slow-disorganized, discontinuous, or attenuated-featureless) had almost perfect agreement (kappa 0.89).The ACNS Standardized Critical Care EEG variables had agreement that was (1) almost perfect for the seizures variable (kappa 0.93), (2) substantial for the continuity (kappa 0.79), voltage (kappa 0.70), and sleep transient (kappa 0.65) variables, (3) moderate for the rhythmic or periodic patterns (kappa 0.55) and interictal epileptiform discharge (kappa 0.60) variables, and (4) fair for the predominant frequency (kappa 0.23) and symmetry (kappa 0.31) variables. Condensing variable options led to improved agreement for the continuity and voltage variables.

CONCLUSIONS

These data support the use of the standardized terminology and the composite overall background category as a basis for standardized EEG interpretation for subsequent studies assessing EEG background for neuroprognostication after pediatric cardiac arrest.

Diagnostic utility of eight-channel EEG for detecting generalized or hemispheric seizures and rhythmic periodic patterns

•

Current practice lacks rapid detection tools to screen for seizures.

•

High agreement exists between neurologists’ diagnoses using full and reduced montage EEG.

•

Reduced channel EEG can be used to screen for generalized or hemispheric or rhythmic and periodic abnormalities.

Objectives

To compare the diagnostic utility of electroencephalography (EEG) using reduced, 8-channel montage (rm-EEG) to full, 18-channel montage (fm-EEG) for detection of generalized or hemispheric seizures and rhythmic periodic patterns (RPPs) by neurologists with extensive EEG training, neurology residents with minimal EEG exposure, and medical students without EEG experience.

Methods

We presented EEG samples in both fm-EEG (bipolar montage) and rm-EEG (lateral leads of bipolar montage) to 20 neurologists, 20 residents, and 42 medical students. Unanimous agreement of three senior epileptologists defined samples as seizures (n = 7), RPPs (n = 10), and normal or slowing (n = 20). Differences in median accuracy, sensitivity, and specificity were assessed using Wilcoxon signed-rank tests.

Results

Full and reduced EEG demonstrated similar accuracy when read by neurologists (fm-EEG: 95%, rm-EEG: 95%, p = 0.29), residents (fm-EEG: 80%, rm-EEG: 80%, p = 0.05), and students (fm-EEG: 60%, rm-EEG: 51%, p = 0.68). Moreover, neurologists’ sensitivity for detecting seizure activity was comparable between fm-EEG (100%) and rm-EEG (98%) (p = 0.17). Furthermore, the specificity of rm-EEG for seizures and RPP (neurologists: 100%, residents: 90%, students: 86%) was significantly greater than that of fm-EEG (neurologists: 93%, p = 0.03; residents: 80%, p = 0.01; students: 69%, p < 0.001).

Conclusions

The reduction of the number of EEG channels from 18 to 8 does not compromise neurologists’ sensitivity for detecting seizures that are often a core reason for performing urgent EEG. It may also increase their specificity for detecting rhythmic and periodic patterns, and thereby providing important diagnostic information to guide patient’s management.

Significance

Our study is the first to document the utility of a reduced channel EEG above the hairline compared to full montage EEG in aiding medical staff with varying degrees of EEG training to detect generalized or hemispheric seizures.

EEG Characteristics of Successful Burst Suppression for Refractory Status Epilepticus

BACKGROUND

Refractory status epilepticus (RSE) is often treated with continuous intravenous medications with the goal of EEG burst suppression. Standard advice is to titrate medications to at least 10-s interburst intervals; however, this has not been shown to improve outcome. We examined EEG characteristics in patients treated with IV anesthetic therapy (IVAT) for RSE to determine which EEG characteristics were associated with successful lifting of IVAT (i.e., without recurrence of status epilepticus).

METHODS

We screened the clinical continuous EEG database for adult patients treated with IVAT for RSE (excluding patients with anoxic injury). We measured the length of bursts and interburst intervals for each patient, calculated EEG burst suppression ratios, and graded bursts for the amount of epileptiform activity. We compared these characteristics in successful versus unsuccessful IVAT lifting attempts.

RESULTS

We included 17 successful and 20 unsuccessful lifting attempts in 19 patients (5 used as a holdout validation set). The interburst intervals, burst suppression ratios, and length of bursts did not differentiate successful and unsuccessful lifting attempts; the amount of epileptiform activity in bursts correlated with success or failure to wean IVAT (p = 0.008). Maximum burst amplitude <125 μV had 84.6 % sensitivity and 61.1 % specificity for predicting successful lifting.

CONCLUSION

The length of interburst intervals and burst suppression did not predict successful termination of RSE in this small cohort. This may suggest that EEG characteristics, rather a strict interburst interval goal, could guide IVAT for RSE.

Continuous Electroencephalography (cEEG) Monitoring and Outcomes of Critically Ill Patients

Background

It is not clear whether performing continuous EEG (cEEG) in critically ill patients during intensive care unit (ICU) treatment affects outcomes at discharge.

Material/Methods

We prospectively matched 234 patients who received cEEG (cases) by admission diagnosis and sex to 234 patients who did not receive cEEG (controls) and followed them until discharge. Patients admitted due to seizures were excluded. The primary measures of outcome were Glasgow Coma Scale at Discharge (GCSD) and disposition at discharge, and the secondary measures of outcome were AED modifications, Glasgow Outcomes Scale, and Modified-Rankin Scale. These outcomes were compared between the cases and controls.

Results

Some differences in primary outcome measures between the groups emerged on univariate analyses, but these differences were small and not significant after controlling for covariates. Cases had longer ICU stays (p=0.002) and lower admission GCS (p=0.01) but similar GCSD (p=0.10). Of the secondary outcome measures, the mean (SD) number of AED modifications for cases was 2.2±3.1 compared to 0.4±0.8 for controls (p<0.0001); 170 (72.6%) cases had at least 1 AED modification compared to only 56 (24.1%) of the controls (p<0.0001).

Conclusions

Performing cEEG did not improve discharge outcome but it significantly influenced AED prescription patterns. Further studies assessing long-term outcomes are needed to better define the role of cEEG in this patient population.

Epileptiform abnormalities predict delayed cerebral ischemia in subarachnoid hemorrhage

OBJECTIVE

To identify whether abnormal neural activity, in the form of epileptiform discharges and rhythmic or periodic activity, which we term here ictal-interictal continuum abnormalities (IICAs), are associated with delayed cerebral ischemia (DCI).

METHODS

Retrospective analysis of continuous electroencephalography (cEEG) reports and medical records from 124 patients with moderate to severe grade subarachnoid hemorrhage (SAH). We identified daily occurrence of seizures and IICAs. Using survival analysis methods, we estimated the cumulative probability of IICA onset time for patients with and without delayed cerebral ischemia (DCI).

RESULTS

Our data suggest the presence of IICAs indeed increases the risk of developing DCI, especially when they begin several days after the onset of SAH. We found that all IICA types except generalized rhythmic delta activity occur more commonly in patients who develop DCI. In particular, IICAs that begin later in hospitalization correlate with increased risk of DCI.

CONCLUSIONS

IICAs represent a new marker for identifying early patients at increased risk for DCI. Moreover, IICAs might contribute mechanistically to DCI and therefore represent a new potential target for intervention to prevent secondary cerebral injury following SAH.

SIGNIFICANCE

These findings imply that IICAs may be a novel marker for predicting those at higher risk for DCI development.

Development and Feasibility Testing of a Critical Care EEG Monitoring Database for Standardized Clinical Reporting and Multicenter Collaborative Research

PURPOSE

The rapid expansion of the use of continuous critical care electroencephalogram (cEEG) monitoring and resulting multicenter research studies through the Critical Care EEG Monitoring Research Consortium has created the need for a collaborative data sharing mechanism and repository. The authors describe the development of a research database incorporating the American Clinical Neurophysiology Society standardized terminology for critical care EEG monitoring. The database includes flexible report generation tools that allow for daily clinical use.

METHODS

Key clinical and research variables were incorporated into a Microsoft Access database. To assess its utility for multicenter research data collection, the authors performed a 21-center feasibility study in which each center entered data from 12 consecutive intensive care unit monitoring patients. To assess its utility as a clinical report generating tool, three large volume centers used it to generate daily clinical critical care EEG reports.

RESULTS

A total of 280 subjects were enrolled in the multicenter feasibility study. The duration of recording (median, 25.5 hours) varied significantly between the centers. The incidence of seizure (17.6%), periodic/rhythmic discharges (35.7%), and interictal epileptiform discharges (11.8%) was similar to previous studies. The database was used as a clinical reporting tool by 3 centers that entered a total of 3,144 unique patients covering 6,665 recording days.

CONCLUSIONS

The Critical Care EEG Monitoring Research Consortium database has been successfully developed and implemented with a dual role as a collaborative research platform and a clinical reporting tool. It is now available for public download to be used as a clinical data repository and report generating tool.

Diagnostic accuracy between readers for identifying electrographic seizures in critically ill adults

Objective

Electrographic seizures in critically ill patients are often equivocal. In this study, we sought to determine the diagnostic accuracy of electrographic seizure annotation in adult intensive care units (ICUs) and to identify affecting factors.

Methods

To investigate diagnostic accuracy, interreader agreement (IRA) measures were derived from 5,769 unequivocal and 6,263 equivocal seizure annotations by five experienced electroencephalogram (EEG) readers after reviewing 74 days of EEGs from 50 adult ICU patients. Factors including seizure equivocality (unequivocal vs. equivocal) and laterality (generalized, partial, or bilaterally independent), cyclicity (cyclic vs. noncyclic), persistency (occurrence of status epilepticus), and patient consciousness level (coma vs. noncoma) were further investigated for their influence on IRA measures.

Results

On average, 70% of seizures marked by a reference reader overlapped, at least in part, with those marked by a test reader (any-overlap sensitivity, AO-Sn). Agreed seizure duration between reader pairs (overlap-integral sensitivity, OI-Sn) was 62%, while agreed nonseizure duration (overlap-integral specificity, OI-Sp) was 99%. A test reader would annotate one additional seizure not overlapping with a reference reader's annotation in every 11.7 h of EEG, that is, the false-positive rate (FPR) was 0.0854/h. Classifying seizure patterns into unequivocal and equivocal improved specificity and FPR (unequivocal patterns) but compromised sensitivity only for equivocal patterns. Sensitivity of all and unequivocal annotations was higher for patients with status epilepticus. Specificity was higher for partial than for bilaterally independent unequivocal seizure patterns, and lower for cyclic all seizure patterns.

Significance

Diagnosing electrographic seizures in critically ill adults is highly specific and moderately sensitive. Improved criteria for diagnosing electrographic seizures in the ICU are needed.

ACNS Critical Care EEG Terminology: Value, Limitations, and Perspectives

The interpretation of the EEG in the critically ill remains a clinical challenge. Because continuous EEG monitoring plays an increasing role in patients' care, it is important that research efforts investigate the clinical significance of periodic and rhythmic discharges and of background abnormalities. The 2012 American Clinical Neurophysiology Society critical care EEG terminology was designed to provide a comprehensive and objective vocabulary for that purpose. The interrater reliability of most of the proposed terms has been established, confirming that they represent a solid basis for research. Studies using the terminology have already started to define the clinical and prognostic values of several known or newly described EEG patterns. Yet, as the field of critical care EEG evolves, improvements will be required to further enhance the clarity of the terminology and incorporate new findings from ongoing research.

Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology

AIMS OF THE STUDY

Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports.

MATERIALS AND METHODS

First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report.

RESULTS

In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures.

CONCLUSION

The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies.

Prospective assessment and validation of rhythmic and periodic pattern detection in NeuroTrend: A new approach for screening continuous EEG in the intensive care unit

BACKGROUND

NeuroTrend is a computational method that analyzes long-term scalp EEGs in the ICU according to ACNS standardized critical care EEG terminology (CCET) including electrographic seizures. At present, it attempts to become a screening aid for continuous EEG (cEEG) recordings in the ICU to facilitate the review process and optimize resources.

METHODS

A prospective multicenter study was performed in two neurological ICUs including 68 patients who were subjected to video-cEEG. Two reviewers independently annotated the first minute of each hour in the cEEG according to CCET. These segments were also screened for faster patterns with frequencies higher than 4 Hz. The matching annotations (2911 segments) were then used as gold standard condition to test sensitivity and specificity of the rhythmic and periodic pattern detection of NeuroTrend.

RESULTS

Interrater agreement showed substantial agreement for localization (main term 1) and pattern type (main term 2) of the CCET. The overall detection sensitivity of NeuroTrend was 94% with high detection rates for periodic discharges (PD = 80%) and rhythmic delta activity (RDA = 82%). Overall specificity was moderate (67%) mainly because of false positive detections of RDA in cases of general slowing. In contrast, a detection specificity of 88% for PDs was reached. Localization revealed only a slight agreement between reviewers and NeuroTrend.

CONCLUSIONS

NeuroTrend might be a suitable screening tool for cEEG in the ICU and has the potential to raise efficiency of long-term EEG monitoring in the ICU. At this stage, pattern localization and differentiation between RDA and general slowing need improvement. This article is part of a Special Issue entitled "Status Epilepticus".

Quantification of EEG reactivity in comatose patients

OBJECTIVE

EEG reactivity is an important predictor of outcome in comatose patients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches.

METHODS

In EEG segments recorded during reactivity testing in 59 comatose patients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis.

RESULTS

The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet's AC1: 65-70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts' agreement regarding reactivity for each individual case.

CONCLUSION

Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity.

SIGNIFICANCE

Quantitative EEG may be useful for evaluating reactivity in comatose patients, offering increased objectivity.

Clinical neurophysiological assessment of sepsis-associated brain dysfunction: a systematic review

Introduction

Several studies have reported the presence of electroencephalography (EEG) abnormalities or altered evoked potentials (EPs) during sepsis. However, the role of these tests in the diagnosis and prognostic assessment of sepsis-associated encephalopathy remains unclear.

Methods

We performed a systematic search for studies evaluating EEG and/or EPs in adult (≥18 years) patients with sepsis-associated encephalopathy. The following outcomes were extracted: a) incidence of EEG/EP abnormalities; b) diagnosis of sepsis-associated delirium or encephalopathy with EEG/EP; c) outcome.

Results

Among 1976 citations, 17 articles met the inclusion criteria. The incidence of EEG abnormalities during sepsis ranged from 12% to 100% for background abnormality and 6% to 12% for presence of triphasic waves. Two studies found that epileptiform discharges and electrographic seizures were more common in critically ill patients with than without sepsis. In one study, EEG background abnormalities were related to the presence and the severity of encephalopathy. Background slowing or suppression and the presence of triphasic waves were also associated with higher mortality. A few studies demonstrated that quantitative EEG analysis and EP could show significant differences in patients with sepsis compared to controls but their association with encephalopathy and outcome was not evaluated.

Conclusions

Abnormalities in EEG and EPs are present in the majority of septic patients. There is some evidence to support EEG use in the detection and prognostication of sepsis-associated encephalopathy, but further clinical investigation is needed to confirm this suggestion.

Inter-rater agreement on identification of electrographic seizures and periodic discharges in ICU EEG recordings

OBJECTIVE

This study investigated inter-rater agreement (IRA) among EEG experts for the identification of electrographic seizures and periodic discharges (PDs) in continuous ICU EEG recordings.

METHODS

Eight board-certified EEG experts independently identified seizures and PDs in thirty 1-h EEG segments which were selected from ICU EEG recordings collected from three medical centers. IRA was compared between seizure and PD identifications, as well as among rater groups that have passed an ICU EEG Certification Test, developed by the Critical Care EEG Monitoring Research Consortium (CCEMRC).

RESULTS

Both kappa and event-based IRA statistics showed higher mean values in identification of seizures compared to PDs (k=0.58 vs. 0.38; p<0.001). The group of rater pairs who had both passed the ICU EEG Certification Test had a significantly higher mean IRA in comparison to rater pairs in which neither had passed the test.

CONCLUSIONS

IRA among experts is significantly higher for identification of electrographic seizures compared to PDs. Additional instruction, such as the training module and certification test developed by the CCEMRC, could enhance this IRA.

SIGNIFICANCE

This study demonstrates more disagreement in the labeling of PDs in comparison to seizures. This may be improved by education about standard EEG nomenclature.

Electroencephalography (EEG) for neurological prognostication after cardiac arrest and targeted temperature management; rationale and study design

BACKGROUND

Electroencephalography (EEG) is widely used to assess neurological prognosis in patients who are comatose after cardiac arrest, but its value is limited by varying definitions of pathological patterns and by inter-rater variability. The American Clinical Neurophysiology Society (ACNS) has recently proposed a standardized EEG-terminology for critical care to address these limitations.

METHODS/DESIGN

In the TTM-trial, 399 post cardiac arrest patients who remained comatose after rewarming underwent a routine EEG. The presence of clinical seizures, use of sedatives and antiepileptic drugs during the EEG-registration were prospectively documented.

DISCUSSION

A well-defined terminology for interpreting post cardiac arrest EEGs is critical for the use of EEG as a prognostic tool.

TRIAL REGISTRATION

The TTM-trial is registered at ClinicalTrials.gov (NCT01020916).

Interrater agreement for Critical Care EEG Terminology

OBJECTIVE

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.

METHODS

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).

RESULTS

IRA statistics (κ 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%).

SIGNIFICANCE

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.

EEG interpretation reliability and interpreter confidence: a large single-center study

The intrarater and interrater reliability (I&IR) of EEG interpretation has significant implications for the value of EEG as a diagnostic tool. We measured both the intrarater reliability and the interrater reliability of EEG interpretation based on the interpretation of complete EEGs into standard diagnostic categories and rater confidence in their interpretations and investigated sources of variance in EEG interpretations. During two distinct time intervals, six board-certified clinical neurophysiologists classified 300 EEGs into one or more of seven diagnostic categories and assigned a subjective confidence to their interpretations. Each EEG was read by three readers. Each reader interpreted 150 unique studies, and 50 studies were re-interpreted to generate intrarater data. A generalizability study assessed the contribution of subjects, readers, and the interaction between subjects and readers to interpretation variance. Five of the six readers had a median confidence of ≥99%, and the upper quartile of confidence values was 100% for all six readers. Intrarater Cohen's kappa (κc) ranged from 0.33 to 0.73 with an aggregated value of 0.59. Cohen's kappa ranged from 0.29 to 0.62 for the 15 reader pairs, with an aggregated Fleiss kappa of 0.44 for interrater agreement. Cohen's kappa was not significantly different across rater pairs (chi-square=17.3, df=14, p=0.24). Variance due to subjects (i.e., EEGs) was 65.3%, due to readers was 3.9%, and due to the interaction between readers and subjects was 30.8%. Experienced epileptologists have very high confidence in their EEG interpretations and low to moderate I&IR, a common paradox in clinical medicine. A necessary, but insufficient, condition to improve EEG interpretation accuracy is to increase intrarater and interrater reliability. This goal could be accomplished, for instance, with an automated online application integrated into a continuing medical education module that measures and reports EEG I&IR to individual users.

Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings

OBJECTIVE

EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection.

METHODS

Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods.

RESULTS

The line length-based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11)s, maximum IBI duration 14.02 (8.73-18.80)s and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%.

CONCLUSION

Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU.

SIGNIFICANCE

This study takes a first step towards fully automatic analysis of the preterm brain.