Interrater Agreement Between Critical Care Providers for Background Classification and Seizure Detection After Implementation of Amplitude-Integrated Electroencephalography in Neonates, Infants, and Children

Bourgoin, Pierre; Barrault, Victoire; Loron, Gauthier; Roger, Alexandre; Bataille, Emmanuelle; Leclair‐Visonneau, Laurène; Joram, Nicolas; Chenouard, Alexis

doi:10.1097/wnp.0000000000000634

Cited by 18 publications

(22 citation statements)

References 27 publications

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“…It is easy for clinicians to learn how to perform aEEGs, even if they are not EEG experts. One study found that, after a short training period, PICU personnel performed well when it came to detecting severely altered background patterns using the Hellström–Westas classification (kappa 0.71, 95% confidence interval 0.57–0.85), but moderate changes were more difficult to identify 16 . Another study explored whether medical students improved how they interpreted trends in a modified aEEG when it came to identifying wakefulness, sleep and encephalopathic patterns compared to conventional EEG.…”

Section: Resultsmentioning

confidence: 99%

“…In addition to reports on seizures, research suggests that the background pattern may have diagnostic value. [14][15][16][17] We do not know how common the use of the aEEG actually is in older infants and children. A telephone survey carried out in East England found that only one of the 17 hospital general paediatric wards used aEEG and another had access to conventional EEG oncall/during weekends.…”

Section: Use Of Aeeg In Paediatric Intensive Care Unitsmentioning

confidence: 99%

“…For that reason, researchers and clinicians often use the neonatal Hellström-Westas classification for infants, children and even adults. [9][10][11]13,16,17,19,20 This includes visually assessing the background patterns, seizures and sleep-wake cycling. It is easy for clinicians to learn how to perform aEEGs, even if they are not EEG experts.…”

Section: Interpretation Of Recordingsmentioning

confidence: 99%

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aEEG as a useful tool for neuromonitoring in critically ill children – Current evidence and knowledge gaps

2020

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Aim Amplitude‐integrated electroencephalography (aEEG) is used in children beyond neonatal age, but systematic investigations have been lacking. This mini‐review summarised aEEG studies on children aged one month to 18 years, evaluated the usefulness of aEEG and identified knowledge gaps or limitations. Methods We searched the PubMed database for articles published in English up to September 2020, and 23 papers were identified. Results aEEG was frequently used to compensate for the absence of continuous full‐channel EEG monitoring, particularly for detecting seizures. Interpreting background patterns was based on neonatal classifications, as reference values for older infants and children are lacking. It is possible that aEEG could predict outcomes after paediatric cardiac arrests and other conditions. Gaps in our knowledge exist with regard to normal values in healthy children and the effects of sedation on aEEG background patterns in children. Conclusion The main application of aEEG was detecting and treating paediatric seizures. Further research should determine reference values and investigate the potential to predict outcome after critical events or in acute neurological disease. It is likely that aEEG will play a role in paediatric critical care in the future.

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Section: Resultsmentioning

confidence: 99%

Section: Use Of Aeeg In Paediatric Intensive Care Unitsmentioning

confidence: 99%

Section: Interpretation Of Recordingsmentioning

confidence: 99%

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aEEG as a useful tool for neuromonitoring in critically ill children – Current evidence and knowledge gaps

2020

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“…Clancy et al (2011), showed a moderate, positive association between grades of EEG and aEEG background, although the distribution of grades was significantly different 24 . Inter‐rater agreement of seizure detection by experts using EEG was generally high ( κ = 0.827) but reduced when seizures were infrequent or of short duration 25,26 . There was significant differences between the visual review of aEEG and EEG for the detection of neonatal seizures; 27 differences that could be resolved using a joint interpretation 28 .…”

Section: Discussionmentioning

confidence: 99%

Reliability and accuracy of EEG interpretation for estimating age in preterm infants

Stevenson

Tataranno

Kamińska

et al. 2020

Ann Clin Transl Neurol

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Objectives To determine the accuracy of, and agreement among, EEG and aEEG readers’ estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants. Methods Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud‐based review software. The FBA was calculated using a machine learning‐based algorithm. Error analysis was used to determine the accuracy of PMA assessments and intraclass correlation (ICC) was used to assess agreement between experts. Results EEG recordings from a PMA range 25 to 38 weeks were successfully interpreted. In 179 recordings from 62 infants interpreted by all human readers, there was moderate agreement between experts (aEEG ICC = 0.724; 95%CI:0.658–0.781 and EEG ICC = 0.517; 95%CI:0.311–0.664). In 149 recordings from 61 infants interpreted by all human readers and the FBA algorithm, random and systematic errors in visual interpretation of PMA were significantly higher than the computational FBA estimate. Tracking of maturation in individual infants showed stable FBA trajectories, but the trajectories of the experts’ PMA estimate were more likely to be obscured by random errors. The accuracy of visual interpretation of PMA estimation was compromised by neurodevelopmental outcome for both aEEG and EEG review. Interpretation Visual assessment of infant maturity is possible from the EEG or aEEG, with an average of human experts providing the highest accuracy. Tracking PMA of individual infants was hampered by errors in experts’ estimates. FBA provided the most accurate maturity assessment and has potential as a biomarker of early outcome.

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“…Multiple educational interventions have shown that it is possible to enable non-experts to rapidly set up and interpret EEG at the bedside [11][12][13]. However, a comprehensive overview of these training programs, including the methods used, outcomes obtained, and potential pitfalls, remains lacking.…”

Section: Introductionmentioning

confidence: 99%

Educational initiatives and implementation of electroencephalography into the acute care environment: a protocol of a systematic review

Taran¹,

Ahmed

Bui

et al. 2020

Syst Rev

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Background: Use of electroencephalography (EEG) is currently recommended by the American Clinical Neurophysiology Society for a wide range of indications, including diagnosis of nonconvulsive status epilepticus and evaluation of unexplained disorders of consciousness. Data interpretation usually occurs by expert personnel (e.g., epileptologists, neurophysiologists), with information relayed to the primary care team. However, data cannot always be read in time-sensitive fashion, leading to potential delays in EEG interpretation and patient management. Multiple training programs have recently been described to enable non-experts to rapidly interpret EEG at the bedside. A comprehensive review of these training programs, including the tools used, outcomes obtained, and potential pitfalls, is currently lacking. Therefore, the optimum training program and implementation strategy remain unknown. Methods: We will conduct a systematic review of descriptive studies, case series, cohort studies, and randomized controlled trials assessing training programs for EEG interpretation by non-experts. Our primary objective is to comprehensively review educational programs in this domain and report their structure, patterns of implementation, limitations, and trainee feedback. Our secondary objective will be to compare the performance of non-experts for EEG interpretation with a gold standard (e.g., interpretation by a certified electroencephalographers). Studies will be limited to those performed in acute care settings in both adult and pediatric populations (intensive care unit, emergency department, or post-anesthesia care units). Comprehensive search strategies will be developed for MEDLINE, EMBASE, WoS, CINAHL, and CENTRAL to identify studies for review. The gray literature will be scanned for further eligible studies. Two reviewers will independently screen the search results to identify studies for inclusion. A standardized data extraction form will be used to collect important data from each study. If possible, we will attempt to meta-analyze the quantitative data. If heterogeneity between studies is too high, we will present meaningful quantitative comparisons of secondary outcomes as per the synthesis without meta-analysis (SWiM) reporting guidelines.

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Interrater Agreement Between Critical Care Providers for Background Classification and Seizure Detection After Implementation of Amplitude-Integrated Electroencephalography in Neonates, Infants, and Children

Cited by 18 publications

References 27 publications

aEEG as a useful tool for neuromonitoring in critically ill children – Current evidence and knowledge gaps

aEEG as a useful tool for neuromonitoring in critically ill children – Current evidence and knowledge gaps

Reliability and accuracy of EEG interpretation for estimating age in preterm infants

Educational initiatives and implementation of electroencephalography into the acute care environment: a protocol of a systematic review

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