Literature DB >> 29448148

Large inter-rater variability on EEG-reactivity is improved by a novel quantitative method.

Christophe Henri Valdemar Duez1, Mads Qvist Ebbesen2, Krisztina Benedek3, Martin Fabricius4, Mary Doreen Atkins5, Sandor Beniczky6, Troels W Kjaer7, Hans Kirkegaard8, Birger Johnsen9.   

Abstract

OBJECTIVE: To assess inter-rater agreement on EEG-reactivity (EEG-R) in comatose patients and compare it with a quantitative method (QEEG-R).
METHODS: Six 30-s stimulation epochs (noxious, visual and auditory) were performed during EEG on 19 neurosurgical and 11 cardiac arrest patients. Six experts analysed EEGs for reactivity using their habitual methods. QEEG-R was defined as present if ≥2/6 epochs were reactive (stimulation/rest power ratio exceeding noise level). Three-months patient outcome was assessed by the Cerebral Performance Category Score (CPC) dichotomized in good (1-2) or poor (3-5).
RESULTS: Agreement among experts on overall EEG-R varied from 53% to 83% (κ: 0.05-0.64) and reached 100% (κ: 1) between two QEEG-R calculators. For the experts, absence of EEG-R yielded sensitivities for poor outcome between 40-85% and specificities between 20-90%, for QEEG-R sensitivity was 40% (CI: 23-68%) and specificity 100% (CI: 69-100%).
CONCLUSIONS: There is a large inter-rater variation among experts on EEG-R assessment in comatose patients. QEEG-R is a promising objective prognostic parameter with low inter-rater variation and a high specificity for prediction of poor outcome. SIGNIFICANCE: Clinicians should be cautious when using the traditional, qualitative method, in particular in end-of-life decisions. Implementation of the quantitative method in clinical practice may improve reliability of reactivity assessments.
Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Coma prognosis; EEG reactivity; Inter-rater variation; Outcome prediction; Quantitative EEG

Mesh:

Year:  2018        PMID: 29448148     DOI: 10.1016/j.clinph.2018.01.054

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  8 in total

1.  Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

Authors:  Edilberto Amorim; Michelle van der Stoel; Sunil B Nagaraj; Mohammad M Ghassemi; Jin Jing; Una-May O'Reilly; Benjamin M Scirica; Jong Woo Lee; Sydney S Cash; M Brandon Westover
Journal:  Clin Neurophysiol       Date:  2019-07-25       Impact factor: 3.708

Review 2.  Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review.

Authors:  Sung-Min Cho; Eva K Ritzl; Jaeho Hwang
Journal:  J Neurol       Date:  2022-08-19       Impact factor: 6.682

3.  Teaching Important Basic EEG Patterns of Bedside Electroencephalography to Critical Care Staffs: A Prospective Multicenter Study.

Authors:  Stephane Legriel; Gwenaëlle Jacq; Amandine Lalloz; Guillaume Geri; Pedro Mahaux; Cedric Bruel; Sandie Brochon; Benjamin Zuber; Cécile André; Krystel Dervin; Mathilde Holleville; Alain Cariou
Journal:  Neurocrit Care       Date:  2021-02       Impact factor: 3.210

4.  Electroencephalographic reactivity as predictor of neurological outcome in postanoxic coma: A multicenter prospective cohort study.

Authors:  Marjolein M Admiraal; Anne-Fleur van Rootselaar; Jeannette Hofmeijer; Cornelia W E Hoedemaekers; Christiaan R van Kaam; Hanneke M Keijzer; Michel J A M van Putten; Marcus J Schultz; Janneke Horn
Journal:  Ann Neurol       Date:  2019-06-08       Impact factor: 10.422

5.  Preserved Electroencephalogram Power and Global Synchronization Predict Better Neurological Outcome in Sudden Cardiac Arrest Survivors.

Authors:  Li-Ting Ho; Bess Ma Fabinal Serafico; Ching-En Hsu; Zhao-Wei Chen; Tse-Yu Lin; Chen Lin; Lian-Yu Lin; Men-Tzung Lo; Kuo-Liong Chien
Journal:  Front Physiol       Date:  2022-04-20       Impact factor: 4.566

6.  Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method.

Authors:  Jia Tian; Yi Zhou; Hu Liu; Zhenzhen Qu; Limiao Zhang; Lidou Liu
Journal:  Front Neurol       Date:  2022-07-28       Impact factor: 4.086

7.  Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review.

Authors:  Eric Azabou; Vincent Navarro; Nathalie Kubis; Martine Gavaret; Nicholas Heming; Alain Cariou; Djillali Annane; Fréderic Lofaso; Lionel Naccache; Tarek Sharshar
Journal:  Crit Care       Date:  2018-08-02       Impact factor: 9.097

Review 8.  Critical care EEG standardized nomenclature in clinical practice: Strengths, limitations, and outlook on the example of prognostication after cardiac arrest.

Authors:  Pia De Stefano; Margitta Seeck; Andrea O Rossetti
Journal:  Clin Neurophysiol Pract       Date:  2021-04-25
  8 in total

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