Erik Westhall1, Ingmar Rosén2, Andrea O Rossetti3, Anne-Fleur van Rootselaar4, Troels Wesenberg Kjaer5, Hans Friberg6, Janneke Horn7, Niklas Nielsen8, Susann Ullén9, Tobias Cronberg10. 1. Department of Clinical Sciences, Division of Clinical Neurophysiology, Lund University, Lund, Sweden. Electronic address: erik.westhall@med.lu.se. 2. Department of Clinical Sciences, Division of Clinical Neurophysiology, Lund University, Lund, Sweden. Electronic address: ingmar.rosen@skane.se. 3. Department of Neurology, CHUV and University of Lausanne, Lausanne, Switzerland. Electronic address: andrea.rossetti@chuv.ch. 4. Department of Neurology/Clinical Neurophysiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: a.f.vanrootselaar@amc.uva.nl. 5. Department of Clinical Neurophysiology, Rigshospitalet University Hospital, Copenhagen, Denmark. Electronic address: neurology@dadlnet.dk. 6. Department of Clinical Sciences, Division of Intensive and Perioperative Care, Lund University, Lund, Sweden. Electronic address: hans.friberg@skane.se. 7. Department of Intensive Care Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: j.horn@amc.uva.nl. 8. Department of Anaesthesia and Intensive Care, Intensive Care Unit, Helsingborg Hospital, Helsingborg, Sweden. Electronic address: niklas.nielsen@med.lu.se. 9. R&D Centre Skane, Skane University Hospital, Lund, Sweden. Electronic address: susann.ullen@skane.se. 10. Department of Clinical Sciences, Division of Neurology, Lund University, Lund, Sweden. Electronic address: tobias.cronberg@skane.se.
Abstract
OBJECTIVE: EEG is widely used to predict outcome in comatose cardiac arrest patients, but its value has been limited by lack of a uniform classification. We used the EEG terminology proposed by the American Clinical Neurophysiology Society (ACNS) to assess interrater variability in a cohort of cardiac arrest patients included in the Target Temperature Management trial. The main objective was to evaluate if malignant EEG-patterns could reliably be identified. METHODS: Full-length EEGs from 103 comatose cardiac arrest patients were interpreted by four EEG-specialists with different nationalities who were blinded for patient outcome. Percent agreement and kappa (κ) for the categories in the ACNS EEG terminology and for prespecified malignant EEG-patterns were calculated. RESULTS: There was substantial interrater agreement (κ 0.71) for highly malignant patterns and moderate agreement (κ 0.42) for malignant patterns. Substantial agreement was found for malignant periodic or rhythmic patterns (κ 0.72) while agreement for identifying an unreactive EEG was fair (κ 0.26). CONCLUSIONS: The ACNS EEG terminology can be used to identify highly malignant EEG-patterns in post cardiac arrest patients in an international context with high reliability. SIGNIFICANCE: The establishment of strict criteria with high transferability between interpreters will increase the usefulness of routine EEG to assess neurological prognosis after cardiac arrest.
RCT Entities:
OBJECTIVE: EEG is widely used to predict outcome in comatose cardiac arrestpatients, but its value has been limited by lack of a uniform classification. We used the EEG terminology proposed by the American Clinical Neurophysiology Society (ACNS) to assess interrater variability in a cohort of cardiac arrestpatients included in the Target Temperature Management trial. The main objective was to evaluate if malignant EEG-patterns could reliably be identified. METHODS: Full-length EEGs from 103 comatose cardiac arrestpatients were interpreted by four EEG-specialists with different nationalities who were blinded for patient outcome. Percent agreement and kappa (κ) for the categories in the ACNS EEG terminology and for prespecified malignant EEG-patterns were calculated. RESULTS: There was substantial interrater agreement (κ 0.71) for highly malignant patterns and moderate agreement (κ 0.42) for malignant patterns. Substantial agreement was found for malignant periodic or rhythmic patterns (κ 0.72) while agreement for identifying an unreactive EEG was fair (κ 0.26). CONCLUSIONS: The ACNS EEG terminology can be used to identify highly malignant EEG-patterns in post cardiac arrestpatients in an international context with high reliability. SIGNIFICANCE: The establishment of strict criteria with high transferability between interpreters will increase the usefulness of routine EEG to assess neurological prognosis after cardiac arrest.
Authors: Jerry P Nolan; Robert A Berg; Stephen Bernard; Bentley J Bobrow; Clifton W Callaway; Tobias Cronberg; Rudolph W Koster; Peter J Kudenchuk; Graham Nichol; Gavin D Perkins; Tom D Rea; Claudio Sandroni; Jasmeet Soar; Kjetil Sunde; Alain Cariou Journal: Intensive Care Med Date: 2017-03-11 Impact factor: 17.440
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Authors: Claudio Sandroni; Sonia D'Arrigo; Sofia Cacciola; Cornelia W E Hoedemaekers; Marlijn J A Kamps; Mauro Oddo; Fabio S Taccone; Arianna Di Rocco; Frederick J A Meijer; Erik Westhall; Massimo Antonelli; Jasmeet Soar; Jerry P Nolan; Tobias Cronberg Journal: Intensive Care Med Date: 2020-09-11 Impact factor: 17.440
Authors: Nicholas S Abend; Shavonne L Massey; Mark Fitzgerald; France Fung; Natalie J Atkin; Rui Xiao; Alexis A Topjian Journal: J Clin Neurophysiol Date: 2017-11 Impact factor: 2.177