Journal article
Interrater variability of EEG interpretation in comatose cardiac arrest patients.
- Westhall E Department of Clinical Sciences, Division of Clinical Neurophysiology, Lund University, Lund, Sweden. Electronic address: erik.westhall@med.lu.se.
- Rosén I Department of Clinical Sciences, Division of Clinical Neurophysiology, Lund University, Lund, Sweden. Electronic address: ingmar.rosen@skane.se.
- Rossetti AO Department of Neurology, CHUV and University of Lausanne, Lausanne, Switzerland. Electronic address: andrea.rossetti@chuv.ch.
- van Rootselaar AF Department of Neurology/Clinical Neurophysiology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: a.f.vanrootselaar@amc.uva.nl.
- Wesenberg Kjaer T Department of Clinical Neurophysiology, Rigshospitalet University Hospital, Copenhagen, Denmark. Electronic address: neurology@dadlnet.dk.
- Friberg H Department of Clinical Sciences, Division of Intensive and Perioperative Care, Lund University, Lund, Sweden. Electronic address: hans.friberg@skane.se.
- Horn J Department of Intensive Care Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: j.horn@amc.uva.nl.
- Nielsen N Department of Anaesthesia and Intensive Care, Intensive Care Unit, Helsingborg Hospital, Helsingborg, Sweden. Electronic address: niklas.nielsen@med.lu.se.
- Ullén S R&D Centre Skane, Skane University Hospital, Lund, Sweden. Electronic address: susann.ullen@skane.se.
- Cronberg T Department of Clinical Sciences, Division of Neurology, Lund University, Lund, Sweden. Electronic address: tobias.cronberg@skane.se.
- 2015-05-03
Published in:
- Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. - 2015
Brain injury
Cardiac arrest
EEG
Hypothermia
Interrater variability
Prognosis
Aged
Coma
Electroencephalography
Female
Heart Arrest
Humans
Male
Middle Aged
Observer Variation
English
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.
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.
- Language
-
- English
- Open access status
- closed
- Identifiers
-
- DOI 10.1016/j.clinph.2015.03.017
- PMID 25934481
- Persistent URL
- https://folia.unifr.ch/global/documents/248464
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