Abstract
BackgroundElectroencephalography (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.In the Target Temperature Management (TTM) trial, a large international trial on temperature management after cardiac arrest, EEG-examinations were part of the prospective study design. The main objective of this study is to evaluate EEG-data from the TTM-trial and to identify malignant EEG-patterns reliably predicting a poor neurological outcome.Methods/DesignIn 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.After the end of the trial, the EEGs were retrieved to form a central EEG-database.The EEG-data will be analysed using the ACNS EEG terminology. We designed an electronic case record form (eCRF). Four EEG-specialists from different countries, blinded to patient outcome, will independently classify the EEGs and report through the eCRF. We will describe the prognostic values of pre-specified EEG patterns to predict poor as well as good outcome. We hypothesise three patterns to always be associated with a poor outcome (suppressed background without discharges, suppressed background with continuous periodic discharges and burst-suppression). Inter- and intra-rater variability and whether sedation or level of temperature affects the prognostic values will also be analyzed.DiscussionA well-defined terminology for interpreting post cardiac arrest EEGs is critical for the use of EEG as a prognostic tool.The results of this study may help to validate the ACNS terminology for assessing post cardiac arrest EEGs and identify patterns that could reliably predict outcome.Trial registrationThe TTM-trial is registered at ClinicalTrials.gov (NCT01020916).
Highlights
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
A well-defined terminology for interpreting post cardiac arrest EEGs is critical for the use of EEG as a prognostic tool
The results of this study may help to validate the American Clinical Neurophysiology Society (ACNS) terminology for assessing post cardiac arrest EEGs and identify patterns that could reliably predict outcome
Summary
The recently revised standardized critical care EEG terminology by ACNS [29] includes characterization of the background activity, which was lacking in the earlier version [57]. Our main objective is to identify highly malignant patterns with an acceptable level of inter-rater variability that can reliably be used to aid in prognostication and in decisions on whether or not to withdraw life sustaining treatment or continue active care. In order to increase the sensitivity of EEG to detect poor outcome, we will test other features of the ACNS terminology that we consider potentially malignant (an expected FPR
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