Background Prognosticating outcome following cardiac arrest is challenging and requires a
multimodal approach. We tested the hypothesis that the combination of initial neurologic …

Introduction Prognosticating outcome after cardiac arrest (CA) requires a multimodal
approach. However, evidence regarding combinations of methods is limited. We evaluated …

Background Prognosticating outcome following cardiac arrest requires a multimodal
approach. We tested whether the combination of initial neurologic examination combined …

Aims To investigate the ability of 30-min electroencephalogram (EEG), short-latency
somatosensory evoked potentials (SEPs) and brain computed tomography (CT) to predict …

Aims To assess the accuracy of electroencephalogram (EEG) and somatosensory evoked
potentials (SEPs) recorded at 12 and 72 h from resuscitation for predicting six-months …

Aim Electroencephalography (EEG) is a key modality for assessment of prognosis following
cardiac arrest (CA); however, whether continuous EEG (cEEG) is superior to routine …

Objective To determine the ability of quantitative electroencephalography (QEEG) to
improve the accuracy of predicting recovery of consciousness by post-cardiac arrest day 10 …

Objective Outcome prediction in patients after cardiac arrest (CA) is challenging.
Electroencephalographic reactivity (EEG‐R) might be a reliable predictor. We aimed to …

Objective To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine
learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG …

Purpose To investigate current practices and timing of neurological prognostication in
comatose cardiac arrest patients. Methods An anonymous questionnaire was distributed to …