Improving the ability of ED physicians to identify subclinical/electrographic seizures on EEG after a brief training module View Full Text


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Article Info

DATE

2019-12

AUTHORS

Geetha Chari, Kabir Yadav, Daniel Nishijima, Ahmet Omurtag, Shahriar Zehtabchi

ABSTRACT

Approximately 5% of emergency department (ED) patients with altered mental status (AMS) have non-convulsive seizures (NCS). Patients with NCS should be diagnosed with EEG as soon as possible to initiate antiepileptic treatment. Since ED physicians encounter such patients first in the ED, they should be familiar with general EEG principles as well as the EEG patterns of NCS/NCSE. We evaluated the utility of a brief training module in enhancing the ED physicians’ ability to identify seizures on EEG. This was a randomized controlled trial conducted in three academic institutions. A slide presentation was developed describing the basic principles of EEG including EEG recording techniques, followed by characteristics of normal and abnormal patterns, the goal of which was to familiarize the participants with EEG seizure patterns. We enrolled board-certified emergency medicine physicians into the trial. Subjects were randomized to control or intervention groups. Participants allocated to the intervention group received a self-learning training module and were asked to take a quiz of EEG snapshots after reviewing the presentation, while the control group took the quiz without the training. A total of 30 emergency physicians were enrolled (10 per site, with 15 controls and 15 interventions). Participants were 52% male with median years of practice of 9.5 years (3, 14). The percentage of correct answers in the intervention group (65%, 63% and 75%) was significantly different (p = 0.002) from that of control group (50%, 45% and 60%). A brief self-learning training module improved the ability of emergency physicians in identifying EEG seizure patterns. More... »

PAGES

11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12245-019-0228-9

DOI

http://dx.doi.org/10.1186/s12245-019-0228-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113047117


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