Abstract Details

Title Implementation of a Quantitative Electroencephalography Curriculum for Seizure Detection in the Pediatric Intensive Care Unit - A Quality Improvement Initiative

Topic Neuro Trauma, Critical Care, and Sports Neurology

Presentation(s) P14 - Poster Session 14 (8:00 AM-9:00 AM)

Poster/Presentation
Number 13-003

Objective To evaluate the effectiveness of a standardized curriculum on the accurate identification of electrographic seizures (ES) amongst child neurology residents and EEG technologists using quantitative EEG (qEEG) in the pediatric intensive care unit (PICU).

Background Early treatment of ES has been associated with lower seizure burden. Despite this, it is impractical for cEEG to be monitored minute to minute leading to a delay in ES identification and treatment. qEEG is a sensitive tool for seizure identification and may improve seizure identification and time to treatment. This quality improvement project focuses on teaching residents and technologists to effectively identify ES using qEEG.

Design/Methods Child neurology residents and EEG technologists of varying levels of training completed a pre-training survey that included examples of common background patterns, artifacts and seizure patterns using proprietary qEEG software (Persyst). After completion of the pre-training survey, technologists had small group training sessions with an epileptologist, and residents viewed a 25 minute on-line education module focused on principles of qEEG and pattern recognition. Residents who completed the video module received a printed reference card which includes common images and descriptions of EEG trends. A post-training survey was then completed to assess accuracy with trend identification. Utilization of EEG trend software was assessed using written audits.

Results Eighty-one percent (17/21) of EEG technologists and 69% (9/13) of child neurology residents completed the pre-training survey with (19/21) and (6/13) completing the post-training survey. The average survey scores improved by 41% for EEG technologists and 20% for residents. Audit data showed an increased frequency of EEG trend software use by EEG technologists.

Conclusions qEEG has the potential to improve seizure identification and subsequent time to treatment. Standardized instructional modules are effective in teaching qEEG patterns.

Authors/Disclosures
Daniel Davila-Williams, MD (Texas Children's Hospital) PRESENTER	Dr. Davila-Williams has nothing to disclose.
Agnieszka Kielian, MD	Dr. Kielian has nothing to disclose.
	No disclosure on file
	No disclosure on file
Arnold J. Sansevere, MD (Children's National Hospital)	Dr. Sansevere has nothing to disclose.

�鶹��ýӳ��

�鶹��ýӳ��

Abstract Details