Abstract Details

Title Neurocritical Care EEG Rounds: A Model to Improve Neurology Resident Electroencephalogram (EEG) Interpretation

Topic ��ɫ��, Research, and Methodology

Presentation(s) S28 - Novel Approaches to Teaching, Assessment, and Beyond in Neurology ��ɫ�� (4:54 PM-5:06 PM)

Poster/Presentation
Number 008

Background EEG has emerged as an important monitoring tool in the critical care setting. Quick interpretation of abnormal EEG patterns may decrease the time to intervention. The classic EEG curriculum for neurology residents mainly consists of didactic lectures, which is often insufficient to develop proficiency and have limited exposure to critical care EEG concepts.

Objective To create a novel educational model to improve critical care electroencephalogram (EEG) interpretation for neurology residents.

Design/Methods To improve neurology resident critical care EEG education at our institution, we organized “ICU EEG rounds”, a daily 30-minute virtual conference involving both the neurocritical care team and EEG readers to review the ongoing EEG studies. We assessed residents’ knowledge and comfort level towards EEG interpretation before and after their 4-week neurocritical care rotation using a self-designed questionnaire covering common abnormal EEG patterns (e.g. background slowing, focal slowing, epileptic features and seizures).

Results Pilot data from July to September 2024 included 10 adult neurology residents. The pretest revealed the toughest concepts were focal slowing, lateralized periodic discharges (LPDs), and mild encephalopathy. On the posttest, 9 residents (90%) indicated a subjective increase in comfort level with EEG interpretation and 5 residents (50%) showed an overall objective improvement in recognizing EEG patterns. The most significant improvement was seen in the weak concepts identified with the pretest (focal slowing, mild encephalopathy, and LPDs). The least improvement was seen in questions testing myoclonic status epilepticus and breach artifact.

Conclusions Initial pilot data showed an increase in EEG comfort level in 90% and EEG pattern recognition in 50% of the residents after implementation of our daily ICU EEG rounds. Our model integrates case-based EEG education into residents’ daily clinical care, and can be an efficient way to improve their knowledge about critical care EEGs.

Authors/Disclosures
Ellen Sanchez Mas, MD (Monarch Medical Center Apartments) PRESENTER	Dr. Sanchez Mas has nothing to disclose.
Mitchell Powell, MD (Baylor College of Medicine)	Dr. Powell has nothing to disclose.
Ali Ahmad, MD	Dr. Ahmad has nothing to disclose.
Corey Goldsmith, MD, FAAN (Baylor College of Medicine - Department of Neurology)	Dr. Goldsmith has received publishing royalties from a publication relating to health care.
Rahul Damani	Rahul Damani has nothing to disclose.
Lu Lin, MD, PhD (Baylor College of Medicine)	Dr. Lin has nothing to disclose.

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