Abstract Details

Title Automated Detection Software Improves Seizure Recognition in an Epilepsy Monitoring Unit

Topic Practice, Policy, and Ethics

Presentation(s) P9 - Poster Session 9 (12:00 PM-1:00 PM)

Poster/Presentation
Number 7-009

Objective To investigate the performance of seizure detection methods and nursing staff response in our epilepsy monitoring unit (EMU).

Background

Video-EEG monitoring in the inpatient epilepsy monitoring unit (EMU) is vital for treating epilepsy. However, provoking and recording seizures via strategies like antiepileptic medication reduction entails inherent risk. Automated alarms can be employed in the EMU to improve seizure recognition and response times.

Design/Methods We retrospectively reviewed 38 EMU patient admissions over a 1-year period capturing 133 epileptic and non-epileptic seizures with associated video-EEG data. We recorded detailed seizure event characteristics for further analysis, including means of seizure recognition by nursing staff.

Results Rates of seizure detection, alarm usage, and time to nursing response varied by seizure type. Patients self-activated the push button (PB) alarm for 31.1% of all seizures, but only 8.9% of focal impaired awareness (FIAS) and focal to bilateral tonic-clonic seizures (FBTCS). In comparison, the Persyst automated seizure alarm reliably detected both electrographic seizures (76.2% of electrographic seizures) and FIAS/FBTCS (87.2% of FIAS/FBTCS), with a false positive alarm rate (FAR) of 0.14/hour, or every 7.3 h. 11.4% of all seizures went unrecognized by nursing staff, of which the majority (80.0%) were FIAS. The PB alarm was of higher yield for alerting nurses to focal aware seizures (FAS) and psychogenic non-epileptic seizures (PNES) versus FIAS and FBTCS (p < 0.001). In contrast, nurses relied more on the automated Persyst software alarm to detect FIAS (p < 0.001). Time to nursing response was no different following audible alarm onset for the PB compared to the Persyst alarms (p = 0.14).

Conclusions Automated seizure detection software plays an important role in our EMU in seizure recognition. More rigorous studies are needed to determine the best utilization of various monitoring techniques and to promote high quality standards and patient safety in the EMU.

Authors/Disclosures
Brad K. Kamitaki, MD (Rutgers-Robert Wood Johnson Medical School) PRESENTER	Dr. Kamitaki has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Brown and Brown Absence Services Group. The institution of Dr. Kamitaki has received research support from New Jersey Health Foundation. The institution of Dr. Kamitaki has received research support from National Institute on Aging.
Alma Yum, MD (Denver Health Medical Center)	No disclosure on file
Stephen Wong, MD (Atlantic Health System)	No disclosure on file

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Abstract Details