Using ICD-10 codes to identify elective epilepsy monitoring unit admissions from administrative billing data: A validation study.

Video-electroencephalogram (EEG) monitoring in the epilepsy monitoring unit (EMU) is essential for managing epilepsy and seizure mimics. Evaluation of care in the EMU would benefit from a validated code set capable of identifying EMU admissions from administrative databases comprised of large, diverse cohorts. We assessed the ability of code-based queries to parse EMU admissions from administrative billing records in a large academic medical center over a four-year period, 2016-2019. We applied prespecified queries for admissions coded as follows: 1) elective, 2) receiving video-EEG monitoring, and 3) including diagnoses typically required by major US healthcare payers for EMU admission. Sensitivity (Sn), specificity (Sp), and predictive value positive/negative (PVP, PVN) were determined. Two approaches were highly effective. Incorporating epilepsy, seizure, or seizure mimic codes as the admitting diagnosis (assigned at admission; Sn 96.3%, Sp 100.0%, PVP 98.3%, and PVN 100.0%) or the principal diagnosis (assigned after discharge; Sn 94.9%, Sp 100.0%, PVP 98.8%, and PVN 100.0%) identified elective adult EMU admissions with comparable reliability (p = 0.096). The addition of surgical procedure codes further separated EMU admissions for intracranial EEG monitoring. When applied to larger, more comprehensive datasets, these code-based queries should enhance our understanding of EMU utilization and access to care on a scalable basis.

Yield of conventional and automated seizure detection methods in the epilepsy monitoring unit.

PURPOSE: To investigate the performance of seizure detection methods and nursing staff response in our epilepsy monitoring unit (EMU). 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. 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). CONCLUSION: Automated seizure detection software plays an important role in our EMU in seizure recognition, particularly for alerting nurses to FIAS. 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.