Diagnostic Yield of Ambulatory versus Inpatient Video EEG in Spell Classification in a Public Safety-net Hospital in the Central Valley of CA
Abstract number :
2.142
Submission category :
3. Neurophysiology / 3C. Other Clinical EEG
Year :
2024
Submission ID :
463
Source :
www.aesnet.org
Presentation date :
12/8/2024 12:00:00 AM
Published date :
Authors :
Presenting Author: Wefaq Alshami, BS – Kern Medical Center
Adora Calistro, BS, R.EEG T, NA-CLTM – Kern Medical
Zara'a Alshami, BS – Kern Medical
Clins Chacko, BS – Kern Medical
Neela Zalmay, M.Sc – Kern Medical Center
Shaan Braich, BS – Kern Medical
Annika Daug, BS – Kern Medical
Christel Benny, BS – United Neuroscience Institute
Charles Liu, MD, PhD – Keck School of Medicine, University of Southern California
Hari Prasad Veedu, MD, FACNS – Kern Medical
Rationale: The Kern Medical Epilepsy Center is the only adult NAEC center in the Central Valley of CA and member of the USC Epilepsy Care Consortium. It has 2-EMU beds prioritized for pre-surgical evaluation, but it has generally elevated all EEG diagnostics in the region. Inpatient video EEG (iVEEG) in an EMU is the gold standard for comprehensive spell evaluation, but we often use ambulatory video EEG (aVEEG) as an alternative in other patients. We compare the diagnostic yield of aVEEG vs iVEEG in our center.
Methods: We retrospectively reviewed a prospectively maintained database of patients undergoing iVEEG (Nihon Kohden EEG 1200) and aVEEG (Cadwell Arc Apollo EEG and Q-Video Mobile 3) between 1/1/20-1/1/23. We collected data on sex, age, monitoring duration, and semiological diagnosis to compare efficacy in capturing and classifying epileptic seizures and non-epileptic events such as psychogenic non-epileptic seizures (PNES) and other paroxysmal events (OPE). We excluded patients admitted for pre-surgical workup or who had undergone surgical intervention/modulation.
For iVEEG, we adjusted medications and/or performed photic stimulation (PS) and hyperventilation (HV) daily with occasional sleep deprivation until we captured one or more events. For aVEEG, we made no medication changes, and performed PS and HV only on day 1. We planned for 72-hour aVEEG, with daily returns to the EEG lab for quality recordings. The iVEEG duration varied; for consistency, we analyzed the data from the first 72-hours and 73 to 120 hours separately to examine the yield of extended monitoring.
We made a post-test diagnosis of epilepsy, PNES, or OPE depending on the video EEG recordings of interictal epileptiform discharges, electro-clinical seizures, or EEG seizures.
Results: In 120 aVEEG patients, 104 (87.4%) initially had a pre-test diagnosis of epilepsy, 11 with PNES (9.24%), and 1 with epilepsy and PNES. However, the post-evaluation confirmed epilepsy diagnosis in only 23 patients (18.3%), PNES (4.1%) in 5 patients, and no patients with both epilepsy and PNES. The diagnosis of epilepsy was based on electroclinical seizures in 5 patients (4.1%) and interictal discharges in 22 patients (18.3%), with no diagnostic yield in 92 patients (76.7%).
In 94 iVEEG patients, 75 (79.7%) had a pre-test diagnosis of epilepsy, 28 with PNES (29.7%) and 12 with epilepsy and PNES (12.7%). However, post-evaluation in the EMU for 72-hrs confirmed the diagnosis of epilepsy in 24 patients (25.5%), 30 with PNES (31.91%), and 2 had both (2.1%). We diagnosed epilepsy by detecting electroclinical seizures in 15 patients (16%), electrographic seizures in 2 (2.1%) and by interictal discharges in 31 (33%). We obtained no diagnostic yield in 37 patients (39.4%). The extension of EMU evaluation beyond 72hrs yielded only 1 additional epilepsy diagnosis.
Conclusions: In our Central Valley public safety-net center, iVEEG in the EMU has superior yield to aVEEG for classifying spells, and it is the option of choice when resources are sufficient.
Funding: None
Neurophysiology