Abstracts

Diagnostic yield of utilizing 24-72 hours Video EEG monitoring in resource-limited settings.

Abstract number : 2.39
Submission category : 3. Neurophysiology / 3A. Video EEG Epilepsy-Monitoring
Year : 2021
Submission ID : 1886511
Source : www.aesnet.org
Presentation date : 12/9/2021 12:00:00 PM
Published date : Nov 22, 2021, 06:56 AM

Authors :
Adaeze Avah, MBBS - Regions Stroke and Neuroscience Hospital; Chinekwu Anyanwu, MD – Epileptologist, Epilepsy, Regions Stroke and Neuroscience Hospital; Jemimah Brownson, MD – Physcian, Neurology, Regions Stroke and Neuroscience Hospital; Chidimma Njoku, MD – Physcian, Neurology, Regions Stroke and Neuroscience Hospital; Chukwuma Nwaze, MD – Physcian, Neurology, Regions Stroke and Neuroscience Hospital; Ahunna Nwogu, MD – Physcian, Neurology, Regions Stroke and Neuroscience Hospital; Chukwudi Nwogu, MD – Physcian, Neurology, Regions Stroke and Neuroscience Hospital; Confidence Olaiya, Msc – Neurophysiologist, Neurophysiology, Regions Stroke and Neuroscience Hospital; Emmanuel Orji, Bsc – Neurophysiologist, Neurophysiology, Regions Stroke and Neuroscience Hospital

Rationale: Long-term video electroencephalogram monitoring has been widely utilized in modern clinical practice for the diagnosis and management of epileptic and non-epileptic conditions, the typical duration of monitoring is 5 to 7 days. However, it has been postulated that similar results can be obtained within a shorter duration, especially in resource-limited settings. Hence, we aim to determine the clinical yield of video electroencephalogram (vEEG) monitoring (VEM) done within 24 to 72 hours.

Methods: We reviewed electronic medical records of patients of all age groups who were admitted from September 2018 to September 2021 for 24-72 hours VEM. Demographics and clinical history including frequency of seizures were obtained. The frequency of seizures was classified as ‘daily’ in patients with one or more seizures per day, ‘persistent’ in patients with less than one seizure per day but at least once in six months, and ‘rare’ in patients with less than one seizure in six months. Patients with unclear duration due to recent onset were classified as ‘undefined’. (1)

Results: A total of seventy consecutive patients (34 males and 36 females) were captured in our study. The mean age was 22.86 ± 18.22 years (range 5 months-80 years) and the average duration of monitoring was 44.23 ± 16.16 hours. Fifty-seven patients (81.4%) were confirmed to have seizures or potential for seizures based on clinical semiology and vEEG findings. No seizures were recorded in two patients (2.9%) and 11 patients (15.7%) had non-epileptic events. Based on admission history, of the 57 patients who were confirmed to have seizures or seizure-like events, 19 patients (33.3%) had daily events, 33 patients (57.9%) had persistent events, 3 patients (5.3%) had rare events and 2 patients (3.5%) had their events undefined. All electrographic evidence of seizures or potential for seizures and non-epileptic events were captured within 48 hours of VEM. 56 of the 57 patients with epileptic events (98.2%) were captured within 24 hours. This included 100% of daily seizures, 100% of persistent seizures, and 100% of undefined events. 1 "rare" seizure was captured within 48 hours (Table 1). On further analyses, 47.4% of patients had first inter-ictal abnormality within 16 hours and 19.3% had first inter-ictal abnormality within 8 hours. This was statistically significant (p = 0.07).

Conclusions: Our study revealed that for diagnostic event recording in Epilepsy and non-epileptic spells, a minimum duration of 24 hours and maximum of 72 hours is sufficient for long term video EEG monitoring especially if event frequency is at least once in six months, as a significant percentage of inter-ictal abnormalities and non-epileptic spells are captured within 48 hours of VEM, and this time frame can be useful in resource-limited settings.

References
1. Loddenkemper T, Kellnghaus C, Wyllie E, Najm IM, Gupta A, Rosenow F, et al. A proposal for a five-dimensional patient-oriented epilepsy classification. Epileptic Disord 2005;7(4):308–16.

Funding: Please list any funding that was received in support of this abstract.: Dr Chinekwu Anyanwu.

Neurophysiology