John Libbey Eurotext

Seizure symptoms and ambulatory EEG findings: incidence of epileptiform discharges Volume 22, numéro 6, December 2020

Tableaux

Auteurs
1 Epilepsy Center, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
2 Charleston Area Medical Center, Charleston, WV, USA
3 University of Texas, McGovern Medical School, Department of Neurology, Houston, TX, USA
4 Chief Medical Officer, The Alliance Family of Companies, Irving, TX, USA
5 Alfaisal University, College of Medicine, Riyadh, KSA, USA
6 Medstar Baltimore, Baltimore, MD, USA
7 Ross University School of Medicine, Portsmouth, Dominica
8 Midwestern University, Chicago, IL, USA
9 Case Western Reserve University, Cleveland, OH, USA
10 Cleveland State University, Cleveland, OH, USA
11 Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
12 Talha Iqbal, Ziauddin Medical College, Ziauddin University, Karachi, Pakistan
13 Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
* Correspondence: Hai Chen George Washington University, School of Medicine and Health Sciences, 2150 Pennsylvania Ave, NW Washington, DC, 20037, USA

Aims. Ambulatory video-EEG monitoring has been utilized as a cost-effective alternative to inpatient video-EEG monitoring for non-surgical diagnostic evaluation of symptoms suggestive of epileptic seizures. We aimed to assess incidence of epileptiform discharges in ambulatory video-EEG recordings according to seizure symptom history obtained during clinical evaluation.

Methods. This was a retrospective cohort study. We queried seizure symptoms from 9,221 consecutive ambulatory video-EEG studies in 35 states over one calendar year. We assessed incidence of epileptiform discharges for each symptom, including symptoms that conformed to a category heading, even if not included in the ILAE 2017 symptom list. We report incidences, odds ratios, and corresponding p values using Fisher's exact test and univariate logistic regression. We applied multivariable logistic regression to generate odds ratios for the six symptom categories that are controlled for the presence of other symptoms.

Results. History that included motor symptoms (OR=1.53) or automatisms (OR=1.42) was associated with increased occurrence of epileptiform discharges, whereas history of sensory symptoms (OR=0.76) predicted lack of epileptiform discharges. Patient-reported symptoms that were associated with increased occurrence of epileptiform discharges included lip-smacking, moaning, verbal automatism, aggression, eye-blinking, déjà vu, muscle pain, urinary incontinence, choking and jerking. On the other hand, auditory hallucination memory deficits, lightheadedness, syncope, giddiness, fibromyalgia and chronic pain predicted absence of epileptiform discharges. The majority of epileptiform discharges consisted only of interictal sharp waves or spikes.

Conclusions. Our study shows that the use of ILAE 2017 symptom categories may help guide ambulatory video-EEG studies.