Epilepsy Center, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Charleston Area Medical Center, Charleston, WV, USA.
Epileptic Disord. 2020 Dec 1;22(6):752-758. doi: 10.1684/epd.2020.1220.
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.
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.
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.
Our study shows that the use of ILAE 2017 symptom categories may help guide ambulatory video-EEG studies.
为了对疑似癫痫发作的症状进行非手术诊断评估,门诊视频脑电图监测已被用作住院视频脑电图监测的一种具有成本效益的替代方法。我们旨在根据临床评估期间获得的癫痫发作症状病史,评估门诊视频脑电图记录中的癫痫样放电发生率。
这是一项回顾性队列研究。我们在一个日历年内对来自 35 个州的 9221 例连续门诊视频脑电图研究中的癫痫发作症状进行了查询。我们评估了每种症状的癫痫样放电发生率,包括符合类别标题的症状,即使这些症状未包含在 ILAE 2017 症状列表中。我们使用 Fisher 精确检验和单变量逻辑回归报告发病率、优势比和相应的 p 值。我们应用多变量逻辑回归生成六个症状类别(控制其他症状存在的情况下)的优势比。
包含运动症状(OR=1.53)或自动症(OR=1.42)的病史与癫痫样放电发生率增加相关,而感觉症状(OR=0.76)的病史预测缺乏癫痫样放电。与癫痫样放电发生率增加相关的患者报告症状包括咂嘴、呻吟、言语自动症、攻击行为、眨眼、似曾相识、肌肉疼痛、尿失禁、窒息和抽搐。另一方面,听觉幻觉、记忆障碍、头晕、晕厥、头晕目眩、纤维肌痛和慢性疼痛预测不存在癫痫样放电。大多数癫痫样放电仅由发作间期棘波或尖波组成。
我们的研究表明,使用 ILAE 2017 症状类别可以帮助指导门诊视频脑电图研究。