Ferastraoaru Victor, Goldenholz Daniel M, Chiang Sharon, Moss Robert, Theodore William H, Haut Sheryl R
Department of Neurology Albert Einstein College of Medicine and Montefiore Medical Center Bronx New York U.S.A.
Division of Epilepsy Beth Israel Deaconess Medical Center Boston Massachusetts U.S.A.
Epilepsia Open. 2018 Jul 4;3(3):364-373. doi: 10.1002/epi4.12237. eCollection 2018 Sep.
To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.
Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed-effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero-inflated negative binomial and logistic mixed-effects models, respectively.
A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: "Overtired or irregular sleep," "Bright or flashing lights," and "Emotional stress" (p < 0.004).
This study explored a large cohort of patients with self-reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient-recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.
分析来自大型电子癫痫发作日记Seizure Tracker的数据，包括不同病因之间癫痫发作特征的比较、癫痫发作波动的时间模式以及特定触发因素。
采用零膨胀负二项混合效应模型评估癫痫发作事件的时间模式（白天或一周内），以及儿童与成人之间、不同病因之间每月癫痫发作频率的组间差异。使用以个体为随机效应的混合效应逻辑模型评估长时间癫痫发作与发作触发因素之间的关联。分别报告涉及零膨胀负二项和逻辑混合效应模型分析的发病率比（IRR）和优势比。
2007年12月至2016年1月，10186名受试者（56.7%为儿童）记录了共计1037909次癫痫发作。儿童的癫痫发作比成人更频繁（每月癫痫发作频率中位数为3.5次对2.7次，IRR为1.26；p<0.001）。癫痫发作呈现昼夜节律模式（上午7:00至10:00之间频率较高，夜间较低），且一周内不同日期报告的癫痫发作情况有所不同（周一至周五的发作率高于周六或周日）。与较短时间的癫痫发作相比，较长时间的癫痫发作（>5分钟或>30分钟）有更高比例的以下触发因素：“过度疲劳或睡眠不规律”、“明亮或闪烁的灯光”以及“情绪压力”（p<0.004）。
本研究探索了一大群自我报告癫痫发作的患者；讨论了大型癫痫发作日记数据库的优势和局限性。本研究的结果与先前在较小的验证队列中的研究结果一致，表明患者记录的数据库是癫痫研究的宝贵资源，能够复制结果并产生新的假设。
