Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Department of Neuroscience, Monash University, Melbourne, VIC, Australia.
Epilepsy Res. 2024 Jul;203:107379. doi: 10.1016/j.eplepsyres.2024.107379. Epub 2024 May 8.
To characterize seizure tracking patterns of people with focal epilepsy using electronic seizure diary entries, and to assess for risk factors associated with poor tracking.
We analyzed electronic seizure diary data from 410 participants with newly diagnosed focal epilepsy in the Human Epilepsy Project 1 (HEP1). Each participant was expected to record data each day during the study, regardless of seizure occurrence. The primary outcome of this post-hoc analysis was whether each participant properly tracked a seizure diary entry each day during their study participation. Using finite mixture modeling, we grouped patient tracking trajectories into data-driven clusters. Once defined, we used multinomial modeling to test for independent risk factors of tracking group membership.
Using over up to three years of daily seizure diary data per subject, we found four distinct seizure tracking groups: consistent, frequent at study onset, occasional, and rare. Participants in the consistent tracking group tracked a median of 92% (interquartile range, IQR: 82%, 99%) of expected days, compared to 47% (IQR:34%, 60%) in the frequent at study onset group, 37% (IQR: 26%, 49%) in the occasional group, and 9% (IQR: 3%, 15%) in the rare group. In multivariable analysis, consistent trackers had lower rates of seizure days per tracked year during their study participation, compared to other groups.
Future efforts need to focus on improving seizure diary tracking adherence to improve quality of outcome data, particularly in those with higher seizure burden. In addition, accounting for missing data when using seizure diary data as a primary outcome is important in research trials. If not properly accounted for, total seizure burden may be underestimated and biased, skewing results of clinical trials.
使用电子癫痫日记条目描述局灶性癫痫患者的癫痫发作跟踪模式，并评估与跟踪不良相关的危险因素。
我们分析了人类癫痫计划 1（HEP1）中 410 名新诊断为局灶性癫痫患者的电子癫痫日记数据。每位参与者预计在研究期间每天记录数据，无论是否发生癫痫。本事后分析的主要结果是每位参与者在研究参与期间是否每天正确记录癫痫日记条目。使用有限混合模型，我们将患者跟踪轨迹分为数据驱动的聚类。一旦定义，我们使用多项建模来测试跟踪组隶属关系的独立危险因素。
使用每位受试者长达三年的每日癫痫日记数据，我们发现了四个不同的癫痫跟踪组：一致、研究开始时频繁、偶尔和罕见。在一致跟踪组中，参与者跟踪了中位数为 92%（四分位距，IQR：82%，99%）的预期天数，而在研究开始时频繁的组中为 47%（IQR：34%，60%），在偶尔组中为 37%（IQR：26%，49%），在罕见组中为 9%（IQR：3%，15%）。在多变量分析中，与其他组相比，一致跟踪者在研究期间的跟踪年中癫痫发作天数的比率较低。
未来的努力需要集中精力提高癫痫日记跟踪的依从性，以改善结局数据的质量，特别是在癫痫发作负担较高的患者中。此外，在研究试验中，当使用癫痫日记数据作为主要结局时，正确考虑缺失数据非常重要。如果没有正确考虑，总癫痫发作负担可能会被低估和产生偏差，从而影响临床试验的结果。
