预测局灶性癫痫成人的癫痫发作风险：一项开发和验证研究。
Forecasting seizure risk in adults with focal epilepsy: a development and validation study.
机构信息
Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Department of Neuroscience, Carney Institute for Brain Science, Brown University, Providence, RI, USA.
Department of Neuroscience, Carney Institute for Brain Science, Brown University, Providence, RI, USA.
出版信息
Lancet Neurol. 2021 Feb;20(2):127-135. doi: 10.1016/S1474-4422(20)30396-3. Epub 2020 Dec 17.
BACKGROUND
People with epilepsy are burdened with the apparent unpredictability of seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) revealed that epileptic brain activity shows robust cycles, operating over hours (circadian) and days (multidien). We hypothesised that these cycles can be leveraged to estimate future seizure probability, and we tested the feasibility of forecasting seizures days in advance.
METHODS
We did a feasibility study in distinct development and validation cohorts, involving retrospective analysis of cEEG data recorded with an implanted device in adults (age ≥18 years) with drug-resistant focal epilepsy followed at 35 centres across the USA between Jan 19, 2004, and May 18, 2018. Patients were required to have had 20 or more electrographic seizures (development cohort) or self-reported seizures (validation cohort). In all patients, the device recorded interictal epileptiform activity (IEA; ≥6 months of continuous hourly data), the fluctuations in which helped estimate varying seizure risk. Point process statistical models trained on initial portions of each patient's cEEG data (both cohorts) generated forecasts of seizure probability that were tested on subsequent unseen seizure data and evaluated against surrogate time-series. The primary outcome was the percentage of patients with forecasts showing improvement over chance (IoC).
FINDINGS
We screened 72 and 256 patients, and included 18 and 157 patients in the development and validation cohorts, respectively. Models incorporating information about multidien IEA cycles alone generated daily seizure forecasts for the next calendar day with IoC in 15 (83%) patients in the development cohort and 103 (66%) patients in the validation cohort. The forecasting horizon could be extended up to 3 days while maintaining IoC in two (11%) of 18 patients and 61 (39%) of 157 patients. Forecasts with a shorter horizon of 1 h, possible only for electrographic seizures in the development cohort, showed IoC in all 18 (100%) patients.
INTERPRETATION
This study shows that seizure probability can be forecasted days in advance by leveraging multidien IEA cycles recorded with an implanted device. This study will serve as a basis for prospective clinical trials to establish how people with epilepsy might benefit from seizure forecasting over long horizons.
FUNDING
None. VIDEO ABSTRACT.
背景
癫痫患者的癫痫发作明显具有不可预测性。在过去的十年中，使用慢性脑电图 (cEEG) 的研究提供的证据表明，癫痫大脑活动具有强大的周期，这些周期可以跨越数小时（昼夜节律）和数天（多日节律）。我们假设这些周期可以被利用来估计未来的癫痫发作概率，我们测试了提前几天预测癫痫发作的可行性。
方法
我们在不同的开发和验证队列中进行了一项可行性研究，该研究涉及对 2004 年 1 月 19 日至 2018 年 5 月 18 日期间在美国 35 个中心接受治疗的药物难治性局灶性癫痫成年患者（年龄≥18 岁）的植入设备记录的 cEEG 数据进行回顾性分析。患者需要有 20 次或更多次的癫痫发作（开发队列）或自我报告的癫痫发作（验证队列）。在所有患者中，设备记录了发作间期癫痫样活动 (IEA；≥6 个月的连续每小时数据)，这些波动有助于估计不同的癫痫发作风险。基于每个患者的 cEEG 数据的初始部分训练的点过程统计模型（两个队列）生成了癫痫发作概率的预测，这些预测在随后的未见过的癫痫发作数据上进行了测试，并与替代时间序列进行了评估。主要结果是预测显示出比机会更好（IoC）的患者比例。
结果
我们筛选了 72 名和 256 名患者，分别将 18 名和 157 名患者纳入开发和验证队列。仅包含多日 IEA 周期信息的模型生成了下一个日历日的每日癫痫发作预测，在开发队列中，15 名（83%）患者和验证队列中 103 名（66%）患者的预测显示出比机会更好（IoC）。在开发队列中，在 18 名患者中的 2 名（11%）和在 157 名患者中的 61 名（39%）中，可以将预测时间延长至 3 天，同时保持 IoC。对于开发队列中的电发作，预测的时间范围较短为 1 小时，18 名患者中的所有患者（100%）均显示出 IoC。
结论
本研究表明，可以通过利用植入设备记录的多日 IEA 周期来提前几天预测癫痫发作概率。本研究将作为前瞻性临床试验的基础，以确定癫痫患者如何从长期的癫痫发作预测中受益。
资金
无。视频摘要。
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