耐药性癫痫患者的长期植入式癫痫预警系统预测癫痫发作的可能性:首例人体研究。

Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.

机构信息

St Vincent's Hospital, Melbourne, Victoria, Australia.

出版信息

Lancet Neurol. 2013 Jun;12(6):563-71. doi: 10.1016/S1474-4422(13)70075-9. Epub 2013 May 2.

Abstract

BACKGROUND

Seizure prediction would be clinically useful in patients with epilepsy and could improve safety, increase independence, and allow acute treatment. We did a multicentre clinical feasibility study to assess the safety and efficacy of a long-term implanted seizure advisory system designed to predict seizure likelihood and quantify seizures in adults with drug-resistant focal seizures.

METHODS

We enrolled patients at three centres in Melbourne, Australia, between March 24, 2010, and June 21, 2011. Eligible patients had between two and 12 disabling partial-onset seizures per month, a lateralised epileptogenic zone, and no history of psychogenic seizures. After devices were surgically implanted, patients entered a data collection phase, during which an algorithm for identification of periods of high, moderate, and low seizure likelihood was established. If the algorithm met performance criteria (ie, sensitivity of high-likelihood warnings greater than 65% and performance better than expected through chance prediction of randomly occurring events), patients then entered an advisory phase and received information about seizure likelihood. The primary endpoint was the number of device-related adverse events at 4 months after implantation. Our secondary endpoints were algorithm performance at the end of the data collection phase, clinical effectiveness (measures of anxiety, depression, seizure severity, and quality of life) 4 months after initiation of the advisory phase, and longer-term adverse events. This trial is registered with ClinicalTrials.gov, number NCT01043406.

FINDINGS

We implanted 15 patients with the advisory system. 11 device-related adverse events were noted within four months of implantation, two of which were serious (device migration, seroma); an additional two serious adverse events occurred during the first year after implantation (device-related infection, device site reaction), but were resolved without further complication. The device met enabling criteria in 11 patients upon completion of the data collection phase, with high likelihood performance estimate sensitivities ranging from 65% to 100%. Three patients' algorithms did not meet performance criteria and one patient required device removal because of an adverse event before sufficient training data were acquired. We detected no significant changes in clinical effectiveness measures between baseline and 4 months after implantation.

INTERPRETATION

This study showed that intracranial electroencephalographic monitoring is feasible in ambulatory patients with drug-resistant epilepsy. If these findings are replicated in larger, longer studies, accurate definition of preictal electrical activity might improve understanding of seizure generation and eventually lead to new management strategies.

FUNDING

NeuroVista.

摘要

背景

癫痫患者的癫痫发作预测在临床上可能会很有用,可以提高安全性、增加独立性并允许进行急性治疗。我们进行了一项多中心临床可行性研究,以评估一种旨在预测药物难治性局灶性癫痫发作患者癫痫发作可能性和量化癫痫发作的长期植入式癫痫预警系统的安全性和有效性。

方法

我们于 2010 年 3 月 24 日至 2011 年 6 月 21 日在澳大利亚墨尔本的三个中心招募了患者。符合条件的患者每月有 2 至 12 次致残性部分发作,有侧化致痫区,没有心因性癫痫发作病史。在设备被手术植入后,患者进入数据收集阶段,在此期间建立了一种用于识别高、中、低癫痫发作可能性期的算法。如果算法符合性能标准(即高可能性警告的敏感性大于 65%,并且通过对随机发生的事件进行机会预测的性能优于预期),则患者随后进入预警阶段并获得有关癫痫发作可能性的信息。主要终点是植入后 4 个月的设备相关不良事件数量。我们的次要终点是数据收集阶段结束时的算法性能、咨询阶段开始后 4 个月的临床效果(焦虑、抑郁、癫痫严重程度和生活质量的测量)以及长期不良事件。这项试验在 ClinicalTrials.gov 上注册,编号为 NCT01043406。

结果

我们为 15 名患者植入了预警系统。在植入后 4 个月内发现了 11 起与设备相关的不良事件,其中 2 起为严重不良事件(设备迁移、血清肿);在植入后第一年又发生了另外两起严重不良事件(与设备相关的感染、设备部位反应),但无需进一步并发症即可解决。在数据收集阶段完成后,有 11 名患者的设备符合启用标准,高可能性性能估计敏感性范围为 65%至 100%。有 3 名患者的算法不符合性能标准,1 名患者因不良事件发生在获得足够训练数据之前而需要移除设备。我们没有发现临床效果测量值在植入后 4 个月与基线相比有显著变化。

解释

这项研究表明,颅内脑电图监测在药物难治性癫痫的门诊患者中是可行的。如果这些发现能在更大、更长的研究中得到复制,那么对发作前电活动的准确定义可能会提高对癫痫发作产生的理解,并最终导致新的管理策略。

资金来源

NeuroVista。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索