Suppr超能文献

在无癫痫发作的患者中停用抗癫痫药物后癫痫复发和长期结局的个体化预测模型:系统评价和个体参与者数据荟萃分析。

Individualised prediction model of seizure recurrence and long-term outcomes after withdrawal of antiepileptic drugs in seizure-free patients: a systematic review and individual participant data meta-analysis.

机构信息

Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands.

Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands; Stichting Epilepsie Instellingen Nederland, Heemstede, Netherlands.

出版信息

Lancet Neurol. 2017 Jul;16(7):523-531. doi: 10.1016/S1474-4422(17)30114-X. Epub 2017 May 5.

Abstract

BACKGROUND

People with epilepsy who became seizure-free while taking antiepileptic drugs might consider discontinuing their medication, with the possibility of increased quality of life because of the elimination of adverse events. The risk with this action, however, is seizure recurrence. The objectives of our study were to identify predictors of seizure recurrence and long-term seizure outcomes and to produce nomograms for estimation of individualised outcomes.

METHODS

We did a systematic review and meta-analysis, and identified eligible articles and candidate predictors, using PubMed and Embase databases with a last update on Nov 6, 2014. Eligible articles had to report on cohorts of patients with epilepsy who were seizure-free and had started withdrawal of antiepileptic drugs; articles also had to contain information regarding seizure recurrences during and after withdrawal. We excluded surgical cohorts, reports with fewer than 30 patients, and reports on acute symptomatic seizures because these topics were beyond the scope of our objective. Risk of bias was assessed using the Quality in Prognosis Studies system. Data analysis was based on individual participant data. Survival curves and proportional hazards were computed. The strongest predictors were selected with backward selection. Models were converted to nomograms and a web-based tool to determine individual risks.

FINDINGS

We identified 45 studies with 7082 patients; ten studies (22%) with 1769 patients (25%) were included in the meta-analysis. Median follow-up was 5·3 years (IQR 3·0-10·0, maximum 23 years). Prospective and retrospective studies and randomised controlled trials were included, covering non-selected and selected populations of both children and adults. Relapse occurred in 812 (46%) of 1769 patients; 136 (9%) of 1455 for whom data were available had seizures in their last year of follow-up, suggesting enduring seizure control was not regained by this timepoint. Independent predictors of seizure recurrence were epilepsy duration before remission, seizure-free interval before antiepileptic drug withdrawal, age at onset of epilepsy, history of febrile seizures, number of seizures before remission, absence of a self-limiting epilepsy syndrome, developmental delay, and epileptiform abnormality on electroencephalogram (EEG) before withdrawal. Independent predictors of seizures in the last year of follow-up were epilepsy duration before remission, seizure-free interval before antiepileptic drug withdrawal, number of antiepileptic drugs before withdrawal, female sex, family history of epilepsy, number of seizures before remission, focal seizures, and epileptiform abnormality on EEG before withdrawal. Adjusted concordance statistics were 0·65 (95% CI 0·65-0·66) for predicting seizure recurrence and 0·71 (0·70-0·71) for predicting long-term seizure freedom. Validation was stable across the individual study populations.

INTERPRETATION

We present evidence-based nomograms with robust performance across populations of children and adults. The nomograms facilitate prediction of outcomes following drug withdrawal for the individual patient, including both the risk of relapse and the chance of long-term freedom from seizures. The main limitations were the absence of a control group continuing antiepileptic drug treatment and a consistent definition of long-term seizure freedom.

FUNDING

Epilepsiefonds.

摘要

背景

正在服用抗癫痫药物且已无癫痫发作的癫痫患者可能会考虑停止用药,从而提高生活质量,因为这可以减少不良反应的发生。但是,这种做法的风险是癫痫复发。我们的研究目的是确定癫痫复发和长期癫痫结局的预测因素,并生成用于估计个体化结局的列线图。

方法

我们进行了系统评价和荟萃分析,通过对 PubMed 和 Embase 数据库进行检索,确定了符合条件的文章和候选预测因素,检索日期截至 2014 年 11 月 6 日。符合条件的文章必须报告癫痫患者的队列,这些患者在无癫痫发作且已开始停用抗癫痫药物;文章还必须包含有关停药期间和停药后癫痫复发的信息。我们排除了手术队列、患者少于 30 人的报告以及急性症状性癫痫发作的报告,因为这些内容超出了我们的研究目的。使用预后研究质量评估系统评估偏倚风险。数据分析基于个体参与者的数据。计算生存曲线和比例风险。使用向后选择选择最强的预测因素。将模型转换为列线图和基于网络的工具,以确定个体风险。

结果

我们共确定了 45 项研究,共 7082 例患者;其中 10 项(22%)研究共 1769 例患者(25%)纳入荟萃分析。中位随访时间为 5.3 年(IQR 3.0-10.0,最长 23 年)。纳入了前瞻性和回顾性研究以及随机对照试验,涵盖了儿童和成人的非选择性和选择性人群。在 1769 例患者中,有 812 例(46%)复发;136 例(9%)在随访的最后一年有癫痫发作,这表明到此时并没有恢复持久的癫痫控制。癫痫缓解前的癫痫持续时间、抗癫痫药物停药前的无癫痫发作间隔、癫痫发作起始年龄、热性惊厥史、缓解前的癫痫发作次数、无自限性癫痫综合征、发育迟缓以及停药前脑电图(EEG)上的癫痫样异常是癫痫复发的独立预测因素。随访最后一年癫痫发作的独立预测因素是癫痫缓解前的癫痫持续时间、抗癫痫药物停药前的无癫痫发作间隔、停药前的抗癫痫药物数量、女性、癫痫家族史、缓解前的癫痫发作次数、局灶性癫痫发作和停药前 EEG 上的癫痫样异常。调整后的一致性统计量为 0.65(95%CI 0.65-0.66),用于预测癫痫复发;为 0.71(0.70-0.71),用于预测长期无癫痫发作。验证在各个研究人群中均稳定。

解释

我们提供了基于证据的列线图,这些列线图在儿童和成人的人群中具有良好的性能。该列线图有助于预测个体患者停药后的结局,包括复发风险和长期无癫痫发作的机会。主要局限性是缺乏继续接受抗癫痫药物治疗的对照组和长期无癫痫发作的一致定义。

资助

癫痫基金。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验