心因性非癫痫性发作临床分类

作者：高晓方译 

法国的一项研究显示，可依据视频脑电图所测得临床指征的自动归类，对心因性非癫痫性发作（PNES）作出客观临床分类。研究报告2011年5月10日在线发表于英国《神经病学、神经外科学、精神病学杂志》（J Neurol Neurosurg Psychiatry）。

PNES的发作性行为改变与癫痫发作相似，但不伴有癫痫的电生理改变。PNES由精神病理学过程所导致，诊断主要基于病史和视频脑电图。临床表现包括多种症状和体征，既无特异性也无敏感性，从而使得PNES诊断困难。PNES往往被误诊为癫痫。此项研究共纳入52例患者，通过视频脑电图对145次PNES的22种临床指征进行回顾性分析，然后实施多重对应分析和分层聚类分析。

结果显示，依据其主要临床特征共确认并命名5类指征：有原始手势活动的张力障碍发作（dystonic attack with primitive gestural activity）（31.6%）；保留对刺激反应能力的运动减少发作（pauci-kinetic attack with preserved responsiveness）（23.4%）；假性晕厥（pseudosyncope）（16.9%）；伴过度换气和先兆的运动过度延长发作（hyperkinetic prolonged attack with hyperventilation and auras）（11.7%）；轴张力障碍延长发作（axial dystonic prolonged attack）（16.4%）。

相关链接：Clinical classification of psychogenic non-epileptic seizures based on video-EEG analysis and automatic clustering

Background Psychogenic non-epileptic seizures (PNES) or attacks consist of paroxysmal behavioural changes that resemble an epileptic seizure but are not associated with electrophysiological epileptic changes. They are caused by a psychopathological process and are primarily diagnosed on history and video-EEG. Clinical presentation comprises a wide range of symptoms and signs, which are individually neither totally specific nor sensitive, making positive diagnosis of PNES difficult. Consequently, PNES are often misdiagnosed as epilepsy. The aim of this study was to identify homogeneous groups of PNES based on specific combinations of clinical signs with a view to improving timely diagnosis.

Methods The authors first retrospectively analysed 22 clinical signs of 145 PNES recorded by video-EEG in 52 patients and then conducted a multiple correspondence analysis and hierarchical cluster analysis.

Results Five clusters of signs were identified and named according to their main clinical features:

dystonic attack with primitive gestural activity (31.6%);

pauci-kinetic attack with preserved responsiveness (23.4%);

pseudosyncope (16.9%);

hyperkinetic prolonged attack with hyperventilation and auras (11.7%);

axial dystonic prolonged attack (16.4%).

When several attacks were recorded in the same patient, they were automatically classified in the same subtype in 61.5% of patients.

Conclusion This study proposes an objective clinical classification of PNES based on automatic clustering of clinical signs observed on video-EEG. It also suggests that PNES are stereotyped in the same patient. Application of these findings could help provide an objective diagnosis of patients with PNES.