Differential diagnosis of seizure disorders: A conversation analytic approach

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• 作者：Meike Schwabe ; Stephen J. Howell ; Markus Reuber
• 关键词：Conversation Analysis ; Epilepsy ; Non-epileptic seizures ; Diagnosis ; Dissociative seizures ; Conversion disorder ; Decision-making ; UK
• 刊名：Social Science and Medicine
• 出版年：2007
• 期刊代码：242_02779536
• 类别：med
• 出版时间：August 2007
• 卷：65
• 期：4
• 页码：712-724
• 文件大小：246 K

摘要

“Taking the history” remains the most important diagnostic tool in the assessment of people who have lost consciousness. The distinction of epileptic and non-epileptic seizures (NES) is particularly difficult and relevant. Whereas epileptic seizures can usually be controlled with antiepileptic drugs, NES are considered an expression of psychosocial distress and may improve with psychotherapy. The recording of typical seizures with simultaneous video and electroencephalography (EEG) can produce almost complete certainty about the diagnosis but access to video-EEG is limited, the test is very expensive and often video-EEG fails to capture typical seizures. A German research group used conversation analysis (CA) to examine patients’ descriptions of seizures to their doctors. They found that certain linguistic and interactional features clustered together and that these clusters were usually concordant with particular medical diagnoses.

This study was undertaken to establish whether the observations made in German-speaking patients could be replicated in English speakers presenting to a less specialised epilepsy service. The findings presented here are based on transcripts of interviews with 11 patients admitted to a neurology ward in England because their consultant felt unable to make a clear diagnosis clinically. Transcripts were only analysed if the diagnosis of epilepsy or NES had been proven with video-EEG. The medical diagnosis was only disclosed to the linguist once a linguistic hypothesis of the diagnosis had been formulated to ensure that the linguist's decision would not be influenced by factors not contained in the 30-min-interview between doctor and patient. The linguist predicted the correct diagnosis in all cases.