38 Are there any factors that predict the diagnosis of epilepsy or Psychogenic Non-Epileptic Seizures (PNES) in patients admitted to a specialist epilepsy unit?

S. Gibson, S. Leighton, M. Oto
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Abstract

Introduction Differentiating between epilepsy and psychogenic non-epileptic seizures (PNES) can be difficult. Although clearly not a substitute for taking a careful history, certain patient characteristics may assist the clinicians towards diagnosis. The population of patients referred to an epilepsy specialist centre represent a complex and distinct group of patients and it is not clear which factors, if any, could point towards a diagnosis of epilepsy or PNES. Aims/Objectives To investigate if the diagnosis of epilepsy or PNES is predicted by baseline demographic and clinical variables, including Hospital Anxiety and Depression (HADS) scores and medication prescription, in patients admitted to a specialist adult epilepsy centre. Methods We conducted an observational retrospective cohort of consecutive patients admitted to the William Quarrier Scottish Epilepsy Centre (WQSEC) over a period of one year (01/09/16–01/09/17). Chosen predictor variables at baseline included: sex, age, employment education or training (EET), Scottish Index of Multiple Deprivation 2016 (SIMD 2016) rank status, attack frequency, length of index admission, number of anti-epileptic agents prescribed, prescription of benzodiazepines, of analgesia, or of psychotropic medications, and HADS scores. Outcome measures were diagnosis of epilepsy or PNES, from diagnosis made by expert clinicians on discharge from index admission. Because of the presence of dual diagnosis, two multivariable binary logistic regression models were built – one for the epilepsy and one for the PNES diagnosis outcomes. Results 50/73 (69%) of patients admitted were diagnosed with epilepsy and 39/73 (53%) with PNES. These respective groups include 16/73 (22%) who had a dual diagnosis of both epilepsy and PNES. The model to predict epilepsy showed that significant individual predictor variables included number of antiepileptic agents prescribed (Odds Ratio (OR)=3.59 (95%CI: 1.37, 9.42), p=0.010), prescription of psychotropic medications (OR=0.19 (95%CI: 0.04, 0.91), p=0.038), and length of index admission (OR=0.89 (95%CI: 0.81, 0.98), p=0.018). The model to predict presence of PNES revealed only one significant individual predictor variable, which was EET status (OR 0.13 (95%CI: 0.02, 0.86), p=0.035). Conclusions Baseline clinical and demographic factors may be of some utility to the clinician in anticipating a diagnosis of epilepsy or of PNES.
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是否有任何因素可以预测癫痫或心因性非癫痫性发作(PNES)的诊断?
区分癫痫和心因性非癫痫发作(PNES)可能是困难的。虽然显然不能代替仔细的病史,但某些患者特征可能有助于临床医生进行诊断。转介到癫痫专科中心的患者群体是一个复杂而独特的患者群体,目前尚不清楚哪些因素(如果有的话)可以指向癫痫或PNES的诊断。目的/目的调查在成人癫痫专科中心住院的患者中,基线人口统计学和临床变量(包括医院焦虑和抑郁(HADS)评分和药物处方)是否可以预测癫痫或PNES的诊断。方法我们对在William Quarrier苏格兰癫痫中心(WQSEC)连续住院的患者进行了为期一年的观察性回顾性队列研究(01/09/16-01/09/17)。选择的基线预测变量包括:性别、年龄、就业教育或培训(EET)、2016年苏格兰多重剥夺指数(SIMD)排名状态、发作频率、指数入院时间、抗癫痫药物处方数量、苯二氮卓类药物处方、镇痛药或精神药物处方,以及HADS评分。结局指标为癫痫或PNES的诊断,由专家临床医生在出院时做出诊断。由于存在双重诊断,我们建立了两个多变量二元logistic回归模型——一个用于癫痫,一个用于PNES诊断结果。结果50/73(69%)患者诊断为癫痫,39/73(53%)患者诊断为PNES。这些相应的组包括16/73(22%)同时诊断为癫痫和PNES的患者。预测癫痫的模型显示,显著的个体预测变量包括抗癫痫药物处方数(OR= 3.59 (95%CI: 1.37, 9.42), p=0.010),精神药物处方(OR=0.19 (95%CI: 0.04, 0.91), p=0.038)和入院时间(OR=0.89 (95%CI: 0.81, 0.98), p=0.018)。预测PNES存在的模型显示只有一个显著的个体预测变量,即EET状态(OR 0.13 (95%CI: 0.02, 0.86), p=0.035)。结论基线临床和人口学因素可能对临床医生预测癫痫或PNES的诊断有一定的帮助。
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