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本文引用的文献

1
Identifying psychogenic seizures through comorbidities and medication history.通过合并症和用药史识别心因性癫痫发作。
Epilepsia. 2017 Nov;58(11):1852-1860. doi: 10.1111/epi.13888. Epub 2017 Sep 12.
2
Three Year Trends in Veterans Health Administration Utilization and Costs after Traumatic Brain Injury Screening among Veterans with Mild Traumatic Brain Injury.轻度创伤性脑损伤退伍军人创伤性脑损伤筛查后的退伍军人健康管理局利用和费用的三年趋势。
J Neurotrauma. 2017 Sep;34(17):2567-2574. doi: 10.1089/neu.2016.4910. Epub 2017 Jun 27.
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Mild Traumatic Brain Injury (mTBI) and chronic cognitive impairment: A scoping review.轻度创伤性脑损伤(mTBI)与慢性认知障碍:一项范围综述
PLoS One. 2017 Apr 11;12(4):e0174847. doi: 10.1371/journal.pone.0174847. eCollection 2017.
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Psychogenic nonepileptic seizures: a concise review.心因性非癫痫性发作:简要综述。
Neurol Sci. 2017 Jun;38(6):935-940. doi: 10.1007/s10072-017-2887-8. Epub 2017 Mar 8.
5
Risk Factors for Diagnostic Delay in Psychogenic Nonepileptic Seizures Among Children and Adolescents.儿童和青少年心因性非癫痫性发作诊断延迟的危险因素
Pediatr Neurol. 2017 Feb;67:71-77. doi: 10.1016/j.pediatrneurol.2016.10.021. Epub 2016 Nov 9.
6
Review of systems questionnaire helps differentiate psychogenic nonepileptic seizures from epilepsy.系统回顾问卷有助于区分精神性非癫痫性发作与癫痫。
J Clin Neurosci. 2016 Dec;34:105-107. doi: 10.1016/j.jocn.2016.05.037. Epub 2016 Jul 26.
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Prognostic models for predicting posttraumatic seizures during acute hospitalization, and at 1 and 2 years following traumatic brain injury.用于预测急性住院期间以及创伤性脑损伤后1年和2年创伤后癫痫发作的预后模型。
Epilepsia. 2016 Sep;57(9):1503-14. doi: 10.1111/epi.13470. Epub 2016 Jul 19.
8
Diagnostic delay in psychogenic seizures and the association with anti-seizure medication trials.心因性发作的诊断延迟及其与抗癫痫药物试验的关联。
Seizure. 2016 Aug;40:123-6. doi: 10.1016/j.seizure.2016.06.015. Epub 2016 Jun 23.
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Depression and dissociation as predictors of physical health symptoms among female rape survivors with posttraumatic stress disorder.抑郁和解离作为创伤后应激障碍女性强奸幸存者身体健康症状的预测因素。
Psychol Trauma. 2016 Sep;8(5):585-91. doi: 10.1037/tra0000135. Epub 2016 May 5.
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Nonepileptic seizures: an updated review.非癫痫性发作:最新综述。
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基于发病年龄和病史识别心因性发作的客观评分。

An objective score to identify psychogenic seizures based on age of onset and history.

机构信息

Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Internal Medicine, Eisenhower Medical Center, Rancho Mirage, CA, United States.

Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States.

出版信息

Epilepsy Behav. 2018 Mar;80:75-83. doi: 10.1016/j.yebeh.2017.11.035. Epub 2018 Feb 2.

DOI:10.1016/j.yebeh.2017.11.035
PMID:29414562
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5845850/
Abstract

OBJECTIVE

Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic.

METHODS

Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews.

RESULTS

The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70-79%) and prospective specificity of 71% (95% CI: 64-82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20years, age of onset after 31years for men, and age of onset after 40years for women had no additional effect on the likelihood of PNES.

DISCUSSION

Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men.

摘要

目的

在进行视频-脑电图监测(VEM)评估药物难治性或非典型性癫痫发作后,常诊断为精神性非癫痫性发作(PNES),但通常在癫痫发作后很长一段时间后才出现,在此期间患者会接受癫痫治疗。因此,需要更易于获得的诊断工具来更早地识别有发生 PNES 风险的患者。基于患者报告的社会心理病史的工具将特别有益,因为它可以在门诊实施。

方法

根据 1375 名 VEM 确诊患者的数据,我们使用逻辑回归比较了五个互斥患者组中特定患者报告的历史事件、人口统计学信息、发病年龄和从首次癫痫发作到 VEM 的时间的频率:癫痫发作(ES)、PNES、生理性非癫痫样发作样事件(PSLE)、混合 PNES 加 ES 和不确定监测。为了确定这些信息区分仅为 PNES 和仅为 ES 的诊断效用,我们使用基于图表回顾的回顾性数据和基于 246 项前瞻性标准化访谈数据训练的多元分段线性逻辑回归来确定。

结果

按性别加权的多元分段线性回顾性曲线下面积为 73%,最大程度提高总体回顾准确性的阈值导致前瞻性敏感性为 74%(95%CI:70-79%)和前瞻性特异性为 71%(95%CI:64-82%)。线性模型和没有性别交互项的分段线性模型具有非常相似的性能统计数据。在多元分段线性性别拆分预测模型中,与 ES 呈正相关的显著因素是热性惊厥史、当前就业或学生身份、创伤性脑损伤(TBI)史以及从首次癫痫发作到 VEM 的时间更长。与 PNES 相关的显著因素是女性、发病年龄较大、轻度 TBI 以及与性虐待相关的重大应激事件,尤其是增加了发生 PNES 的可能性。延迟超过 20 年、男性发病年龄超过 31 岁以及女性发病年龄超过 40 岁不会增加发生 PNES 的可能性。

讨论

我们有希望的结果表明,当与其他因素结合考虑时,客观评分有可能作为早期门诊筛查工具来识别更有可能发生 PNES 的患者。此外,我们的分析表明,性虐待比包括身体虐待在内的其他心理压力源更与 PNES 相关。在生育期,女性发生 PNES 的频率呈上升趋势,而在生育期之外则趋于平稳,而男性则没有观察到这种趋势。