Kerr Wesley T, Janio Emily A, Braesch Chelsea T, Le Justine M, Hori Jessica M, Patel Akash B, Gallardo Norma L, Bauirjan Janar, D'Ambrosio Shannon R, Chau Andrea M, Hwang Eric S, Davis Emily C, Buchard Albert, Torres-Barba David, Al Banna Mona, Barritt Sarah E, Cho Andrew Y, Engel Jerome, Cohen Mark S, Stern John M
Department of Internal Medicine, Eisenhower Medical Center, Rancho Mirage, California, U.S.A.
Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, California, U.S.A.
Epilepsia. 2017 Nov;58(11):1852-1860. doi: 10.1111/epi.13888. Epub 2017 Sep 12.
Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost.
Based on data from 1,365 adult patients with video-electroencephalography-confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database.
Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72-84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone.
An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.
需要低成本的循证工具来促进对可能患有心因性非癫痫性发作（PNES）患者的早期识别。在准确诊断之前，PNES患者无法接受针对其发作病因的干预措施，因此由于癫痫发作障碍未得到控制而产生高昂的医疗费用并导致残疾。癫痫发作和合并症都可能导致这种高成本。
基于来自单一中心的1365例经视频脑电图确诊的成年患者的数据，我们使用逻辑回归和泊松回归比较了五个相互排斥的诊断组中的合并症总数、药物数量和特定合并症的存在情况：仅癫痫发作（ES）、仅PNES、PNES和ES混合、生理性非癫痫性发作样事件以及监测结果不明确。为了确定合并诊断和用药史对仅区分PNES和仅ES的诊断效用，我们使用多变量逻辑回归，控制性别和年龄，使用回顾性数据库进行训练，并使用前瞻性数据库进行验证。
我们的模型区分仅PNES和仅ES的前瞻性准确率为78%（95%置信区间=72-84%），曲线下面积为79%。除了少数例外，合并症和药物数量比特定合并症更具预测性。与PNES相关的合并症是哮喘、慢性疼痛和偏头痛（p<0.01）。与ES相关的合并症是糖尿病和非转移性肿瘤（p<0.01）。人群水平分析表明，PNES和ES混合的患者可能是一个与单独患有这两种疾病的患者不同的人群。
准确的患者报告病史和用药史在筛查可能的PNES时可能有用。我们经过前瞻性验证的客观评分可能有助于在癫痫发作描述和病史背景下解读用药和病史。
