Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Epilepsy Res. 2021 Mar;171:106563. doi: 10.1016/j.eplepsyres.2021.106563. Epub 2021 Jan 21.
Functional seizures (FS) are often misclassified as epileptic seizures (ES). This study aimed to create an easy to use but comprehensive screening tool to guide further evaluation of patients presenting with this diagnostic dilemma.
Demographic, clinical and diagnostic data were collected on patients admitted for video-EEG monitoring for clarification of their diagnosis. Upon discharge, patients were classified as having ES vs FS. Using the collected characteristics and video-EEG diagnosis, we created a multivariable logistic regression model to identify predictors of ES. Then, we trained an integer-coefficient model with the most frequently selected predictors, creating a pointing system coined DDESVSFS, with scores ranging from -17 to +8 points.
43 patients with FS and 165 patients with ES were recruited. In the final integer-coefficient model, 8 predictors were identified as significant in differentiating ES from FS: normal electroencephalogram (-3 points), predisposing factors for FS (-3 points), increased number of comorbidities (-3 points), semiology suggestive of FS (-4 points), increased seizure frequency (-4 points), longer disease duration (+3 points), antiepileptic polypharmacy (+2 points) and compliance with antiepileptic drugs (+3 points). Cumulative scores of ≤ -9 points carried <5% predictive value for ES, while cumulative scores of ≥ -1 points carried >95% predictive value. The model performed well (AUC: 0.923, sensitivity: 0.945, specificity: 0.698).
We propose DDESVSFS as a simple, rapid and comprehensive prediction score for the Differential Diagnosis of Epileptic Seizures VS Functional Seizures. Large prospective studies are needed to evaluate its utility in clinical practice.
功能性发作（FS）常被误诊为癫痫发作（ES）。本研究旨在创建一种易于使用但全面的筛选工具，以指导对出现这种诊断难题的患者进行进一步评估。
收集入院进行视频脑电图监测以明确诊断的患者的人口统计学、临床和诊断数据。出院时，患者被分类为 ES 或 FS。使用收集到的特征和视频脑电图诊断，我们建立了多变量逻辑回归模型来识别 ES 的预测因素。然后，我们使用最常选择的预测因素训练整数系数模型，创建一个命名为 DDESVSFS 的评分系统，分数范围为-17 至+8 分。
共纳入 43 例 FS 和 165 例 ES 患者。在最终的整数系数模型中，有 8 个预测因素被确定为区分 ES 和 FS 的重要因素：正常脑电图（-3 分）、FS 的易患因素（-3 分）、合并症数量增加（-3 分）、半侧发作提示 FS（-4 分）、发作频率增加（-4 分）、疾病持续时间较长（+3 分）、抗癫痫药多药治疗（+2 分）和对癫痫药物的依从性（+3 分）。累积得分≤-9 分对 ES 的预测价值<5%，而累积得分≥-1 分对 ES 的预测价值>95%。该模型性能良好（AUC：0.923，灵敏度：0.945，特异性：0.698）。
我们提出 DDESVSFS 作为一种简单、快速和全面的预测评分，用于癫痫发作与功能性发作的鉴别诊断。需要进行大型前瞻性研究来评估其在临床实践中的应用。
