Dashtkoohi Mohammad, Ranji-Bourachaloo Sakineh, Pouremamali Rozhina, Dashtkoohi Mohadese, Zamani Raha, Moeinafshar Aysan, Shizarpour Arshia, Shakiba Shirin, Babaee Mohammadali, Tafakhori Abbas
Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran.
Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
Front Neurol. 2023 Nov 29;14:1295266. doi: 10.3389/fneur.2023.1295266. eCollection 2023.
Distinguishing functional seizures (FS) from epileptic seizures (ES) poses a challenge due to similar clinical manifestations. The creation of a clinical scoring system that assists in accurately diagnosing patients with FS would be a valuable contribution to medical practice. This score has the potential to enhance clinical decision-making and facilitate prompt diagnosis of patients with FS.
Participants who met the inclusion criteria were randomly divided into three distinct groups: training, validation, and test cohorts. Demographic and semiological variables were analyzed in the training cohort by univariate analyses. Variables that showed a significant difference between FS and ES were then further scrutinized in two multivariate logistic regression models. The CFSS was developed based on the odds ratio of the discriminating variables. Using the validation group, the optimal cutoff value was determined based on the AUC, and then the CFSS was evaluated in the test cohort to assess its performance.
The developed score yielded an AUC of 0.78 in the validation cohort, and a cutoff point of 6 was established with a focus on maximizing sensitivity without significantly compromising specificity. The score was then applied in the test cohort, where it achieved a sensitivity of 86.96% and a specificity of 73.81%.
We have developed a new tool that shows promising results in identifying patients suspicious of FS. With further analysis through prospective studies, this innovative, simple tool can be integrated into the diagnostic process of FS.
由于临床表现相似，区分功能性癫痫发作（FS）和癫痫性发作（ES）具有挑战性。创建一个有助于准确诊断FS患者的临床评分系统将对医学实践做出宝贵贡献。该评分有可能改善临床决策并促进FS患者的快速诊断。
符合纳入标准的参与者被随机分为三个不同的组：训练组、验证组和测试组。通过单变量分析在训练组中分析人口统计学和症状学变量。然后在两个多变量逻辑回归模型中进一步仔细研究FS和ES之间显示出显著差异的变量。基于鉴别变量的比值比开发了CFSS。使用验证组，根据AUC确定最佳截断值，然后在测试组中评估CFSS以评估其性能。
开发的评分在验证组中的AUC为0.78，并确定截断点为6，重点是在不显著损害特异性的情况下最大化敏感性。然后将该评分应用于测试组，在该组中其敏感性达到86.96%，特异性达到73.81%。
我们开发了一种新工具，在识别疑似FS患者方面显示出有希望的结果。通过前瞻性研究进行进一步分析后，这种创新、简单的工具可纳入FS的诊断过程。
