Clinical Functional Seizure Score (Cfss): A Simple Algorithm for Clinicians to Suspect Functional Seizures Publisher



Dashtkoohi M¹ ; Ranjibourachaloo S^{2, 3} ; Pouremamali R¹ ; Dashtkoohi M¹ ; Zamani R¹ ; Moeinafshar A¹ ; Shizarpour A¹ ; Shakiba S^{2, 3} ; Babaee M^{2, 3} ; Tafakhori A^{2, 3} Show Affiliations
Source: Frontiers in Neurology Published:2023

Abstract

Purpose: 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. Methods: 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. Results: 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%. Conclusion: 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. Copyright © 2023 Dashtkoohi, Ranji-Bourachaloo, Pouremamali, Dashtkoohi, Zamani, Moeinafshar, Shizarpour, Shakiba, Babaee and Tafakhori.