Sleep profiles in epilepsy patients undergoing monotherapy and polytherapy: A comparative cross-sectional study.

OBJECTIVE: Sleep disturbances commonly reported among epilepsy patients have a reciprocal relationship with the condition; While epilepsy and anti-seizure medications (ASMs) can disrupt sleep structure, disturbed sleep can also exacerbate the frequency of seizures. This study explored subjective sleep disturbances and compared sleep profiles in patients who underwent ASM monotherapy and polytherapy. METHODS: We enrolled 176 epilepsy patients who completed a structured questionnaire containing demographic and clinical information and the Persian versions of the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), and Patient Health Questionnaire-9 (PHQ-9) to evaluate sleep quality, insomnia, excessive daytime sleepiness (EDS), and depressive symptoms, respectively. Chi-square and Mann-Whitney U tests were employed to analyze the association between variables, and logistic regression analysis was conducted to identify factors predicting sleep disturbances. RESULTS: Comparative analysis of mono/polytherapy groups revealed a significantly higher prevalence of insomnia and EDS among patients on polytherapy compared to monotherapy. However, no significant difference was found in sleep quality between the two groups. Logistic regression analysis revealed that a depressive mood serves as a robust predictor for sleep issues, whereas treatment type did not emerge as an independent predictor of sleep disturbances. CONCLUSION: Our findings suggest that an increased number of ASMs does not inherently result in a higher incidence of sleep issues. Therefore, multiple ASMs may be prescribed when necessary to achieve improved seizure control. Furthermore, this study underscores the importance of comprehensive management that addresses seizure control and treating affective symptoms in individuals with epilepsy.

Clinical Functional Seizure Score (CFSS): a simple algorithm for clinicians to suspect functional seizures.

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.