Burden and Determinants of Adverse Effects from Antiseizure Medications: Insights from Saudi Cohort

<i>Background and objectives</i>: Antiseizure medications are essential for epilepsy management but often cause adverse effects that impact treatment adherence and quality of life. This study investigates the incidence rate and determinants of high-burden adverse effects of antiseizure medications. <i>Materials and Methods</i>: This study was a cross-sectional study including data extraction by a medical record review and administration of a standardized scale. It was conducted at an epilepsy outpatient clinic in Saudi Arabia and included adult patients on antiseizure medications. The validated Arabic version of the Liverpool Adverse Events Profile (LAEP) was used. The total LAEP scores ranged from 19 to 76. In this study, LAEP scores ≥ 45 were classified as high-burden adverse effects. <i>Results</i>: Of 153 included patients, 84 (54.9%) had high-burden adverse effects. The overall mean (SD) LAEP score was 45.63 (21.04). The most frequently rated adverse effects were difficulty in concentrating, with a mean score of 2.71 out of 4, followed closely by disturbed sleep (2.69), sleepiness (2.63), and memory problems (2.56). Of examined variables, generalized seizure and polytherapy were significantly associated with increased adverse effects. Likewise, uncontrolled seizure and presence of depression comorbidity were also associated with increased risk of adverse effects, but not statistically significant. <i>Conclusion</i>: The study reported a high rate of adverse effects of antiseizure medications and identified patients at high risk of adverse effects. Early recognition of these patients is important to provide appropriate care, including counselling, regular monitoring, and management of psychiatric comorbidities. Central nervous system symptoms were the most frequently reported adverse effects. Initiation of antiseizure medications with low doses and gradual titration may improve tolerability. Future research should focus on prediction adverse effects using pharmacogenomic AI-based decision-making tools.