Background/Objectives: Epilepsy is a long-term condition that affects the brain and has a big impact on a person’s daily life, especially in areas where people do not have a lot of money or access to good healthcare. This study aimed to evaluate the relationship between medication adherence and QoL and to assess the role of seizure severity in the association between them among patients with epilepsy. Methods: A cross-sectional study of 1100 adult patients with epilepsy was conducted using registry data and structured interviews. The main outcomes that were assessed are quality of life (QoL), medication adherence, and seizure severity. Results: Reduced QoL was observed in 62% of patients. Low medication adherence was significantly associated with reduced QoL (OR = 4.33 [3.24–5.79] unadjusted; 3.90 [3.07–5.80] fully adjusted). Seizure severity was also associated with reduced QoL (OR = 1.62, p = 0.002; OR = 2.05, p < 0.001). Cognitive impairment showed the strongest association with reduced QoL, with ORs of 14.6 for mild and 80.8 for moderate-severe impairment in unadjusted models, remaining significant after adjustment. Medication adherence was significantly associated with seizure severity (OR = 1.18, p = 0.002), and attenuation of its effect after adjustment suggests that these variables are interrelated, although causality cannot be determined in this study. Additional factors associated with reduced QoL included lower education, longer disease duration, polytherapy, structural brain abnormalities, and comorbidities. Conclusions: Reduced QoL in epilepsy is strongly influenced by cognitive impairment and medication nonadherence, with seizure severity potentially contributing to this association, although causality cannot be inferred. These findings support integrated care strategies targeting adherence, cognition, and seizure control to improve patient outcomes.

Background/Objectives: Adult epilepsy is clinically heterogeneous, and individual clinical predictors may not fully capture the multidimensional burden associated with drug-resistant epilepsy (DRE). This study aimed to identify latent clinical phenotypes in adults with epilepsy and examine their cross-sectional associations with DRE and broader disease burden. Methods: This regional observational cohort study used a source database of 1100 patients with epilepsy. After excluding two patients aged <18 years, the adult analytic cohort included 1098 patients. Complete-case latent class analysis (LCA) was performed in 1054 patients using age at onset, disease duration, seizure type, seizure frequency, serial seizures/status, postictal confusion, neurological status, neuroimaging category, and number of antiseizure medications. Model selection was based on statistical fit, class size, and clinical interpretability. Internal clinical validation outcomes included DRE, quality of life, cognitive screening, and stigma scores. Post hoc characterization described the classes by epilepsy etiology, derived epilepsy type, and seizure categories aligned with current terminology. Results: A three-class solution was selected, with class sizes of 314, 465, and 275. DRE prevalence increased stepwise across classes: 5.7%, 14.2%, and 33.1%, respectively (p < 0.001). In adjusted analysis, Class 2 had higher odds of DRE than Class 1 (odds ratio 2.70, 95% confidence interval 1.56–4.67), while Class 3 showed the strongest association (odds ratio 8.19, 95% confidence interval 4.15–16.16; both p < 0.001). Higher-burden classes showed lower quality-of-life and cognitive scores and higher stigma scores. Conclusions: LCA identified three clinically interpretable, burden-enriched phenotypic profiles associated with a stepwise gradient in DRE and broader multidimensional disease burden. These cross-sectional profiles may provide a useful framework for describing clinical heterogeneity in adult epilepsy and generating hypotheses for future validation studies.

Background/Objectives: In resource-limited regions, limited access to genetic testing may delay etiological diagnosis in selected patients with epilepsy and complex neurological manifestations, particularly when the phenotype raises suspicion of an underlying genetic etiology. This study aimed to assess the diagnostic value and clinical utility of phenotype-guided WES in selected patients aged ≥17 years with epilepsy and complex neurological manifestations in Southern Kazakhstan and to characterize clinical, therapeutic, and molecular features associated with drug-resistant epilepsy (DRE). Methods: This observational cohort included 78 patients aged ≥17 years with epilepsy and complex neurological manifestations identified through 25 outpatient medical centers. After enrollment, all patients underwent structured HPO-based phenotyping, assessment of antiseizure medication exposure, seizure burden, treatment response, and proband-only WES. Variants were interpreted according to ACMG/AMP guidelines. Exploratory multivariable logistic regression was used to assess factors associated with DRE. Results: P/LP variants were identified in 12/78 patients, corresponding to a diagnostic yield of 15.4%. VUS were detected in 16/78 patients (20.5%). DRE was present in 41 patients (52.6%), all of whom were receiving polytherapy. Carbamazepine was the most frequently used antiseizure medication (52/78, 66.7%), followed by valproic acid (32/78, 41.0%) and levetiracetam (20/78, 25.6%). Patients with DRE had earlier seizure onset, more frequent definite structural MRI abnormalities, and a higher proportion of P/LP variants. In the adjusted model, P/LP variant presence and definite structural MRI abnormality were associated with DRE. Conclusions: Phenotype-guided proband-only WES provided clinically relevant diagnostic information in a real-world epilepsy cohort with complex neurological manifestations in a resource-limited outpatient setting. The observed burden of DRE and polytherapy supports the potential value of integrating therapeutic profiling, neuroimaging, and genomic testing in the etiological evaluation of patients with epilepsy and additional neurological features.