BAYESIAN APPROACH FOR JOINT MODELING OF TIME TO SEIZURE FREEDOM AND SEIZURE FREQUENCY COUNT IN EPILEPTIC PATIENT AT HAWASSA UNIVERSITY COMPREHENSIVE SPECIALIZED HOSPITAL

Abstract

Background: Epilepsy was classified as a chronic, non-communicable brain condition by the World Health Organization. Over 50 million people globally have been impacted by epilepsy, one of the most common neurological disorders. In Ethiopia, epilepsy is one of the top 20 killers, and 5.2 out of every 1000 people will suffer from it at some point in their lifetime. The main objective of the study was to investigate predictors of seizure attacks progression and time to seizure freedom among epileptic patients using separate and joint analysis in Bayesian approach. Methodology: The study analyzed data from 203 epileptic patients who initiated anti-epileptic drugs (AEDs) at Hawassa University Comprehensive Specialized Hospital Neurologic Clinic between 1st May 2018 up to 1st May 2023. A retrospective cohort study design was carried out and epileptic patients age greater than 18 years old were used as source of population for this study and also the data obtained from HUCSH. A Bayesian approach for joint modeling is used to analysis time to seizure freedom and seizure frequency count. Results: Out of these patients, 80.3% (163) achieved seizure freedom, while 19.7% (40) were censored due to not achieving seizure freedom within the study period. Analyzing factors influencing seizure outcomes, the study found that Phenytoin usage showed a statistically significant positive effect on seizure reduction, while Phenobarbitone and Sodium Valproate did not exhibit significant effects. Having more treatment sessions had a significant positive effect on reducing seizures. Patients with a partial seizure type showed a significant increase in seizure frequency, while those who exercised, had a family history of epilepsy, or consumed alcohol experienced a significant reduction in seizure frequency. Patients without chronic diseases had significantly fewer seizures. Moreover, patients with co-morbidities or a history of alcohol consumption had a higher frequency of seizures. Conclusion: Bayesian joint modeling revealed that the Weibull survival model and Negative Binomial Zero-Inflated model provided the best fit for survival and count data, respectively. This study's findings contribute to a comprehensive understanding of the factors influencing seizure freedom and seizure frequency in epileptic patients, offering valuable insights for clinical management and treatment strategies.