Jote Abiy Disasa, Lelisho Mesfin Esayas, Sheferaw Wegayehu Enbeyle
Department of Statistics, College of Natural Science, Jimma University, Jimma, Ethiopia.
Department of Statistics, College of Natural and Computational Sciences, Mizan-Tepi University, Tepi, Ethiopia.
Sci Rep. 2025 Sep 1;15(1):32195. doi: 10.1038/s41598-025-17991-2.
Epilepsy remains a significant global health concern with increasing prevalence and incidence. This study aimed to model the time to first remission among epilepsy patients at Jimma University Medical Center, Ethiopia, using parametric shared frailty models. A retrospective study was conducted on epilepsy patients treated between 1st January 2018 and 30th December 2023. All patients received anti-seizure medications (ASMs) upon enrollment. Additionally, 12% of the cohort had received ASM treatment prior to enrollment outside JUMC. Log-logistic, log-normal, and Weibull baseline hazard functions were combined with gamma and inverse Gaussian frailty distributions to model time to first remission. Model selection was based on the Akaike Information Criterion (AIC). The median time for patients to achieve their first seizure remission was 38 months, with 45.5% (95% CI: 40.9-50.1%) of patients experiencing remission. The variability in remission times across different districts, as modelled by the log-normal-inverse Gaussian shared frailty model, was estimated to be θ = 0.454. Patients aged 25-44 years (acceleration factor 1.13 [95% CI 1.04-1.23], p = 0.005], those with more than five pre-treatment seizures (acceleration factor 1.08 [95% CI 1.02-1.15, p = 0.018]), and individuals with focal epilepsy (acceleration factor 1.15 [95% CI 1.07-1.25, p = 0.003]) were associated with significantly longer remission times compared to other patient groups while those with good treatment adherence (acceleration factor 0.88 [95% CI 0.81-0.96, p = 0.005]) were associated with significantly shorter remission times compared to poor treatment adherence. The log-normal-inverse Gaussian shared frailty model offers valuable insights into the variability of remission patterns among patients. Specifically, individuals aged 25-44 years, those with a history of more than five pre-treatment seizures, and patients with focal epilepsy experienced significantly longer remission times. In contrast, patients who adhered well to their treatment regimens achieved remission more quickly than other groups.
癫痫仍然是一个重大的全球健康问题，其患病率和发病率不断上升。本研究旨在使用参数共享脆弱模型，对埃塞俄比亚吉姆马大学医学中心癫痫患者首次缓解的时间进行建模。对2018年1月1日至2023年12月31日期间接受治疗的癫痫患者进行了一项回顾性研究。所有患者在入组时均接受抗癫痫药物（ASMs）治疗。此外，12%的队列在入组前在吉姆马大学医学中心以外接受过ASM治疗。将对数逻辑、对数正态和威布尔基线风险函数与伽马和逆高斯脆弱分布相结合，以对首次缓解时间进行建模。模型选择基于赤池信息准则（AIC）。患者首次癫痫缓解的中位时间为38个月，45.5%（95%CI：40.9-50.1%）的患者实现了缓解。通过对数正态-逆高斯共享脆弱模型模拟的不同地区缓解时间的变异性估计为θ = 0.454。年龄在25-44岁的患者（加速因子1.13 [95%CI 1.04-1.23]，p = 0.005）、治疗前癫痫发作超过五次的患者（加速因子1.08 [95%CI 1.02-1.15，p = 0.018]）以及局灶性癫痫患者（加速因子1.15 [95%CI 1.07-1.25，p = 0.003]）与其他患者组相比，缓解时间显著更长，而治疗依从性良好的患者（加速因子0.88 [95%CI 0.81-0.96，p = 0.005]）与治疗依从性差的患者相比，缓解时间显著更短。对数正态-逆高斯共享脆弱模型为了解患者缓解模式的变异性提供了有价值的见解。具体而言，年龄在25-44岁的个体、治疗前癫痫发作超过五次的个体以及局灶性癫痫患者的缓解时间显著更长。相比之下，治疗方案依从性良好的患者比其他组更快实现缓解。
