BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING

• 成果类型: Article
• 署名作者: Wang, Emily T.; Chiang, Sharon; Haneef, Zulfi; Rao, Vikram R.; Moss, Robert; Vannucci, Marina
• 署名单位: Rice University; University of California System; University of California San Francisco; Baylor College of Medicine
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1630
• 发表日期: 2023
• 页码: 333-356
• 关键词: sudden unexpected death; epilepsy; dependence; binary; COUNT

摘要

A major issue in the clinical management of epilepsy is the unpredictabil-ity of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies which are a stochastic measurement of seizure risk. We consider a Bayesian nonhomo-geneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers signifi-cant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation tech-niques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure TrackerTM system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings charac-terizing the presence and volatility of risk states in Dravet syndrome which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.