Bayesian Non-Homogeneous Hidden Markov Model with Variable Selection for Investigating Drivers of Seizure Risk Cycling

TL;DR

This paper introduces a Bayesian non-homogeneous hidden Markov model with variable selection to analyze seizure risk dynamics, improving prediction accuracy and identifying key clinical factors influencing seizure risk in epilepsy patients.

Contribution

It presents a novel Bayesian model that captures seizure risk states, incorporates variable selection for clinical covariates, and demonstrates clinical utility with real patient data.

Findings

Identifies seizure risk cycling patterns in Dravet syndrome

Validates known pharmacologic effects on seizure risk

Uncovers new insights into risk state volatility

Abstract

A major issue in the clinical management of epilepsy is the unpredictability 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 non-homogeneous 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 significant 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 techniques. 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 Tracker TM 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 characterizing 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.