Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability

Abstract

Objective: A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk.

Methods: Using data from SeizureTracker.com, a patient-reported seizure diary tool containing over 1.2 million recorded seizures across 8 years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed-effects hidden Markov model for zero-inflated count data was developed to estimate changes in underlying seizure risk using patient-reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (TSC) were assessed using data from SeizureTracker.com.

Results: EpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to TSC, four underlying seizure risk states were identified. The expected duration of each state was <12 months, providing a data-driven estimate of the amount of time a person with TSC would be expected to remain at the same seizure risk level according to the natural course of epilepsy.

Significance: We propose a novel Bayesian statistical approach for evaluating seizure risk on an individual patient level using patient-reported seizure diaries, which allows for the incorporation of external clinical variables to assess impact on seizure risk. This tool may improve the ability to distinguish true changes in seizure risk from natural variations in seizure frequency in clinical practice. Incorporation of systematic statistical approaches into antiepileptic drug (AED) management may help improve understanding of seizure unpredictability as well as timing of treatment interventions for people with epilepsy.

Keywords: Bayesian inference; Epilepsy; Hidden Markov model; Mixed effects; Natural history; Seizure diary data; Seizure risk; Tuberous sclerosis complex; Zero‐inflated Poisson.

Figure 1

Example of the issue of probabilistic variation in interpreting seizure count data: Monthly seizure diary from a patient with TSC from SeizureTracker.com. In month 6, the patient reported a decrease from 6 (red circle) to 5 (red cross) monthly seizures. However, the standard deviation of monthly seizures was 5.1. Therefore, the decrease from 6 to 5 seizures falls within an expected probabilistic deviation, suggesting it may not be representative of a true improvement in the risk of another seizure. Similarly, the increase from 6 to 8 seizures in month 50, shown in blue, also falls within an expected probabilistic deviation, underscoring the importance of distinguishing between probabilistic variation and true changes in seizure risk.

Figure 2

Validation study using simulated data: Proportion of incorrectly identified underlying seizure risk states, under (dark bars): proposed Bayesian mixed‐effects hidden Markov model EpiSAT, (medium bars) quantiles of pooled group seizure counts (QUANT‐GROUP), and (light bars) quantiles of individual patient‐level seizure counts (QUANT‐PATIENT). Seizure diaries with various levels of dispersion (ϕ), zero‐inflation (p), and seizure emission rates (λ) were tested. Mean error rate and standard error of the mean are shown.

Figure 3

Validation study using simulated data: Seizure frequencies from a sample simulated seizure diary (A), along with the estimated underlying seizure risk states using EpiSAT (B) are shown. In comparison, approaches that estimated seizure risk relying only on observed seizure frequencies demonstrated significantly poorer performance in correctly identifying changes in underlying seizure risk (C–D). Red = true underlying seizure risk state; black = estimated underlying seizure risk state. λ = (1, 10, 50); p = 0.1; φ = 0.2.

Figure 4

Effect of seizure diary unreliability on accuracy in seizure risk estimation. As expected, increasing proportions of missing seizure diary entries led to decreased accuracy in seizure risk estimation.

Figure 5

Application of EpiSAT to tuberous sclerosis complex (TSC) patients from SeizureTracker.com: Distributions of the duration (in months) of each identified underlying seizure risk state in TSC are shown (sojourn distributions). The mean sojourn time was <12 months for all seizure risk states and is also reported, along with the median and percentage of patients with sojourn times of <12 months. SD, standard deviation.