Modeling seizures in patients with focal epilepsy

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Abstract With an estimated annual cost of $12.5 billion in the United States, epilepsy affects approximately 3.4 million Americans and carries a lifetime risk of around 3%. The most common form of epilepsy is focal in nature, meaning seizures arise from a restricted part of the brain. It remains a significant challenge to predict who will respond well to treatment despite modern technology and research, largely due to the heterogeneity of focal epilepsy. Existing statistical methods to analyze seizure data do not appropriately address the within- and between-individual variation in epileptic seizures over time. We propose to leverage our access to the Human Epilepsy Project (HEP1 and HEP2), an observational study of 450 patients with focal epilepsy that tracked seizures longitudinally, and our statistical and clinical expertise to develop novel dynamic prediction models for seizure frequency over time. The daily seizure data from HEP show (1) subgroups of individuals with different seizure trajectories and (2) clumping of seizures, in which a patient is more likely to experience subsequent seizures following a seizure episode. Dynamic prediction models have been used successfully in other clinical areas besides epilepsy, but they do not allow for subgroups of trajectories. Similarly, clumping of events has been handled with the Hawkes process in association models where a homogenous population is assumed. Instead, we seek to predict occurrence of events and understand covariate effects on processes where subgroups of trajectories exist. We hypothesize that accounting for subgroups of individual trajectories and clumping of seizures will provide more accurate and precise prediction of seizure outcomes. We plan to develop novel models for prediction of seizure events through the following aims: (i) Develop a Bayesian nonparametric models for dynamic personalized prediction of seizures over time. We will use the HEP1 dataset to predict longitudinal seizure count and occurrence that allows for subgroups of trajectories and will evaluate the methods using HEP2 data, (ii) Develop a novel Dirichlet Process Mixture Hawkes process model for personalized prediction of recurrent event data that will allow for subgroups and clumping of events; and will compare to existing approaches to handle clumping, and (iii) Develop an R package and a shiny application to implement and illustrate the novel methods. The expected outcomes of this work are both clinically and statistically significant: a) a shiny application tool to obtain tailored predictions of longitudinal seizure trajectory based on seizure history, treatment and other clinically relevant covariates that will help patients and clinicians identify optimal treatment earlier in the personalized course of the patient’s disease; and b) the new methods will be relevant to other epilepsy types and other conditions, for example in modeling relapses in multiple sclerosis.