Mixed Methods Framework to Facilitate Clinical Phenotyping and Surveillance of Status Epilepticus - Project Summary Status epilepticus (SE) is a neurologic emergency associated with high risk of neurologic decline and readmission. Mortality, length of stay, and cost all increase when patients in SE progress to refractory status epilepticus (RSE). SE is clinically heterogenous and broadly defined, which is a barrier to conducting randomized trials and contributes to pervasive diagnostic delays and treatment variability. Further, rare subtypes of SE, such as New-onset refractory status epilepticus (NORSE) remain poorly understood. Case definitions that are extractible from the electronic health record (EHR) are necessary for a population-level approach to surveillance of SE, including NORSE, aimed at identifying high risk groups and associated conditions and exposures, supporting early diagnosis, determining incidence, establishing natural history and targeting of therapies. EHR and administrative case definitions for SE do not exist, and current methods of identifying patients with SE using only structured EHR data are prone to bias. In general, prediction models using only structured data often have limited utility. To our knowledge, our proposed project is the first attempt at large-scale multidimensional phenotyping for SE using unstructured data. We hypothesize that generating consensus around the spectrum of clinical phenotypes of SE and using Natural Language Processing (NLP), to identify and classify SE is an essential first step for the creation of SE registries and comparative effectiveness and pragmatic trials of RSE prevention. In Aim 1, we will apply an innovative sequential mixed methods approach, using (a) a modified Delphi method to establish consensus around labels to identify relevant information elements (“ground truth”) and (b) a discrete choice experiment (DCE) to rank identify time-evolving attributes of SE. This will allow us to study whether attributes are weighted differently during a SE admission, and whether risk trajectories toward developing RSE are identifiable. In Aim 2, we will leverage unstructured EHR data from two large academic centers and apply NLP to develop a standardized data extraction model of symptom dimensions, clinical features, and complex concepts of SE from EHRs. Such a model could then categorize SE by clinical outcomes, specifically RSE. Such a tool lays an essential foundation for future comparative effectiveness and pragmatic trials of potentially modifiable preventive factors of RSE, leading to the development of clinical decision support tools, quality metrics, and performance measures for SE and RSE management.
