Definitions of Drug-Resistant Epilepsy for Administrative Claims Data Research
Chloé E. Hill, Chun Chieh Lin, Samuel W. Terman, Subhendu Rath, Jack M. Parent, Lesli E. Skolarus, James Burke
Abstract
BACKGROUND AND OBJECTIVE: To assess the accuracy of definitions of drug-resistant epilepsy applied to administrative claims data. METHODS: We randomly sampled 450 patients from a tertiary health system with ≥1 epilepsy/convulsion encounter, ≥2 distinct antiseizure medications (ASMs) from 2014 to 2020, and ≥2 years of electronic medical records (EMR) data. We established a drug-resistant epilepsy diagnosis at a specific visit by reviewing EMR data and using a rubric based on the 2010 International League Against Epilepsy definition. We performed logistic regressions to assess clinically relevant predictors of drug-resistant epilepsy and to inform claims-based definitions. RESULTS: Of 450 patients reviewed, 150 were excluded for insufficient EMR data. Of the 300 patients included, 98 (33%) met criteria for current drug-resistant epilepsy. The strongest predictors of current drug-resistant epilepsy were drug-resistant epilepsy diagnosis code (odds ratio [OR] 16.9, 95% confidence interval [CI] 8.8-32.2), ≥2 ASMs in the prior 2 years (OR 13.0, 95% CI 5.1-33.3), ≥3 nongabapentinoid ASMs (OR 10.3, 95% CI 5.4-19.6), neurosurgery visit (OR 45.2, 95% CI 5.9-344.3), and epilepsy surgery (OR 30.7, 95% CI 7.1-133.3). We created claims-based drug-resistant epilepsy definitions (1) to maximize overall predictiveness (drug-resistant epilepsy diagnosis; sensitivity 0.86, specificity 0.74, area under the receiver operating characteristics curve [AUROC] 0.80), (2) to maximize sensitivity (drug-resistant epilepsy diagnosis or ≥3 ASMs; sensitivity 0.98, specificity 0.47, AUROC 0.72), and (3) to maximize specificity (drug-resistant epilepsy diagnosis and ≥3 nongabapentinoid ASMs; sensitivity 0.42, specificity 0.98, AUROC 0.70). DISCUSSION: Our findings provide validation for several claims-based definitions of drug-resistant epilepsy that can be applied to a variety of research questions.