Objective:To assess 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 and >2 distinct antiseizure medications (ASMs) from 2014-2020 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 employing a rubric based in 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 (OR 16.9, 95% CI 8.8-32.2), >2 ASMs in the prior two years (OR 13.0, 95% CI 5.1-33.3), >3 non-gabapentinoid 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 to: 1) maximize overall predictiveness (drug-resistant epilepsy diagnosis; sensitivity 0.86, specificity 0.74, area under the receiver operating characteristics curve [AUROC] 0.80), 2) maximize sensitivity (drug-resistant epilepsy diagnosis or >3 ASMs; sensitivity 0.98, specificity 0.47, AUROC 0.72), and 3) maximize specificity (drug-resistant epilepsy diagnosis and >3 non-gabapentinoid ASMs; sensitivity 0.42, specificity 0.98, AUROC 0.70).Conclusions:Our findings provide validation for several claims-based definitions of drug-resistant epilepsy that can be applied to a variety of research questions.
Predicting drug-resistant epilepsy — A machine learning approach based on administrative claims data
