Identification of patients with drug-resistant epilepsy in electronic medical record data using the Observational...

More than one third of appropriately treated patients with epilepsy have continued seizures despite two or more medication trials, meeting criteria for drug-resistant epilepsy (DRE). Accurate and reliable identification of patients with DRE in observational data would enable large-scale, real-world comparative effectiveness research and improve access to specialized epilepsy care. In the present study, we aim to develop and compare the performance of computable phenotypes for DRE using the Observational Medical Outcomes Partnership (OMOP) Common Data Model.

We randomly sampled 600 patients from our academic medical center's electronic health record (EHR)-derived OMOP database meeting previously validated criteria for epilepsy (January 2015-August 2021). Two reviewers manually classified patients as having DRE, drug-responsive epilepsy, undefined drug responsiveness, or no epilepsy as of the last EHR encounter in the study period based on consensus definitions. Demographic characteristics and codes for diagnoses, antiseizure medications (ASMs), and procedures were tested for association with DRE. Algorithms combining permutations of these factors were applied to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for DRE. The F1 score was used to compare overall performance.

Among 412 patients with source record-confirmed epilepsy, 62 (15.0%) had DRE, 163 (39.6%) had drug-responsive epilepsy, 124 (30.0%) had undefined drug responsiveness, and 63 (15.3%) had insufficient records. The best performing phenotype for DRE in terms of the F1 score was the presence of ≥1 intractable epilepsy code and ≥2 unique non-gabapentinoid ASM exposures each with ≥90-day drug era (sensitivity = .661, specificity = .937, PPV = .594, NPV = .952, F1 score = .626). Several phenotypes achieved higher sensitivity at the expense of specificity and vice versa.

OMOP algorithms can identify DRE in EHR-derived data with varying tradeoffs between sensitivity and specificity. These computable phenotypes can be applied across the largest international network of standardized clinical databases for further validation, reproducible observational research, and improving access to appropriate care.