Identification of Patients with Drug-resistant Epilepsy in Electronic Medical Record Data Using the Observational Medical Outcomes Partnership Common Data Model
Abstract number :
2.263
Submission category :
7. Anti-seizure Medications / 7E. Other
Year :
2022
Submission ID :
2204621
Source :
www.aesnet.org
Presentation date :
12/4/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:25 AM
Authors :
Genna Waldman, MD – University of Pennsylvania ; Victor Castano, BA – Medical Student, Neurological Surgery, Columbia University; Matthew Spotnitz, MD MPH – Biomedical Informatics – Columbia University; Evan Joiner, MD – Neurological Surgery – Columbia University; Hyumni Choi, MD MS – Neurology – Columbia University; Anna Ostropolets, MD – Biomedical Informatics – Columbia University; Karthik Natarajan, PhD – Biomedical Informatics – Columbia University; Guy McKhann, MD – Neurological Surgery – Columbia University; Ruth Ottman, PhD – Neurology – Columbia University; Al Neugut, MD PhD – Epidemiology – Columbia University; George Hripcsak, MD MS – Biomedical Informatics – Columbia University; Brett Youngerman, MD MS – Neurological Surgery – Columbia University
Rationale: Over a third of appropriately treated patients with epilepsy fail 2 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 phenotype algorithms for DRE using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
Methods: We randomly sampled 600 patients from our academic medical center’s electronic health record derived OMOP database meeting previously validated criteria for epilepsy (1/2015-7/2021) (Spotnitz M, et al. Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database. Epilepsy & Behavior. 2022). Two reviewers manually classified patients as having DRE, drug-responsive epilepsy, undefined drug responsiveness, or not epilepsy as of the last EHR encounter in the study period based on consensus definitions. Demographic characteristics and codes for diagnoses, antiseizure medication (ASM), 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.
Results: Among 412 patients with source-record confirmed epilepsy, 62 (15.0%) had DRE, 163 (39.6%) drug-responsive epilepsy, 124 (30.0%) undefined drug responsiveness, and 63 (15.3%) insufficient records (Figure 1). Thirty possible DRE phenotypes were constructed with various permutations of the OMOP CDM concepts using algorithmic rules-based criteria. 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 0.661, specificity 0.937, PPV 0.594, NPV 0.952, F1-score 0.626). Several phenotypes achieved higher sensitivity at the expense of specificity and vice versa (Figure 2).
Conclusions: CDM algorithms can identify DRE in EHR-derived data with varying tradeoffs between sensitivity and specificity. These DRE definitions can be applied across an international network of standardized databases for further validation, reproducible observational research, and improving access to appropriate care.
Funding: Dr Brett Youngerman: KL2 Mentored Career Development Award
Anti-seizure Medications