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

• Published in: Epilepsy & behavior Vol. 129; p. 108630
• Main Authors: Spotnitz, Matthew, Ostropolets, Anna, Castano, Victor G., Natarajan, Karthik, Waldman, Genna J., Argenziano, Michael, Ottman, Ruth, Hripcsak, George, Choi, Hyunmi, Youngerman, Brett E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2022
• Subjects: Antiseizure medication; Databases, Factual; Electronic Health Records; Epilepsy; Epilepsy - diagnosis; Epilepsy - drug therapy; Feasibility Studies; Humans; Levetiracetam; Observational Health Data Science and Informatics (OHDSI); Observational Medical Outcomes Partnership (OMOP); Practice patterns; Observational Health Data Science and Informatics (OHDSI); Antiseizure medication; Practice patterns; DRE; Epilepsy; OHDSI; OMOP; ASM; Observational Medical Outcomes Partnership (OMOP); CDM; EHR
• ISSN: 1525-5050; 1525-5069
• DOI: 10.1016/j.yebeh.2022.108630

•Comparisons of epilepsy treatment pathways are limited by available data standards.•The Common Data Model (CDM) offers an international clinical data sharing standard.•We validated a CDM phenotype to identify epilepsy in electronic health record data.•Levetiracetam replaced phenytoin as the most common first-line agent over time.•Significant variability persisted in first and subsequent antiseizure medication use. Efforts to characterize variability in epilepsy treatment pathways are limited by the large number of possible antiseizure medication (ASM) regimens and sequences, heterogeneity of patients, and challenges of measuring confounding variables and outcomes across institutions. The Observational Health Data Science and Informatics (OHDSI) collaborative is an international data network representing over 1 billion patient records using common data standards. However, few studies have applied OHDSI's Common Data Model (CDM) to the population with epilepsy and none have validated relevant concepts. The goals of this study were to demonstrate the feasibility of characterizing adult patients with epilepsy and ASM treatment pathways using the CDM in an electronic health record (EHR)-derived database. We validated a phenotype algorithm for epilepsy in adults using the CDM in an EHR-derived database (2001–2020) against source records and a prospectively maintained database of patients with confirmed epilepsy. We obtained the frequency of all antecedent conditions and procedures for patients meeting the epilepsy phenotype criteria and characterized ASM exposure sequences over time and by age and sex. The phenotype algorithm identified epilepsy with 73.0–85.0% positive predictive value and 86.3% sensitivity. Many patients had neurologic conditions and diagnoses antecedent to meeting epilepsy criteria. Levetiracetam incrementally replaced phenytoin as the most common first-line agent, but significant heterogeneity remained, particularly in second-line and subsequent agents. Drug sequences included up to 8 unique ingredients and a total of 1,235 unique pathways were observed. Despite the availability of additional ASMs in the last 2 decades and accumulated guidelines and evidence, ASM use varies significantly in practice, particularly for second-line and subsequent agents. Multi-center OHDSI studies have the potential to better characterize the full extent of variability and support observational comparative effectiveness research, but additional work is needed to validate covariates and outcomes.