Abstracts

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

Abstract number : 3.416
Submission category : 7. Anti-seizure Medications / 7E. Other
Year : 2021
Submission ID : 1886498
Source : www.aesnet.org
Presentation date : 12/6/2021 12:00:00 PM
Published date : Nov 22, 2021, 06:56 AM

Authors :
Genna Waldman, MD - Columbia University Irving Medical Center/New York Presbyterian; Matthew Spotnitz, MD PHD - Department of Biomedical Informatics - Columbia University Irving Medical Center; Anna Ostropolets, MD - Department of Biomedical Informatics - Columbia University Irving Medical Center; Victor Castano, BA - Department of Neurological Surgery - Columbia University Irving Medical Center; Karthik Natarajan, PhD - Department of Biomedical Informatics - Columbia University Irving Medical Center; Michael Argenziano, BA - Department of Neurological Surgery - Columbia University Irving Medical Center; Ruth Ottman, PhD - The Gertrude H. Sergievsky Center - Columbia University Vagelos College of Physicians and Surgeons; George Hripcsak, MD MS - Department of Biomedical Informatics - Columbia University Irving Medical Center; Hyumni Choi, MD MS - Department of Neurology - Columbia University Irving Medical Center; Brett Youngerman, MD MS - Department of Neurological Surgery - Columbia University Irving Medical Center

Rationale: 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. The Common Data Model (CDM) is a standard that normalizes the structure and content of observational data across participating sites and databases. Previous OHDSI studies demonstrated the feasibility of characterizing variability in pharmacotherapy treatment pathways at scale across the network for other chronic diseases. Few studies have applied OHDSI's CDM standards to the epilepsy population and none have validated relevant concepts. The goals of this study were to demonstrate the feasibility of characterizing adult epilepsy patients and ASM treatment pathways using the CDM in an electronic health record (EHR)-derived database.

Methods: 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.

Results: The phenotype algorithm identified epilepsy with 73.0-85.0% positive predictive value and 86.3% sensitivity. Prevalence was slightly higher in females, 11,995 (53.3%), than males, 10,479 (46.6%). Many patients had neurologic conditions and diagnoses antecedent to meeting epilepsy criteria. Notable conditions that commonly occurred prior to a diagnosis of epilepsy included dizziness (13.5%), altered mental status (13.1%), depressive disorder (12.8%), syncope (11.0%), and cerebral infarction (10.0%). Common antecedent drug exposures included opioids (31.1%), anxiolytics (26.9%), antidepressants (22.1%), and antipsychotics (15.4%). 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 (Figure 1). 

Conclusions: 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. This study validated an OMOP CDM phenotype algorithm to identify adult epilepsy patients in electronic health record-derived data.

Funding: Please list any funding that was received in support of this abstract.: This work was supported by a KL2 Mentored Career Development Award (KL2TR001874) to Dr. Brett Youngerman.

Anti-seizure Medications