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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.
Spotnitz, Matthew; Ostropolets, Anna; Castano, Victor G; Natarajan, Karthik; Waldman, Genna J; Argenziano, Michael; Ottman, Ruth; Hripcsak, George; Choi, Hyunmi; Youngerman, Brett E.
Afiliação
  • Spotnitz M; Department of Biomedical Informatics, Columbia University Irving Medical Center, United States.
  • Ostropolets A; Department of Biomedical Informatics, Columbia University Irving Medical Center, United States.
  • Castano VG; Department of Neurological Surgery, Columbia University Irving Medical Center, United States.
  • Natarajan K; Department of Biomedical Informatics, Columbia University Irving Medical Center, United States.
  • Waldman GJ; Department of Neurology, Columbia University Irving Medical Center, United States.
  • Argenziano M; Department of Neurological Surgery, Columbia University Irving Medical Center, United States.
  • Ottman R; Department of Neurology, Columbia University Irving Medical Center, United States; The Gertrude H. Sergievsky Center, Columbia University Vagelos College of Physicians and Surgeons, United States; Department of Epidemiology, Mailman School of Public Health, Columbia University Irving Medical Center,
  • Hripcsak G; Department of Biomedical Informatics, Columbia University Irving Medical Center, United States.
  • Choi H; Department of Neurology, Columbia University Irving Medical Center, United States.
  • Youngerman BE; Department of Neurological Surgery, Columbia University Irving Medical Center, United States. Electronic address: bey2103@cumc.columbia.edu.
Epilepsy Behav ; 129: 108630, 2022 04.
Article em En | MEDLINE | ID: mdl-35276502
ABSTRACT

INTRODUCTION:

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.

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. 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.

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Epilepsia / Registros Eletrônicos de Saúde Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: Epilepsy Behav Assunto da revista: CIENCIAS DO COMPORTAMENTO / NEUROLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Epilepsia / Registros Eletrônicos de Saúde Tipo de estudo: Clinical_trials / Diagnostic_studies / Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: Epilepsy Behav Assunto da revista: CIENCIAS DO COMPORTAMENTO / NEUROLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos