ArticleFrontiers in neurology2020
Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model.
Article in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Monitoring of dual-drug combination therapy in pediatric epilepsy patients: a machine learning model for simultaneous VPA-LEV concentration-dose prediction.Journal of translational medicine · 2026Article
- Utility of Treatment Pattern Analysis Using a Common Data Model: A Scoping Review.Healthcare informatics research · 2025Article
- Clinical Outcomes and Systemic Complications Related to the Severity and Etiology of Status Epilepticus Using a Common Data Model.Neurocritical care · 2024Article
- Epilepsy in Asian countries.Acta epileptologica · 2023Review
- Effectiveness of the Use of Standardized Vocabularies on Epilepsy Patient Cohort Generation.Healthcare informatics research · 2022Article
- Real-world treatment intensities and pathways of macular edema following retinal vein occlusion in Korea from Common Data Model in ophthalmology.Scientific reports · 2022Article
- Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting.Clinical and experimental pediatrics · 2022Article
- Drug-drug interactions in atrial fibrillation patients receiving direct oral anticoagulants.Scientific reports · 2021Article
- Treatment Patterns of Type 2 Diabetes Assessed Using a Common Data Model Based on Electronic Health Records of 2000-2019.Journal of Korean medical science · 2021Article
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8 authors at 3 institutions in 1 country.
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Abstract
The purpose of this pilot study was to analyze treatment pathways of pediatric epilepsy using the common data model (CDM) based on electronic health record (EHR) data. We also aimed to reveal whether CDM analysis was feasible and applicable to epilepsy research. We analyzed the treatment pathways of pediatric epilepsy patients from our institute who underwent antiseizure medication (ASM) treatment for at least 2 years, using the Observational Medical Outcomes Partnership (OMOP)-CDM. Subgroup analysis was performed for generalized or focal epilepsy, varying age of epilepsy onset, and specific epilepsy syndromes. Changes in annual prescription patterns were also analyzed to reveal the different trends. We also calculated the proportion of drug-resistant epilepsy by applying the definition of seizure persistence after application of two ASMs for a sufficient period of time (more than 6 months). We identified 1,192 patients who underwent treatment for more than 2 years (mean ± standard deviation: 6.5 ± 3.2 years). In our pediatric epilepsy cohort, we identified 313 different treatment pathways. Drug resistance, calculated as the application of more than three ASMs during the first 2 years of treatment, was 23.8%. Treatment pathways and ASM resistance differed between subgroups of generalized vs. focal epilepsy, different onset age of epilepsy, and specific epilepsy syndromes. The frequency of ASM prescription was similar between onset groups of different ages; however, phenobarbital was frequently used in children with epilepsy onset < 4 years. Ninety-one of 344 cases of generalized epilepsy and 187 of 835 cases of focal epilepsy were classified as medically intractable epilepsy. The percentage of drug resistance was markedly different depending on the specific electro-clinical epilepsy syndrome [79.0% for Lennox-Gastaut syndrome (LGS), 7.1% for childhood absence epilepsy (CAE), and 9.0% for benign epilepsy with centrotemporal spikes (BECTS)]. We could visualize the annual trend and changes of ASM prescription for pediatric epilepsy in our institute from 2004 to 2017. We revealed that CDM analysis was feasible and applicable for epilepsy research. The strengths and limitations of CDM analysis should be carefully considered when planning the analysis, result extraction, and interpretation of results.
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