Cargando…

Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Kim, Hunmin, Yoo, Sooyoung, Jeon, Yonghoon, Yi, Soyoung, Kim, Seok, Choi, Sun Ah, Hwang, Hee, Kim, Ki Joong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235379/
https://www.ncbi.nlm.nih.gov/pubmed/32477256
http://dx.doi.org/10.3389/fneur.2020.00409
_version_ 1783535954499207168
author Kim, Hunmin
Yoo, Sooyoung
Jeon, Yonghoon
Yi, Soyoung
Kim, Seok
Choi, Sun Ah
Hwang, Hee
Kim, Ki Joong
author_facet Kim, Hunmin
Yoo, Sooyoung
Jeon, Yonghoon
Yi, Soyoung
Kim, Seok
Choi, Sun Ah
Hwang, Hee
Kim, Ki Joong
author_sort Kim, Hunmin
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7235379
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-72353792020-05-29 Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model Kim, Hunmin Yoo, Sooyoung Jeon, Yonghoon Yi, Soyoung Kim, Seok Choi, Sun Ah Hwang, Hee Kim, Ki Joong Front Neurol Neurology 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. Frontiers Media S.A. 2020-05-12 /pmc/articles/PMC7235379/ /pubmed/32477256 http://dx.doi.org/10.3389/fneur.2020.00409 Text en Copyright © 2020 Kim, Yoo, Jeon, Yi, Kim, Choi, Hwang and Kim. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Kim, Hunmin
Yoo, Sooyoung
Jeon, Yonghoon
Yi, Soyoung
Kim, Seok
Choi, Sun Ah
Hwang, Hee
Kim, Ki Joong
Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model
title Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model
title_full Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model
title_fullStr Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model
title_full_unstemmed Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model
title_short Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model
title_sort characterization of anti-seizure medication treatment pathways in pediatric epilepsy using the electronic health record-based common data model
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235379/
https://www.ncbi.nlm.nih.gov/pubmed/32477256
http://dx.doi.org/10.3389/fneur.2020.00409
work_keys_str_mv AT kimhunmin characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel
AT yoosooyoung characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel
AT jeonyonghoon characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel
AT yisoyoung characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel
AT kimseok characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel
AT choisunah characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel
AT hwanghee characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel
AT kimkijoong characterizationofantiseizuremedicationtreatmentpathwaysinpediatricepilepsyusingtheelectronichealthrecordbasedcommondatamodel