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Analysis of treatment pattern of anti-dementia medications in newly diagnosed Alzheimer’s dementia using OMOP CDM

Authors :
JungHyun Byun
Dong Yun Lee
Chang-Won Jeong
Yerim Kim
Hak Young Rhee
Ki Won Moon
Jeongwon Heo
Yoonki Hong
Woo Jin Kim
Seung-Joo Nam
Hoon Sung Choi
Ji In Park
In Kook Chun
So Hyeon Bak
Kyoungyul Lee
Gi Hwan Byeon
Kyoung Lae Kim
Jeong-Ah Kim
Young Joo Park
Jeong Hyun Kim
Eun ju Lee
Sang-Ah Lee
Sung Ok Kwon
Sang-Won Park
Payam Hosseinzadeh Kasani
Jung-Kyeom Kim
Yeshin Kim
Seongheon Kim
Jae-Won Jang
Source :
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

Abstract Anti-dementia medications are widely prescribed to patients with Alzheimer’s dementia (AD) in South Korea. This study investigated the pattern of medical management in newly diagnosed patients with AD using a standardized data format—the Observational Medical Outcome Partnership Common Data Model from five hospitals. We examined the anti-dementia treatment patterns from datasets that comprise > 5 million patients during 2009–2019. The medication utility information was analyzed with respect to treatment trends and persistence across 11 years. Among the 8653 patients with newly diagnosed AD, donepezil was the most commonly prescribed anti-dementia medication (4218; 48.75%), followed by memantine (1565; 18.09%), rivastigmine (1777; 8.98%), and galantamine (494; 5.71%). The rising prescription trend during observation period was found only with donepezil. The treatment pathways for the three cholinesterase inhibitors combined with N-methyl-d-aspartate receptor antagonist were different according to the drugs (19.6%; donepezil; 28.1%; rivastigmine, and 17.2%; galantamine). A 12-month persistence analysis showed values of approximately 50% for donepezil and memantine and approximately 40% for rivastigmine and galantamine. There were differences in the prescribing pattern and persistence among anti-dementia medications from database using the Observational Medical Outcome Partnership Common Data Model on the Federated E-health Big Data for Evidence Renovation Network platform in Korea.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.bccdb24f8f0d4b98b5c6497fa018013b
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-022-08595-1