Treatment patterns of biologic disease-modifying anti-rheumatic drugs in juvenile idiopathic arthritis:a population-based study in Korea
10.4078/jrd.2025.0101
- Author:
Jong Gyun AHN
;
Min-Taek LEE
;
Daye LEE
;
Eun Jeong MIN
;
Dae Chul JEONG
- Publication Type:Original Article
- From:Journal of Rheumatic Diseases
2026;33(2):111-121
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objective:Juvenile idiopathic arthritis (JIA) is the most common chronic arthritis in children, with an unknown cause and prolonged disease course. Biologic disease-modifying anti-rheumatic drugs (bDMARDs) have improved outcomes in severe or refractory cases, but challenges remain due to disease heterogeneity and long-term management needs. Understanding treatment patterns of novel therapies is essential for optimizing care. This study aimed to investigate bDMARD treatment patterns in JIA patients using a real-world database.
Methods:A retrospective analysis was conducted using Health Insurance Review and Assessment (HIRA) national claims data from 2007 to 2019. JIA patients were classified into bDMARDs and non-bDMARDs groups, with treatment patterns compared.
Results:Among 1,728 JIA patients, 31.3% (n=541) received bDMARDs, with 15.5% (n=84) discontinuing treatment. The median time from diagnosis to first bDMARD was 36.7 (9.7~58.1) months, with a median treatment duration of 28.1 (11.5~54.4) months and follow-up of 24.3 (7.7~43.7) months post-discontinuation. In the non-bDMARDs group (68.7%, n=1,187), 68.7% (n=815) used conventional synthetic DMARDs (csDMARDs), with a 46.6% (n=380) discontinuation rate. Methotrexate, sulfasalazine, and hydroxychloroquine were the most prescribed csDMARDs. Etanercept (69.7%) was the most common first bDMARD, followed by adalimumab (19.0%) and tocilizumab (7.6%), with 14.8% switching to a second biologic. Discontinuation rates of csDMARDs were higher in the non-bDMARDs group (p<0.001).
Conclusion:This is the first comprehensive study profiling the treatment patterns of Korean patients with JIA using populationbased claims data. The findings enhance understanding of real-world treatment trends, aiding clinical decision-making.
