Detection of novel drug-adverse drug reaction signals in rheumatoid arthritis and ankylosing spondylitis: analysis of Korean real-world biologics registry data.
Mi-Hye KwonChung-Il JoungHyunah ShinC C LeeYoung Soo SongYung Jin LeeSeong Hui KangJong-Yeup KimSuehyun LeePublished in: Scientific reports (2024)
This study aimed to detect signals of adverse drug reactions (ADRs) associated with biological disease-modifying antirheumatic drugs (DMARDs) and targeted therapies in rheumatoid arthritis (RA) and ankylosing spondylitis (AS) patients. Utilizing the KOrean College of Rheumatology BIOlogics & Targeted Therapy Registry (KOBIO) data, we calculated relative risks, excluded previously reported drug-ADR pairs, and externally validated remaining pairs using US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and single centre's electronic health records (EHR) data. Analyzing data from 2279 RA and 1940 AS patients, we identified 35 significant drug-ADR pairs in RA and 26 in AS, previously unreported in drug labels. Among the novel drug-ADR pairs from KOBIO, 15 were also significant in the FAERS data. Additionally, 2 significant drug-laboratory abnormality pairs were found in RA using CDM MetaLAB analysis. Our findings contribute to the identification of 14 novel drug-ADR signals, expanding our understanding of potential adverse effects related to biological DMARDs and targeted therapies in RA and AS. These results emphasize the importance of ongoing pharmacovigilance for patient safety and optimal therapeutic interventions.
Keyphrases
- adverse drug
- electronic health record
- rheumatoid arthritis
- ankylosing spondylitis
- disease activity
- clinical decision support
- rheumatoid arthritis patients
- patient safety
- end stage renal disease
- systemic lupus erythematosus
- ejection fraction
- drug induced
- interstitial lung disease
- newly diagnosed
- chronic kidney disease
- climate change
- risk assessment
- big data
- peritoneal dialysis
- quality improvement
- prognostic factors
- physical activity
- machine learning
- data analysis
- juvenile idiopathic arthritis
- artificial intelligence
- drug administration
- systemic sclerosis
- human health
- quantum dots
- real time pcr