P117 Machine-learning derived characteristics associated with tapering TNF inhibitors in individuals with rheumatoid arthritis

Abstract Background/Aims Tapering of TNF inhibitor (TNFi) drugs may be considered in some patients to reduce risks and costs. Selecting appropriate patients is not always straightforward and may be influenced by age, sex, comorbidity and disease activity state. We sought to identify predictors for d...

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Published in:Rheumatology (Oxford, England) England), 2023-04, Vol.62 (Supplement_2)
Main Authors: Phillips, Thomas, Bhandari, Megha, Stammers, Matthew, Fraser, Simon, George, Michael, Lin, Sharon, Lwin, May, Holroyd, Christopher, Edwards, Christopher
Format: Article
Language:English
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Summary:Abstract Background/Aims Tapering of TNF inhibitor (TNFi) drugs may be considered in some patients to reduce risks and costs. Selecting appropriate patients is not always straightforward and may be influenced by age, sex, comorbidity and disease activity state. We sought to identify predictors for dose tapering in a real-world clinical setting. Algorithmic extraction, selection and analysis of relevant patient sub-cohorts could enable identification of relevant predictors associated with TNFi dose tapering. Methods Our institution has a Rheumatology Biologics database running prospectively for over 15 years. Our approach for patients with RA receiving TNFi has been to dose-taper by one third and then 50% if remission achieved (defined as DAS28
ISSN:1462-0324
1462-0332
DOI:10.1093/rheumatology/kead104.158