ArticleArthritis care & research2026
Development and External Validation of a Multivariable Predictive Model for Progression to Difficult-to-Treat Rheumatoid Arthritis in Biologic-Experienced Patients.
Article in Arthritis care & research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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8 authors.
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Abstract
objectiveApproximately 20% of patients with rheumatoid arthritis (RA) cycle through multiple therapies without achieving treatment goals and are classified as having "difficult-to-treat" RA (D2T-RA); however, no risk prediction tools exist to identify which patients are at highest risk. Our aim was to develop and validate a predictive model for progression to D2T-RA among patients with RA.
methodsWe used data from two large independent observational cohorts of patients with RA to develop and externally validate a multivariable prediction model to identify participants at risk of D2T-RA, defined using EULAR 2021 criteria. We developed a multivariable predictive model for D2T-RA using random survival forests in participants treated with their first biologic and/or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) (derivation cohort). We validated the model in a cohort of participants initiating or switching b/tsDMARD therapies.
resultsA total of 700 participants were in the derivation cohort (84% female, mean age 55 years, median follow-up 40 months, 113 [16%] with D2T-RA), and 2,070 participants were included in the validation cohort (79% female, mean age 56 years, median follow-up 8 months, 571 [28%] with D2T-RA). We observed C-index values of 0.643 (95% confidence interval [CI] 0.585-0.698; derivation cohort) and 0.620 (95% CI 0.596-0.643; validation cohort). Calibration measures suggested overall moderate predictive ability. Worsened functional status, pain, fatigue, and global disease activity were consistently top predictors across both cohorts.
conclusionOur model demonstrated moderate discrimination and calibration, highlighting the challenge in accurately predicting D2T-RA outcomes. These findings underscore the need for further research to improve predictive performance, potentially through the incorporation of additional biomarkers.
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