ArticleRMD open2026
Predicting the progression of difficult-to-treat rheumatoid arthritis by a machine learning scoring system, from the FIRST registry.
Article in RMD open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Article
- Difficult-to-Treat Rheumatoid Arthritis in Saudi Arabia: Clinical Characteristics and Associated Factors in a Multicentre Study.Open access rheumatology : research and reviews · 2026Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop and validate a prediction model for the future progression of difficult-to-treat rheumatoid arthritis (D2T RA) and support the precise use of biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs).
methodsData were analysed from 1221 patients with rheumatoid arthritis in the FIRST registry, a multicentre collaborative database, who initiated their first b/tsDMARD. 25 prediction models for D2T RA were generated using 43 baseline characteristics prior to initiating the first b/tsDMARD. Model performances were tested. A scoring system based on the selected model was designed and internally validated for use in routine clinical practice.
resultsAmong the 1221 patients, 193 (15.8%) progressed to D2T RA after a median of 54.0 months. These patients had higher tender joint count, global assessment, pain scale, Health Assessment Questionnaire score and Clinical Disease Activity Index score and more frequent coexisting lung disease. Among the machine learning models tested, Lasso logistic regression performed best (area under the curve 0.71), identifying pain scale, erythrocyte sedimentation rate and coexisting lung disease as key predictors. The scoring system based on these predictors demonstrated comparable performance. Among patients identified as high risk for D2T RA by the scoring system, interleukin-6 receptor inhibitors (IL-6Ri) significantly reduced D2T RA progression (relative risk (RR): 0.48).
conclusionThis study developed and validated a scoring system to predict D2T RA progression and identify patients at high risk and suggested the advantages of IL-6Ri. This system may advance precision medicine in RA and may contribute to controlling unmet needs.
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