ArticleTherapeutic advances in musculoskeletal disease2022
Predictive model to identify multiple failure to biological therapy in patients with rheumatoid arthritis.
Article in Therapeutic advances in musculoskeletal disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Early identification of difficult-to-manage axial spondyloarthritis using machine-learning decision trees.Therapeutic advances in musculoskeletal disease · 2026Article
- Early-onset difficult-to-treat rheumatoid arthritis: proposal of data-driven predictors and temporal threshold.Arthritis research & therapy · 2025Observational
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
- Gut dysbiosis is associated with difficult-to-treat rheumatoid arthritis.Frontiers in medicine · 2024Article
- A Model to Predict Future Biologic or Targeted Synthetic DMARD Switch at a Subsequent Clinic Visit in Rheumatoid Arthritis.Rheumatology and therapy · 2023Article
- Re-thinking the perception of long-term opioid use in RMDs.Nature reviews. Rheumatology · 2023Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Despite advances in the treatment of rheumatoid arthritis (RA) and the wide range of therapies available, there is a percentage of patients whose treatment presents a challenge for clinicians due to lack of response to multiple biologic and target-specific disease-modifying antirheumatic drugs (b/tsDMARDs). Objective: To develop and validate an algorithm to predict multiple failure to biological therapy in patients with RA. Design: Observational retrospective study involving subjects from a cohort of patients with RA receiving b/tsDMARDs. Methods: Based on the number of prior failures to b/tsDMARDs, patients were classified as either multi-refractory (MR) or non-refractory (NR). Patient characteristics were considered in the statistical analysis to design the predictive model, selecting those variables with a predictive capability. A decision algorithm known as 'classification and regression tree' (CART) was developed to create a prediction model of multi-drug resistance. Performance of the prediction algorithm was evaluated in an external independent cohort using area under the curve (AUC). Results: A total of 136 patients were included: 51 MR and 85 NR. The CART model was able to predict multiple failures to b/tsDMARDs using disease activity score-28 (DAS-28) values at 6 months after the start time of the initial b/tsDMARD, as well as DAS-28 improvement in the first 6 months and baseline DAS-28. The CART model showed a capability to correctly classify 94.1% Conclusion: Our model correctly classified
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