ArticleRheumatology (Oxford, England)2023
Machine learning identifies a profile of inadequate responder to methotrexate in rheumatoid arthritis.
Article in Rheumatology (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 27 citations in OpenAlex.
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
- Multi-cohort Analysis Reveals Microbiome Signatures Associated with Drug Response in New-Onset Rheumatoid Arthritis.bioRxiv : the preprint server for biology · 2026Article
- Nitazoxanide alleviates collagen-induced arthritis by inhibiting STAT3-dependent Th17 cell differentiation and inflammatory cytokines production.Frontiers in pharmacology · 2026Article
- Observational
- Artificial Intelligence in Rheumatology: Quo Vadis?Mediterranean journal of rheumatology · 2025Article
- Association ofPharmaceuticals (Basel, Switzerland) · 2025Article
- Common genetic variants do not impact clinical prediction of methotrexate treatment outcomes in early rheumatoid arthritis.Journal of internal medicine · 2025Article
- Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.Rheumatology international · 2025Article
- Predicting abatacept retention using machine learning.Arthritis research & therapy · 2025Observational
- Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study.Frontiers in medicine · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Article
- Unmet Needs and Current Challenges of Rheumatoid Arthritis: Difficult-to-Treat Rheumatoid Arthritis and Late-Onset Rheumatoid Arthritis.Journal of clinical medicine · 2024Review
- Is rheumatoid arthritis always preceded by a symptomatic at-risk phase of arthralgia?RMD open · 2024Article
- On difficulties to define prognostic factors for clinical practice in rheumatoid arthritis.RMD open · 2024Review
- [Rheumatological care in Germany : Memorandum of the German Society for Rheumatology and Clinical Immunology 2024].Zeitschrift fur Rheumatologie · 2024Article
- Advancing Rheumatology Care Through Machine Learning.Pharmaceutical medicine · 2024Article
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
- Predictors of Inadequate Serum Urate Response to Low-Dose Febuxostat in Male Patients with Gout.Journal of inflammation research · 2024Article
- Development of machine learning models for detection of vision threatening Behçet's disease (BD) using Egyptian College of Rheumatology (ECR)-BD cohort.BMC medical informatics and decision making · 2023Article
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Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAround 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers.
methodsModels were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (± 6 months). Several models were compared on the training set using the AUROC. The best model was evaluated on an external validation cohort (tREACH). The model's predictions were explained using Shapley values to extract a biomarker of inadequate response.
resultsWe included 493 therapeutic sequences from ESPOIR, 239 from EAC and 138 from tREACH. The model selected DAS28, Lymphocytes, Creatininemia, Leucocytes, AST, ALT, swollen joint count and corticosteroid co-treatment as predictors. The model reached an AUROC of 0.72 [95% CI (0.63, 0.80)] on the external validation set, where 70% of patients were responders to MTX. Patients predicted as inadequate responders had only 38% [95% CI (20%, 58%)] chance to respond and using the algorithm to decide to initiate MTX would decrease inadequate-response rate from 30% to 23% [95% CI: (17%, 29%)]. A biomarker was identified in patients with moderate or high activity (DAS28 > 3.2): patients with a lymphocyte count superior to 2000 cells/mm3 are significantly less likely to respond.
conclusionOur study highlights the usefulness of machine learning in unveiling subgroups of inadequate responders to MTX to guide new therapeutic strategies. Further work is needed to validate this approach.
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