ArticleArthritis research & therapy2021
Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon.
Article in Arthritis research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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Who cites it
19 citing papers in PubMed.
- Clinical characterization and healthcare burden of difficult-to-treat inflammatory bowel disease in Latin America: a real-world registry-based study.Crohn's & colitis 360 · 2026Article
- Factors Associated with Difficult-to-Treat Rheumatoid Arthritis (D2T-RA): Real-World Evidence from a Single-Center Cross-Sectional Study.Journal of personalized medicine · 2026Article
- Development and External Validation of a Multivariable Predictive Model for Progression to Difficult-to-Treat Rheumatoid Arthritis in Biologic-Experienced Patients.Arthritis care & research · 2026Article
- Machine learning-assisted screening of clinical features for predicting difficult-to-treat rheumatoid arthritis.Scientific reports · 2025Article
- Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.Rheumatology international · 2025Article
- Prevalence and characteristics of adults with difficult-to-treat rheumatoid arthritis in a large patient registry.Rheumatology (Oxford, England) · 2025Article
- Review
- Obesity and fibromyalgia are associated with Difficult-to-Treat Rheumatoid Arthritis (D2T-RA) independent of age and gender.Arthritis research & therapy · 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
- Unmet Needs and Current Challenges of Rheumatoid Arthritis: Difficult-to-Treat Rheumatoid Arthritis and Late-Onset Rheumatoid Arthritis.Journal of clinical medicine · 2024Review
- A Review of Difficult-to-Treat Rheumatoid Arthritis: Definition, Clinical Presentation, and Management.Current rheumatology reports · 2023Review
- Effectiveness and safety of intravenous golimumab with and without concomitant methotrexate in patients with rheumatoid arthritis in the prospective, noninterventional AWARE study.BMC rheumatology · 2023Article
- Difficult-to-treat rheumatoid arthritis (D2T RA): clinical issues at early stages of disease.RMD open · 2023Article
- Artificial intelligence in rheumatoid arthritis: potential applications and future implications.Frontiers in medicine · 2023Review
- Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- High Throughput Genetic Characterisation of Caucasian Patients Affected by Multi-Drug Resistant Rheumatoid or Psoriatic Arthritis.Journal of personalized medicine · 2022Article
- Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs).Therapeutic advances in musculoskeletal disease · 2022Review
- Difficult-to-treat rheumatoid arthritis: Current concept and unsolved problems.Frontiers in medicine · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data.
methodsRoutine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data.
resultsWe identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82-0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71-0.75)).
conclusionsDuring this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.
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