Trial reportArthritis research & therapy2021
Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs.
Trial report 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 31 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Machine learning for predicting treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in rheumatoid arthritis: a scoping review.BMC rheumatology · 2025Pooled it
- Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis.Frontiers in immunology · 2021Trial
- AI-enabled clinical trials.Nature reviews bioengineering · 2026Article
- Artificial Intelligence and Machine Learning in Rheumatology and Systemic Inflammatory Diseases: From Pattern Recognition to Signal Analysis and Clinical Decision Support.Journal of clinical medicine · 2026Review
- Review
- Electronic Health Record-Based Machine Learning Model for Predicting Disease Activity in Patients with Rheumatoid Arthritis.Health data science · 2026Article
- Review
- Observational
- Management and treatment of early rheumatoid arthritis, process and barriers among rheumatologists in Spain: a Delphi consensus.BMC rheumatology · 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
- [Rheumatological care in Germany : Memorandum of the German Society for Rheumatology and Clinical Immunology 2024].Zeitschrift fur Rheumatologie · 2024Article
- Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches.JMIR formative research · 2024Article
- Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.Arthritis care & research · 2024Article
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
- Article
- Rheumatoid Arthritis Has Won the Battle but Not the War: How Many Joints Will We Save Tomorrow?Medicina (Kaunas, Lithuania) · 2023Review
- Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis.Exploratory research in clinical and social pharmacy · 2023Article
- Real-World Sarilumab Use and Rule Testing to Predict Treatment Response in Patients with Rheumatoid Arthritis: Findings from the RISE Registry.Rheumatology and therapy · 2023Article
- A survey of artificial intelligence in rheumatoid arthritis.Rheumatology and immunology research · 2023Article
- The prognostic value of whole-genome DNA methylation in response to Leflunomide in patients with Rheumatoid Arthritis.Frontiers in immunology · 2023Article
Corrections and comments
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Authors and funding
19 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiological disease-modifying anti-rheumatic drugs (bDMARDs) can be tapered in some rheumatoid arthritis (RA) patients in sustained remission. The purpose of this study was to assess the feasibility of building a model to estimate the individual flare probability in RA patients tapering bDMARDs using machine learning methods.
methodsLongitudinal clinical data of RA patients on bDMARDs from a randomized controlled trial of treatment withdrawal (RETRO) were used to build a predictive model to estimate the probability of a flare. Four basic machine learning models were trained, and their predictions were additionally combined to train an ensemble learning method, a stacking meta-classifier model to predict the individual flare probability within 14 weeks after each visit. Prediction performance was estimated using nested cross-validation as the area under the receiver operating curve (AUROC). Predictor importance was estimated using the permutation importance approach.
resultsData of 135 visits from 41 patients were included. A model selection approach based on nested cross-validation was implemented to find the most suitable modeling formalism for the flare prediction task as well as the optimal model hyper-parameters. Moreover, an approach based on stacking different classifiers was successfully applied to create a powerful and flexible prediction model with the final measured AUROC of 0.81 (95%CI 0.73-0.89). The percent dose change of bDMARDs, clinical disease activity (DAS-28 ESR), disease duration, and inflammatory markers were the most important predictors of a flare.
conclusionMachine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.