Trial reportJAMA network open2022
Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis.
Trial report in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Estimating Patient-Specific Relative Benefit of Adding Biologics to Conventional Rheumatoid Arthritis Treatment: An Individual Participant Data Meta-Analysis.JAMA network open · 2023Pooled it
- Early divergence of treatment response trajectories to adalimumab or its biosimilar in active ankylosing spondylitis: consensus clustering analysis of a randomized controlled trial.Frontiers in immunology · 2026Trial
- Clinical Disease Manifestations Associated With Tumor Necrosis Factor Inhibitor Nonresponse in Juvenile Spondyloarthritis.The Journal of rheumatology · 2026Article
- Association between type 2 diabetes and short-term clinical response to TNF inhibitors in patients with ankylosing spondylitis: a propensity score-matched retrospective cohort study.Clinical rheumatology · 2026Article
- Efficacy of Ixekizumab in Radiographic Axial Spondyloarthritis by Baseline C-Reactive Protein Level: A Pooled Analysis of Phase III Trials.Rheumatology and therapy · 2026Article
- Electronic Health Record-Based Machine Learning Model for Predicting Disease Activity in Patients with Rheumatoid Arthritis.Health data science · 2026Article
- Efficacy of Ixekizumab in Chinese Patients with Radiographic Axial Spondyloarthritis by Baseline C-Reactive Protein Level.Rheumatology and therapy · 2025Article
- Limosilactobacillus reuteri prevents progression of ankylosing spondylitis in mice by restoring gut microbiota-metabolism homeostasis.Journal of translational medicine · 2025Article
- Type 1 interferon signature and allograft inflammatory factor-1 contribute to refractoriness to TNF inhibition in ankylosing spondylitis.Nature communications · 2025Article
- Personalized dose reduction strategies for biologic disease-modifying antirheumatic drugs for treating axial spondyloarthritis: a clinical and economic evaluation with predictive modeling.BMC rheumatology · 2025Article
- Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.Rheumatology international · 2025Article
- Tofacitinib Efficacy/Safety in Patients with Ankylosing Spondylitis by Baseline Body Mass Index: A Post Hoc Analysis of Phase 2/3 Trials.Rheumatology and therapy · 2025Article
- Impact of disease manifestations on first biologic drug survival in axial spondyloarthritis: a real-life Canadian study.Rheumatology advances in practice · 2025Article
- Advancing precision medicine in axial spondyloarthritis: insights from multi-omics approaches.Frontiers in medicine · 2025Review
- Biomarkers for successful tapering of a tumor necrosis factor inhibitor in patients with radiographic axial spondyloarthritis: A pilot study.SAGE open medicine · 2025Article
- Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries.Therapeutic advances in musculoskeletal disease · 2025Article
- Real-world use of ixekizumab for axial spondyloarthritis treatment in Spain (ESPADA study).Frontiers in medicine · 2025Article
- Review
- "Long-term MRI findings in Ankylosing spondylitis patients treated with TNF inhibitors for a decade".Rheumatology international · 2024Article
- Identification of necroptosis-related genes in ankylosing spondylitis by bioinformatics and experimental validation.Journal of cellular and molecular medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Tumor necrosis factor inhibitors (TNFis) have revolutionized the management of ankylosing spondylitis (AS); however, the lack of notable clinical responses in approximately one-half of patients suggests important heterogeneity in treatment response. Identifying patients likely to respond or not respond to TNFis could provide opportunities to personalize treatment strategies. Objective: To develop models of the probability of short-term response to TNFi treatment in individual patients with active AS. Design, Setting, and Participants: This is a retrospective cohort study using data of the TNFi group (ie, treatment group) from 10 randomized clinical trials (RCTs) of TNFi treatment among patients with active AS, conducted from 2002 to 2016. Participants were adult patients with active AS who failed nonsteroidal anti-inflammatory drugs. Included RCTs were phase 3 and 4 studies that assessed the efficacy of an originator TNFi at week 12 and/or week 24, either compared with placebo or an antirheumatic drug. The cohort was divided into a training and a testing set. Data analysis was conducted from July 1, 2019, to November 30, 2020. Exposures: All included patients received an originator TNFi for at least 12 weeks. Main Outcomes and Measures: Outcomes included major response and no response based on the change of AS Disease Activity Score at 12 weeks. Machine learning algorithms were applied to estimate the probability of having major response and no response for individual patients. Results: The study included 1899 participants from 10 trials. The training set included 1207 individuals (mean [SD] age, 39 [12] years; 908 [75.2%] men), of whom 407 (33.7%) had major response and 414 (34.3%) had no response. In the reduced logistic regression models, accuracy was 0.74 for major response and 0.75 for no response. The probability of major response increased with higher C-reactive protein (CRP) level, patient global assessment (PGA), and Bath AS Disease Activity Index (BASDAI) question 2 score and decreased with higher body mass index (BMI) and Bath AS Functional Index (BASFI) score. The probability of no response increased with age and BASFI score, and decreased with higher CRP level, BASDAI question 2 score, and PGA. In the testing set (692 participants; mean [SD] age, 38 [11] years; 533 [77.0%] men), models demonstrated moderate to high accuracy. Conclusions and Relevance: In this cohort study, the probability of initial response to TNFi was predicted from baseline variables, which may facilitate personalized treatment decision-making.
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