Trial reportRheumatology (Oxford, England)2016
Prediction of remission and low disease activity in disease-modifying anti-rheumatic drug-refractory patients with rheumatoid arthritis treated with golimumab.
Trial report in Rheumatology (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed.
- Factors associated with successful discontinuation of certolizumab pegol in early rheumatoid arthritis.International journal of rheumatic diseases · 2020Trial
- Regional differences in baseline disease activity and remission rates following golimumab treatment for RA: results from the GO-MORE trial.Clinical rheumatology · 2018Trial
- A Machine Learning Approach for Prediction of CDAI Remission with TNF Inhibitors: A Concept of Precision Medicine from the FIRST Registry.Rheumatology and therapy · 2024Article
- Clinical prediction models of rheumatoid arthritis and its complications: focus on cardiovascular disease and interstitial lung disease.Arthritis research & therapy · 2023Review
- The role of comorbidities alongside patient and disease characteristics in long-term disease activity in RA using UK inception cohort data.Rheumatology (Oxford, England) · 2022Article
- Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics.Arthritis research & therapy · 2021Article
- Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis.Arthritis & rheumatology (Hoboken, N.J.) · 2021Article
- Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis.Frontiers in immunology · 2021Article
- Toward Overcoming Treatment Failure in Rheumatoid Arthritis.Frontiers in immunology · 2021Review
- Using the Immunophenotype to Predict Response to Biologic Drugs in Rheumatoid Arthritis.Journal of personalized medicine · 2019Review
- Effectiveness, Tolerability, and Safety of Tofacitinib in Rheumatoid Arthritis: A Retrospective Analysis of Real-World Data from the St. Gallen and Aarau Cohorts.Journal of clinical medicine · 2019Article
- Tumor necrosis factor-α blockade treatment decreased CD154 (CD40-ligand) expression in rheumatoid arthritis.PloS one · 2017Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo create a tool to predict probability of remission and low disease activity (LDA) in patients with RA being considered for anti-TNF treatment in clinical practice.
methodsWe analysed data from GO-MORE, an open-label, multinational, prospective study in biologic-naïve patients with active RA (DAS28-ESR ⩾3.2) despite DMARD therapy. Patients received 50 mg s.c. golimumab (GLM) once monthly for 6 months. In secondary analyses, regression models were used to determine the best set of baseline factors to predict remission (DAS28-ESR <2.6) at month 6 and LDA (DAS28-ESR ⩽3.2) at month 1.
resultsIn 3280 efficacy-evaluable patients, of 12 factors included in initial regression models predicting remission or LDA, six were retained in final multivariable models. Greater likelihood of LDA and remission was associated with being male; younger age; lower HAQ, ESR (or CRP) and tender joint count (or swollen joint count) scores; and absence of comorbidities. In models predicting 1-, 3- and 6-month LDA or remission, area under the receiver operating curve was 0.648-0.809 (R(2) = 0.0397-0.1078). The models also predicted 6-month HAQ and EuroQoL-5-dimension scores. A series of matrices were developed to easily show predicted rates of remission and LDA.
conclusionA matrix tool was developed to show predicted GLM treatment outcomes in patients with RA, based on a combination of six baseline characteristics. The tool could help provide practical guidance in selection of candidates for anti-TNF therapy.
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