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Baseline predictors of different types of treatment success in rheumatoid arthritis
  1. Dafne Capelusnik1,2,
  2. Daniel Aletaha1
  1. 1Department of Rheumatology, Medical University of Vienna, Wien, Austria
  2. 2Department of Rheumatology, Instituto de Rehabilitación Psicofísica, CABA, Argentina
  1. Correspondence to Professor Daniel Aletaha, Department of Rheumatology, Medical University of Vienna, Wien 1090, Austria; daniel.aletaha{at}meduniwien.ac.at

Abstract

Objective To perform a comprehensive analysis on predictors of achieving disease activity outcomes by change, response and state measures.

Methods We used data from three rheumatoid arthritis (RA) trials (one for main analysis, two for validation) to analyse the effect of patient and disease characteristics, core set measure and composite indices on the achievement of different outcomes: response outcomes (% of patients achieving a relative response margin); state outcomes (remission or low disease activity, LDA) and change outcomes (numerical change on metric scales).

Results We included patients from the ASPIRE trial (for analysis) and from the ATTRACT and GO-BEFORE trials (for validation). While lower disease activity components at baseline—except acute phase reactants—were associated with achievement of state outcomes (such as LDA by the Simplified Disease Activity Index, SDAI), higher baseline values were associated with change outcomes (such as SDAI absolute change). A multivariate analysis of the identified predictors of state outcomes identified best prediction by a combination of shorter disease duration, male gender and lower disease activity. For prediction of response, no consistently significant predictors were found, again, with exception of C reactive protein, for which higher levels at baseline were associated with better responses.

Conclusion Prediction of treatment success is limited in RA. Particularly in early RA, prediction of state targets can be achieved by lower baseline levels of diseases activity. Gender and disease duration may improve the predictability of state targets. In clinical trials, included populations and choice of outcomes can be coordinated to maximise efficiency from these studies.

  • arthritis
  • rheumatoid
  • outcome assessment
  • health care
  • antirheumatic agents

Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information.

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Data availability statement

All data relevant to the study are included in the article or uploaded as supplemental information.

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Footnotes

  • Handling editor David S Pisetsky

  • Contributors DC contributed to planning of the project, analysis and interpretation of data, drafting the work. DA contributed to project plan and design, interpretation and reporting the data, critical revisions of the work.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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