Article Text

This article has a correction. Please see:

Extended report
Predicting the outcome of ankylosing spondylitis therapy
  1. Nathan Vastesaeger1,
  2. Désirée van der Heijde2,
  3. Robert D Inman3,
  4. Yanxin Wang1,
  5. Atul Deodhar4,
  6. Benjamin Hsu5,
  7. Mahboob U Rahman6,
  8. Ben Dijkmans7,
  9. Piet Geusens8,
  10. Bert Vander Cruyssen9,
  11. Eduardo Collantes10,
  12. Joachim Sieper11,
  13. Jürgen Braun12
  1. 1Schering-Plough Corporation, Kenilworth, New Jersey, USA (Currently Merck, Whitehouse Station, New Jersey, USA)
  2. 2Leiden University Medical Center, Leiden, The Netherlands
  3. 3University of Toronto, Toronto, Canada
  4. 4Oregon Health and Science University, Portland, Oregon, USA
  5. 5Centocor Research & Development, Malvern, Pennsylvania, USA
  6. 6Formerly at Centocor Research & Development, Malvern, Pennsylvania, USA (Currently Pfizer, Collegeville, Pennsylvania, USA)
  7. 7VU Medical Centre, Amsterdam, The Netherlands
  8. 8University Hasselt, Hasselt, Belgium
  9. 9Ghent University Hospital, Ghent, Belgium
  10. 10Reina Sofia Hospital and University Cordoba, Cordoba, Spain
  11. 11Charite Hospital Berlin, Berlin, Germany
  12. 12Rheumazentrum Ruhrgebiet, Herne, Germany
  1. Correspondence to Dr Nathan Vastesaeger, Allewaertstraat 13, 2000 Antwerpen, Belgium; nathan.vastesaeger{at}


Objectives To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS).

Methods ASSERT and GO–RAISE trial data (n=635) were analysed to identify baseline predictors for various disease-state and disease-activity outcome instruments in AS. Univariate, multivariate, receiver operator characteristic and correlation analyses were performed to select final predictors. Their associations with outcomes were explored. Matrix and algorithm-based prediction models were created using logistic and linear regression, and their accuracies were compared. Numbers needed to treat were calculated to compare the effect size of anti-TNF therapy between the AS matrix subpopulations. Data from registry populations were applied to study how a daily practice AS population is distributed over the prediction model.

Results Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were identified as predictors. Their associations with each outcome instrument varied. However, the combination of these factors enabled adequate prediction of each outcome studied. The matrix model predicted outcomes as well as algorithm-based models and enabled direct comparison of the effect size of anti-TNF treatment outcome in various subpopulations. The trial populations reflected the daily practice AS population.

Conclusion Age, BASFI, enthesitis, therapy, CRP and HLA-B27 were associated with outcomes in AS. Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations. This may help guide clinicians in making treatment decisions in daily practice.

This paper is freely available online under the BMJ Journals unlocked scheme, see

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Supplementary materials


  • Funding BVC is a postdoctoral researcher supported by the FWO Flanders.

  • Competing interests None.

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

Linked Articles

  • Miscellaneous
    BMJ Publishing Group Ltd and European League Against Rheumatism