Predicting the outcome of ankylosing spondylitis therapy

Nathan Vastesaeger, Désirée Van Der Heijde, Robert D. Inman, Yanxin Wang, Atul Deodhar, Benjamin Hsu, Mahboob U. Rahman, Ben Dijkmans, Piet Geusens, Bert Vander Cruyssen, Eduardo Collantes, Joachim Sieper, Jürgen Braun

Research output: Contribution to journalArticle

119 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)973-981
Number of pages9
JournalAnnals of the rheumatic diseases
Volume70
Issue number6
DOIs
StatePublished - Jun 2011

ASJC Scopus subject areas

  • Immunology and Allergy
  • Rheumatology
  • Immunology
  • Biochemistry, Genetics and Molecular Biology(all)

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    Vastesaeger, N., Van Der Heijde, D., Inman, R. D., Wang, Y., Deodhar, A., Hsu, B., Rahman, M. U., Dijkmans, B., Geusens, P., Cruyssen, B. V., Collantes, E., Sieper, J., & Braun, J. (2011). Predicting the outcome of ankylosing spondylitis therapy. Annals of the rheumatic diseases, 70(6), 973-981. https://doi.org/10.1136/ard.2010.147744