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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11960

Title: Predicting the outcome of ankylosing spondylitis therapy
Authors: Vastesaeger, Nathan
Inman, Robert D.
Wang, Yanxin
Deodhar, Atul
Hsu, Benjamin
Rahman, Mahboob U.
Dijkmans, Ben
Vander Cruyssen, Bert
Collantes, Eduardo
Sieper, Joachim
Braun, Juergen
Issue Date: 2011
Citation: ANNALS OF THE RHEUMATIC DISEASES, 70 (6). p. 973-981
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.
Notes: [Vastesaeger, Nathan; Wang, Yanxin] Schering Plough Corp, Kenilworth, NJ 07033 USA. [van der Heijde, Desiree] Leiden Univ, Med Ctr, Leiden, Netherlands. [Inman, Robert D.] Univ Toronto, Toronto, ON, Canada. [Deodhar, Atul] Oregon Hlth & Sci Univ, Portland, OR 97201 USA. [Hsu, Benjamin; Rahman, Mahboob U.] Centocor Res & Dev, Malvern, PA USA. [Dijkmans, Ben] Vrije Univ Amsterdam Med Ctr, Amsterdam, Netherlands. [Geusens, Piet] Univ Hasselt, Hasselt, Belgium. [Vander Cruyssen, Bert] Ghent Univ Hosp, B-9000 Ghent, Belgium. [Collantes, Eduardo] Reina Sofia Hosp, Cordoba, Spain. [Collantes, Eduardo] Univ Cordoba, Cordoba, Spain. [Sieper, Joachim] Charite Hosp Berlin, Berlin, Germany. [Braun, Juergen] Rheumazentrum Ruhrgebiet, Herne, Germany. nathan.vastesaeger@merck.com
URI: http://hdl.handle.net/1942/11960
DOI: 10.1136/ard.2010.147744
ISI #: 000290149900016
ISSN: 0003-4967
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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