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SAT0338 Disease Activity and Clinical Response Early in the Course of Treatment PREDICT Long-Term Outcomes in Axial Spondyloarthritis Patients Treated with Certolizumab Pegol
  1. D. van der Heijde1,
  2. A. Deodhar2,
  3. O. Davies3,
  4. T. Nurminen4,
  5. M. Rudwaleit5
  1. 1Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands
  2. 2Oregon Health and Science University, Portland, United States
  3. 3UCB Pharma, Slough, United Kingdom
  4. 4UCB Pharma, Monheim
  5. 5Endokrinologikum Berlin, Berlin, Germany

Abstract

Background Early response to anti-TNF therapy has been shown to be a strong predictor of good long-term outcomes in ankylosing spondylitis (AS).1 However, early identification of patients (pts) who are unlikely to achieve good long-term disease control by anti-TNF therapy has been less well characterized, although identifying such pts may help avoid unnecessary exposure, increase cost-effectiveness and improve the chance of achieving long-term treatment goals.

Objectives To assess the association between disease activity (DA) and clinical response (CR) during the first 12 weeks (wks) of treatment, and attainment/lack of attainment of a treatment target at Wk48 in axial spondyloarthritis (axSpA) pts, including AS and non-radiographic (nr-)axSpA pts, receiving certolizumab pegol (CZP).

Methods The relationship between DA, CR or a combination of both during the first 12 wks of treatment, and inactive disease (ID) or moderate disease (MD) at Wk48, was assessed post hoc using data from the RAPID-axSpA trial (NCT01087762).2 DA state was defined as either ASDAS ID, MD, high (HD) or very high DA (vHD). CR level was defined as ASDAS major improvement (MI), clinically important improvement but not MI (CII), or less than CII (<CII). Analyses are based on all pts randomized to CZP (200mg Q2W and 400mg Q4W doses combined) in the overall axSpA population and also in the AS and nr-axSpA subpopulations. Predictability analyses for a given wk are based on all pts continuing treatment at that wk. For these pts, LOCF was applied for withdrawals before, or missing evaluation at, Wk48.

Results A clear relationship between DA at Wk2 and ID at Wk48 was observed, with 71% (22/31) of pts with ID at Wk2 achieving ID at Wk48, compared with 0% (0/27) of pts with vHD at Wk2 achieving ID at Wk48. This trend was maintained at Wk12, by which point more pts had ID and MD, and fewer had HD and vHD. 68% (34/50) of pts with ID at Wk12 achieved ID at Wk48, and 0% (0/21) of pts with vHD at Wk12 achieved ID at Wk48 (Table A).When MD was selected as a less stringent target, more pts in ID, MD or HD at each time point achieved the target, although still only 14% (3/21) of pts in vHD at Wk12 attained the target at Wk48. Magnitude of CR at Wk12 was also associated with likelihood of attaining ID at Wk48 (albeit to a lesser extent): Of 83/211 (39%) CZP pts that achieved MI at Wk12, 47% (39/83) achieved ID at Wk48 while 19% (12/65) of CII non-responders (<CII) at Wk12 achieved ID at Wk48. If a combination of DA at baseline and CR at Wk12 was considered, it was possible to more selectively identify pts who were unlikely to achieve the treatment target: For example, only 3% (1/30) of pts with vHD at baseline and CR <CII at Wk12 achieved ID at Wk48 (Table B). Similar trends were observed in the AS and nr-axSpA subpopulations.

Conclusions Using a combination of DA state and CR level during the first 12 wks of CZP treatment, it was possible to identify a subset of pts who are unlikely to achieve long-term treatment goals. This approach may enable physicians adopting a treat-to-target strategy to determine early on when to change therapy in pts not responding to CZP.

References

  1. Sieper J. Ann Rheum Dis 2012;71:700-706; 2. Landewé R. Ann Rheum Dis 2014;73:39-47

Acknowledgements The authors acknowledge Costello Medical Consulting for writing and editorial assistance which was funded by UCB Pharma.

Disclosure of Interest D. van der Heijde Consultant for: AbbVie, Amgen, AstraZeneca, Augurex, BMS, Celgene, Centocor, Chugai, Covagen, Daiichi, Eli-Lilly, GSK, Janssen Biologics, Merck, Novartis, Novo-Nordisk, Otsuka, Pfizer, Roche, Sanofi-Aventis, Schering-Plough, UCB Pharma, Vertex, A. Deodhar Grant/research support: UCB Pharma, Abbott, Amgen, Janssen, Novartis, Consultant for: UCB Pharma, Abbott, O. Davies Shareholder of: UCB Pharma, Employee of: UCB Pharma, T. Nurminen Employee of: UCB Pharma, M. Rudwaleit Consultant for: Abbott, BMS, MSD, Pfizer, Roche, UCB Pharma

DOI 10.1136/annrheumdis-2014-eular.1564

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