Article Text

AB0239 Personalized Biological Treatment for Rheumatoid Arthritis: A Systematic Review with a Focus on Clinical Applicability
  1. B.V. Cuppen,
  2. P.M. Welsing,
  3. J.J. Sprengers,
  4. J.W. Bijlsma,
  5. A.C. Marijnissen,
  6. J.M. van Laar,
  7. F.P. Lafeber,
  8. S.C. Nair
  1. Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht, Netherlands


Background Biological treatments have dramatically improved the outcome of RA patients. A substantial number of patients, however, fails to clinically respond to these therapies. Prediction of therapeutic (non) response before start of treatment could aid in clinical decision making of a personalized treatment approach. Numerous studies have previously addressed this topic.

Objectives To review studies that address prediction of response to biological treatment in rheumatoid arthritis (RA) and to explore the added value in clinical applicability of these (bio)markers.

Methods A search for relevant articles was performed in PUBMED, EMBASE and COCHRANE databases. Studies which presented predictive values or in which these could be calculated were selected. The added value was determined upon sensitivities, specificities and the added value on prior probability for each (bio)marker univariately. An increase/decrease in chance of response of ≥15% was considered clinically relevant enough, whereas in oncology values >25% are common.

Results Out of the 52 eligible studies, 14 (bio)markers were studied multiple times and were compared for the additive predictive effect of each (bio)marker. Of the replicated predictors, none consistently showed an increase/decrease in chance of response of ≥15%. The TNF-alpha 308 polymorphism modestly predicted response to TNF-alpha inhibitors (decrease of 3.7-30.1%), and rheumatoid factor (RF) and anti-citrullinated antibodies (ACPA) predicted response to rituximab (increase of 1.9-8.9% and 1.1-7.5% resp.). Besides these, 71 (bio)markers studied once, showed remarkably high (but not validated) predictive values.

Conclusions We were not able to indicate truly clinically useful baseline (bio)markers for individually tailored treatment. Few predictors are consistently predictive yet low in added predictive value, while several others are promising but await replication. The challenge now is to design studies to validate all explored and promising findings individually and in combination, to make these (bio)markers relevant to clinical practice.

Disclosure of Interest None declared

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