Predicting outcomes in rheumatoid arthritis

Abstract

Biologic therapies have brought improved efficacy in the field of rheumatoid arthritis (RA), but their use in clinical practice may be limited by concerns over cost. Predictive models are, therefore, needed to identify those people with RA with the worst potential outcomes, who will benefit most from the use of these drugs. A variety of studies have investigated factors that will predict the onset of RA to allow preventative intervention and the identification of prognostic factors to guide the need for aggressive treatment at the time of diagnosis and prognostic factors in patients who have failed on optimal traditional therapies—all strategies to guide the cost-effective use of modern therapies. Prediction rules have been developed that are sensitive and specific, but many are limited by their complexity or the need for biomarkers that will never be routinely measured in the clinic. Most rules to date have therefore failed to have a major impact on clinical practice. Probably most interesting is the prediction of response to therapy based upon early treatment response, with outcomes at as early as 3 months predicting response at 12 months. Further work is needed, however, to identify the efficacy of current therapies in preventing disease onset and the long-term cost-effectiveness of appropriately targeted treatment with biologics.