Does disease activity add to functional disability in estimation of utility for rheumatoid arthritis patients on biologic treatment?

OBJECTIVE: Treatment in general is mostly directly aimed at disease activity, and measures such as the DAS28 might therefore present important additional information. Our aim was to develop and validate a model that uses a combination of disease activity (DAS28) and HAQs to estimate EuroQoL 5-dimension scale (EQ5D) utilities. METHODS: Longitudinal data from a cohort study in RA patients from the Utrecht Rheumatoid Arthritis Cohort study Group (Stichting Reumaonderzoek Utrecht) who started treatment with a biologic drug were used for mapping and validation. All 702 observations, including DAS28, HAQ and EQ5D assessed at the same time points, were used. The observations were randomly divided into a subset for development of the model (n = 428 observations) and a subset for validation (n = 274). A stepwise multivariable regression analysis was used to test the association of DAS28 (components) and HAQ (domains) with EQ5D. Model performance was assessed using the explained variance (R(2)) and root mean square errors. Observed and predicted utility scores were compared to check for under- or overestimation of the scores. Finally, the performance of the model was compared with published mapping models. RESULTS: Lower DAS28 score and HAQ items dressing and grooming, arising, eating, walking and activities were associated with higher EQ5D scores. The final model had an explained variance of 0.35 and a lower root mean square error as compared with other models tested. The agreement between predicted and observed scores was fair. CONCLUSION: HAQ components estimate EQ5D better than total HAQ. Adding DAS28 to HAQ components does not result in better utility estimations.

Personalized biological treatment for rheumatoid arthritis: a systematic review with a focus on clinical applicability.

OBJECTIVES: To review studies that address prediction of response to biologic treatment in RA and to explore the clinical utility of the studied (bio)markers. METHODS: A search for relevant articles was performed in PubMed, Embase and Cochrane databases. Studies that presented predictive values or in which these could be calculated were selected. The added value was determined by the added value on prior probability for each (bio)marker. Only an increase/decrease in chance of response ⩾15% was considered clinically relevant, whereas in oncology values >25% are common. RESULTS: Of the 57 eligible studies, 14 (bio)markers were studied in more than one cohort and an overview of the added predictive value of each marker is presented. Of the replicated predictors, none consistently showed an increase/decrease in probability of response ⩾15%. However, positivity of RF and ACPA in case of rituximab and the presence of the TNF-α promoter 308 GG genotype for TNF inhibitor therapy were consistently predictive, yet low in added predictive value. Besides these, 65 (bio)markers studied once showed remarkably high (but not validated) predictive values. CONCLUSION: We were unable to address clinically useful baseline (bio)markers for use in individually tailored treatment. Some 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.