Decision making in rheumatoid arthritis treatment: from population to patients

Background: There is a high need to tailor treatment to the individual patient in rheumatoid arthritis (RA) which is also known as personalized treatment. It has become increasingly important to assess the applicability of newly developed tools. Methods: In the first part of the thesis relationship between different processes and outcomes and the generalizability of the results form RCTs was studied. In the second part of this thesis extensive literature search was performed on personalized treatment approaches. Treatment algorithms were developed for specific markers. Based on treatment algorithm and the predictive ability of test(s), an estimate of the increased effectiveness of the treatment strategy, and the (longer term) effect on health and costs was made. Objective: Hence the central question in this thesis was how can recently developed tools/knowledge regarding the prediction of response to (biological) treatment in RA be used in a patient tailored treatment approach and what are the expected cost and health consequences? Results:In the first part of the thesis, it was possible to determine the importance of disease activity as an important treatment target for RA during the course of the disease, early but also (especially) later in relation to functional ability. However, the addition of disease activity to health assessment questionnaire in determining utility was only minimal. Finally, translation of results from RCTs does not seem straight forward but the treatment effect is influenced by certain prognostic factors which need to be considered. In the second part of the thesis personalized treatment were extensively explored in the literature, although many markers have potential but need replication. Personalized tools have potential to be cost effective and the developed treatment algorithm could be beneficial in driving treatment decision. Although these results are preliminary, this approach would benefit preventing patients and society from over and under treatment. Conclusions Overall, it can be said that one of the most ideal ways to go forward in decision making for biological treatment is to be able to predict which individual would benefit from which biological. We present prediction rules along with cost effectiveness of some predictive tools to facilitate better decision making in treatment of RA.