Personalized medicine in rheumatoid arthritis: Combining biomarkers and patient preferences to guide therapeutic decisions

Abstract

The last few decades have seen major therapeutic advancements in rheumatoid arthritis (RA) therapeutics. New disease-modifying antirheumatic drugs (DMARDs) have continued to emerge, creating more choices for people. However, no therapeutic works for all patients. Each has its own inherent benefits, risks, costs, dosing, and monitoring considerations. In parallel, there has been a focus on personalized medicine initiatives that tailor therapeutic decisions to patients based on their unique characteristics or biomarkers. Personalized effect estimates require an understanding of a patient's baseline probability of response to treatment and data on the comparative effectiveness of the available treatments. However, even if accurate risk prediction models are available, trade-offs often still need to be made between treatments. In this paper, we review the history of RA therapeutics and progress that has been made toward personalized risk predictive models for DMARDs, outlining where knowledge gaps still exist. We further review why patient preferences play a key role in a holistic view of personalized medicine and how this links with shared decision-making. We argue that a "preference misdiagnosis" may be equally important as a medical misdiagnosis but is often overlooked.

Keywords: Clinical prediction models; Patient preferences; Personalized medicine; Shared decision-making.