Precision medicine (PM) is a very commonly used term that implies a highly individualized and tailored approach to patient management. There are, however, many layers of precision, as for example taking an appropriate patient history, or performing additional lab or imaging tests are already helping to better tailor treatments to the right patient. All this adds to the narrower definition of PM, which implies using the unique molecular characteristics of a patient for management decisions. Big data has become an essential part of PM, including as much information as possible to improve precision of disease management, although integration of multi-source data continues to be a challenge in practical application. In research big data can identify new (sub-)phenotypes in unsupervised analyses, which ultimately advance precision by allowing new targeted therapeutic approaches. We will discuss the current status of PM in rheumatoid arthritis (RA) in the management areas of diagnosis, prognosis, selection of therapy, and decision to reduce therapy. PM markers for diagnosis of RA are usually markers of RA classification rather than diagnosis, and subtypes of RA are potentially underrecognized. Prognostic precision is well established for RA, including markers of disease activity or structure, as well as autoantibodies and genetics. The choice of the right compound in a patient identified to have a poor prognosis, however, remains widely arbitrary. Finally and most recently, the most reliable markers for a safe withdrawal of therapy continue to be lower levels of disease activity and longer presence of remission.
Keywords: Biomarkers; Diagnosis; Precision medicine; Prediction; Prognosis; Rheumatoid arthritis.
Copyright © 2020. Published by Elsevier Ltd.