Building Towards Initiation, Moderation, De-Escalation and Cessation of Disease-Modifying Treatments for Multiple...

Background/Objectives: Multiple Sclerosis (MS) is a chronic disease with significant clinical and radiological heterogeneity. This fact, together with the increased number of disease-modifying treatments available, poses challenges in the therapeutic decisions and for the overall management of the disease. In this study, an expert panel on MS from Greece aimed to formulate a consensus, in order to provide recommendation on disease-modifying treatment (DMT) initiation and switching, as well as de-escalation strategies in Relapsing MS (RMS). Methods: The study followed two-round voting based on a modified Delphi setting. A questionnaire was constructed by a subgroup of five experts (core group) and was subsequently administered in a printed form to a group of 12 MS experts in total (panel) in a face-to-face meeting. Consensus required at least 80% agreement within the panel in order to signify strong consensus. Results: The panel agreed that the overall therapeutic plan (DMT choice) must take into consideration the degree of disease activity (low/moderate/high). In certain cases with suboptimal response to a moderate-efficacy DMT, a horizontal switch to another moderate-efficacy DMT may be a valid strategy. However, in cases exhibiting disability accumulation, therapy escalation should be preferred. The concept of de-escalation was suggested as an alternative strategy for cases with stable disease receiving a high-efficacy long-term DMT in the long term. Due to the possibility of rebound phenomena with certain medications (such as fingolimod and natalizumab), a bridging strategy could be applied in cases of family planning and drug-related adverse events (such as lymphopenia and hepatotoxicity), especially in PwMS with recent inflammatory activity. Conclusions: Although novel biomarkers may soon help clinicians predict future disability accumulation, currently, regular and detailed patient monitoring seems to be the optimal way to guide clinicians' decisions on treatment changes.