Empirical development of a multimodal prognostication tool to guide treatment-strategy for Multiple Sclerosis (R-12918)

Multiple Sclerosis (MS) is an autoimmune neurodegenerative disease of the central nervous system. It is crucial to halt disease progression early to prevent long-term disability. Two treatment strategies are used: a classical escalation (starting first line agents) and an induction (starting high-efficacy drugs) approach. The therapy decision is often only based on clinical disease activity; however, subclinical disease can cause a patient to have a poor prognosis and be in need of highly-efficacious therapy. It is thus essential to determine the correct prognosis of a patient. Nonetheless, it is not evident to be cognizant of all prognostic signals and reliable prognostic tools for MS are lacking. Hence, in this project we want to integrate all key prognostic factors into a compact, multimodal prognostication tool for establishing MS prognosis and guiding treatment strategy, incorporating the expertise of MS specialists (MSSs). First, general neurologists and MSSs will be asked to formulate a prognosis and treatment strategy for authentic MS cases using baseline prognostic factors. Their data will be used to assess inter-neurologist variance and build the prediction model behind the tool. Collaborating with data scientists, a prototype of the tool will be established that will be tested for accuracy on an international cohort. Finally, the added value of the tool vs. the standard of care will be determined. We believe our tool could change the way we treat MS patients today.

Keywords:multimodal prognostication tool, multiple sclerosis

Disciplines:Neurological and neuromuscular diseases