Project Summary:
Multiple sclerosis (MS) exhibits a markedly heterogeneous and unpredictable course, with a clinical
spectrum ranging from very mild forms of the disease in some patients (often termed “benign MS”) to an
aggressive disease course with rapid accumulation of disability in others. Furthermore, there appears to be
significant inter-individual variability in the responses to the many available disease-modifying therapies (DMT).
A variety of factors have been proposed to be associated with the disease course in MS, including demographics,
lifestyle factors, clinical characteristics, MRI-derived measures, and elevated serum neurofilament light chain
(NfL), among others. It remains unclear though if these factors are complementary or redundant in their predictive
value. Moreover, there is a lack of validated tools to accurately predict, at an individual level, future inflammatory
disease activity or disability worsening. This is largely due to the lack of datasets with sufficient size, breadth and
representativeness. The use of electronic medical records (EMR) has dramatically increased in recent years,
enabling the capture of a wide variety of data measures from large numbers of individuals. Furthermore, the
development and refinement of statistical machine learning methods has revolutionized the approach to analysis
of such high-dimensional datasets. This background provides a unique opportunity to leverage and analyze “big
data” in order to develop clinical risk prediction algorithms and personalized medicine tools in MS.
Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is a network of 10
MS centers that have standardized elements of their clinical practice to implement a centralized health
information exchange architecture. MS PATHS was designed around the concept of a learning health system
(LHS), merging research with ongoing patient care by collecting standardized clinical and imaging data during
routine medical visits. As of August, 2019, >15,000 patients have opted to participate in MS PATHS. Thus, the
MS PATHS network is an ideal, deeply phenotyped, “real-world”, large population of people with MS, in which
clinically relevant predictive algorithms may be developed and validated.
The goal of the current project is to develop and validate multi-modal predictive algorithms of clinically
relevant disease outcomes in MS. We hypothesize that integrating a wide variety of potential predictors, including
demographics, clinical characteristics (including current/historical DMT use), comorbidities/lifestyle factors, MRI-
derived measures and laboratory data (including serum NfL) will lead to the development and validation of
algorithms that may accurately predict future clinical disability worsening and inflammatory disease activity.
Furthermore, this approach will allow the assessment of the individual contribution of specific predictors to the
developed predictive algorithms, and may aid with the identification of novel risk factors of disease severity in
MS.
