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.
Successful completion of this project will lead to the development and dissemination of clinically relevant predictive algorithms to enable personalized risk assessment and therapeutic decision-making in MS, provide the means to optimize selection/stratification of participants in clinical trials, and will also provide insight regarding the interactions and relative importance of putative prognostic factors in MS.
