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Background: Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is the first example of a learning health system in multiple sclerosis (MS). This paper describes the initial implementation of MS PATHS and initial patient characteristics. Methods: MS PATHS is an ongoing initiative conducted in 10 healthcare institutions in three countries, each contributing standardized information acquired during routine care. Institutional participation required the following: active MS patient census of ≥500, at least one Siemens 3T magnetic resonance imaging scanner, and willingness to standardize patient assessments, share standardized data for research, and offer universal enrolment to capture a representative sample. The eligible participants have diagnosis of MS, including clinically isolated syndrome, and consent for sharing pseudonymized data for research. MS PATHS incorporates a self-administered patient assessment tool, the Multiple Sclerosis Performance Test, to collect a structured history, patient-reported outcomes, and quantitative testing of cognition, vision, dexterity, and walking speed. Brain magnetic resonance imaging is acquired using standardized acquisition sequences on Siemens 3T scanners. Quantitative measures of brain volume and lesion load are obtained. Using a separate consent, the patients contribute DNA, RNA, and serum for future research. The clinicians retain complete autonomy in using MS PATHS data in patient care. A shared governance model ensures transparent data and sample access for research. Results: As of August 5, 2019, MS PATHS enrolment included participants (n = 16,568) with broad ranges of disease subtypes, duration, and severity. Overall, 14,643 (88.4%) participants contributed data at one or more time points. The average patient contributed 15.6 person-months of follow-up (95% CI: 15.5-15.8); overall, 166,158 person-months of follow-up have been accumulated. Those with relapsing-remitting MS demonstrated more demographic heterogeneity than the participants in six randomized phase 3 MS treatment trials. Across sites, a significant variation was observed in the follow-up frequency and the patterns of disease-modifying therapy use. Conclusions: Through digital health technology, it is feasible to collect standardized, quantitative, and interpretable data from each patient in busy MS practices, facilitating the merger of research and patient care. This approach holds promise for data-driven clinical decisions and accelerated systematic learning.

Concepts of multiple sclerosis (MS) have shifted from the traditional view of a T cell-mediated, demyelinating disease of the white matter to include a broad range of immunopathogenic mechanisms, axonal damage, and widespread gray matter pathology. The cause of MS remains unknown, but recent epidemiological work has focused on genetic factors; environmental factors such as vitamin D, sunlight, and Epstein-Barr virus; and the controversial theory of chronic cerebrospinal venous insufficiency. Revised criteria facilitate making the diagnosis of MS. Emerging therapies are rapidly expanding treatment options, including both parenterally administered and oral medications. Strategies to preserve tissue, promote repair, and restore function are under development, and it is anticipated that they will provide better options for patients with progressive disease.

Background: The impact of multiple sclerosis (MS) disease-modifying therapies (DMTs) on SARS-CoV-2 vaccination response is uncertain. Methods: Post-SARS-CoV-2 vaccination blood samples across multiple DMTs were tested for SARS-CoV-2 immunoglobulin G (IgG) response. Results: Three hundred twenty-two people with MS were included; 91.9% received an mRNA vaccine. Post-vaccination reactive IgG rates (IgG index > 1) were 40% for anti-CD20 (32/80 patients); 41% for sphingosine 1-phosphate receptor modulators (S1PRM, 16/39); and 100% for all other classes, including the no DMT group. Conclusion: Anti-CD20 therapies and S1PRMs reduce IgG response to SARS-CoV-2 vaccination; IgG response is preserved with other DMTs.

Objective-Investigate the relationship between socioeconomic status (SES) and race with selfreported fatigue, depression, and anxiety levels in multiple sclerosis (MS). Methods-Cross-sectional review of the MS Partners Advancing Technology and Health Solutions (MS PATHS) database for adults with MS in the United States. We evaluated race and socioeconomic status (available markers: insurance, employment status, or level of education) as predictors of fatigue, depression, and anxiety sub-scores of the Neuro-QoL (Quality of life in neurological disorders), with particular interest between Caucasians/ whites (CA) and African Americans/blacks (AA). Multivariate linear regression models included as covariates age, sex, disability status, smoking status, body mass index, and disease-modifying therapy. Results-7,430 individuals were included; compared to CA, AA tended to be younger, more female-predominant , and had a higher level of disability. AA had completed slightly less education, had a higher level of Medicaid coverage or uninsured status, and had higher rates of unemployed or disabled status. In the univariate model, markers oflower SES, by whichever definition we used, correlated with worse affective symptoms. In the multivariate model stratified by race, CA showed similar trends. In contrast, in AA, only lower SES by employment status was correlated with worse affective symptoms. In both CA and AA, moderate and severe level of disability correlated with worse affective symptoms.