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Harnessing Real-World Data to Inform Decision-Making: Multiple Sclerosis Partners Advancing Technology and Health

Ellen M Mowry1, Robert A Bermel2, James R Williams3

  • 1Johns Hopkins University, Baltimore, MD, United States.

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|August 28, 2020
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Summary
This summary is machine-generated.

The Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) learning health system successfully implemented standardized data collection in routine care. This digital health approach enables data-driven decisions and accelerates research for multiple sclerosis (MS) patients.

Keywords:
MS PATHSdigital health technologylearning health systemmultiple sclerosisstandardized brain magnetic resonance imaging

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Area of Science:

  • Neurology
  • Health Informatics
  • Clinical Research

Background:

  • The Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) initiative represents the first learning health system specifically designed for multiple sclerosis (MS) care.
  • This paper details the foundational implementation phase of MS PATHS and presents the initial demographic and clinical characteristics of its participant cohort.

Purpose of the Study:

  • To describe the initial implementation and operational framework of the MS PATHS learning health system.
  • To characterize the initial patient population enrolled in MS PATHS, including disease subtypes, duration, and severity.
  • To assess the feasibility of collecting standardized, quantitative data within busy clinical practices for both research and patient care.

Main Methods:

  • MS PATHS involves 10 healthcare institutions across three countries, standardizing data collection during routine care for patients with a diagnosis of MS.
  • Utilized the Multiple Sclerosis Performance Test (MSPT) for self-administered assessments of patient-reported outcomes and quantitative testing (cognition, vision, dexterity, walking speed).
  • Standardized Siemens 3T MRI acquisition sequences for quantitative brain volume and lesion load analysis, with options for collecting biological samples (DNA, RNA, serum).

Main Results:

  • As of August 2019, 16,568 participants were enrolled, with 14,643 (88.4%) contributing data, accumulating over 166,000 person-months of follow-up.
  • The enrolled population exhibited broad ranges of MS subtypes, duration, and severity, with relapsing-remitting MS patients showing greater demographic heterogeneity than those in Phase 3 trials.
  • Observed significant inter-site variations in follow-up frequency and patterns of disease-modifying therapy utilization.

Conclusions:

  • Digital health technology facilitates the collection of standardized, quantitative, and interpretable data from MS patients in routine clinical settings.
  • The MS PATHS model effectively merges research with patient care, enabling data-driven clinical decisions and promoting systematic learning.
  • This approach shows significant promise for accelerating advancements in multiple sclerosis treatment and management through real-world evidence.