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This study created the first myositis-specific Common Data Elements (CDEs) to standardize data collection for myositis research. These new CDEs will improve data sharing and accelerate clinical studies and therapeutic trials for myositis patients.

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Area of Science:

  • Rheumatology
  • Data Science
  • Clinical Informatics

Background:

  • Validated myositis outcome measures are advancing, but data standardization across registries remains a challenge.
  • The National Institutes of Health (NIH) Common Data Elements (CDE) Repository aims to standardize data, yet lacks sufficient myositis-specific CDEs.
  • Novel data science strategies were employed to develop essential myositis outcome measure CDEs.

Purpose of the Study:

  • To develop standardized, computable Common Data Elements (CDEs) for myositis outcome measures.
  • To address critical deficiencies in data standardization for myositis registries.
  • To facilitate interoperable data collection and sharing for myositis research.

Main Methods:

  • Examined data dictionaries from myositis registries to understand current outcome measure capture.
  • Utilized the Linked data Modeling Language (LinkML) to create computable CDEs.
  • Convened international expert panels using Delphi and modified nominal group techniques for consensus on CDEs.

Main Results:

  • Established a workflow for CDE creation, resulting in 852 new CDEs for 27 myositis forms.
  • Achieved universal agreement on CDEs for ten myositis core set activity measures (CSMs).
  • Prioritized and developed CDEs for additional measures including response criteria, damage, physical function, and PROMIS instruments.

Conclusions:

  • Developed the first myositis-specific CDEs by integrating clinical and data science expertise.
  • These CDEs will accelerate interoperable myositis clinical studies and therapeutic trials.
  • The established workflow can be applied to develop CDEs for other autoimmune diseases.