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How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

Najia Ahmadi1, Michele Zoch2, Oya Guengoeze3

  • 1Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany. najia.ahmadi@tu-dresden.de.

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Summary
This summary is machine-generated.

Developing a rare diseases common data model (RD-CDM) facilitates multi-center studies by harmonizing data. This OMOP-based structure enables AI-driven insights for rare disease research.

Keywords:
Artificial intelligenceCommon data modelData standardizationGenotypes and phenotypesInteroperabilityMulti-center studiesOHDSIOMOPRare disease

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Area of Science:

  • Medical Informatics
  • Rare Diseases Research
  • Data Harmonization

Background:

  • Geographical sparsity of rare diseases (RDs) complicates cohort assembly.
  • Common Data Models (CDM) are crucial for harmonizing disparate data sources.
  • This study aims to support large-scale, multi-center studies for RDs.

Purpose of the Study:

  • To design a common data model (CDM) for rare diseases (RDs).
  • To facilitate multi-center studies and AI-based research in RDs.
  • To create a blueprint for extending the CDM to other domains.

Main Methods:

  • An interdisciplinary group defined RD data elements across three medical domains.
  • Extract, Transform, Load (ETL) processes were developed to map data to a joint CDM.
  • The developed CDM was mapped to the Observational Medical Outcomes Partnership (OMOP) CDM and tested with a hematology dataset.

Main Results:

  • An OMOP-based rare diseases common data model (RD-CDM) was developed and tested.
  • The study cohort comprised 61,697 patients.
  • ETL processes were leveraged for seamless data transfer, with specific modules for phenotypes and genotypes.

Conclusions:

  • The modular RD-CDM can be extended to novel data types and serve as a blueprint for other domains.
  • An interdisciplinary stakeholder group is essential for comprehensive CDM development.
  • The RD-CDM enables large-scale, multi-center studies and leverages OHDSI community tools.