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Mapping Local Biospecimen Records to the OMOP Common Data Model.

Chelsea L Michael1,2, Evan T Sholle3, Regina T Wulff4

  • 1Department of Health Informatics, Memorial Sloan Kettering Cancer Center, New York, NY.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|June 2, 2020
PubMed
Summary
This summary is machine-generated.

Integrating biospecimen and clinical data is crucial for leukemia precision medicine. Mapping biobank data to the Observational Medical Outcomes Partnership common data model (OMOP CDM) showed challenges with terminology, highlighting needs for broader code support and data structure enhancements.

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Area of Science:

  • Biomedical Informatics
  • Precision Medicine
  • Leukemia Research

Background:

  • Precision medicine for leukemia necessitates integrating diverse patient data, including biospecimens and clinical records.
  • The Observational Medical Outcomes Partnership common data model (OMOP CDM) offers a standardized framework for health data, including a Specimen table for biospecimen information.
  • Existing research details challenges in mapping electronic health record (EHR) data to the OMOP CDM, but less is known about populating it with biospecimen data.

Purpose of the Study:

  • To evaluate the feasibility and challenges of mapping institutional biobank data to the OMOP CDM's Specimen table.
  • To identify limitations in the current OMOP CDM structure and terminology for representing research-oriented biospecimen data.
  • To propose recommendations for improving the OMOP CDM for biospecimen data integration in leukemia research.

Main Methods:

  • Utilized biobank data from an institutional research cohort.
  • Mapped biospecimen records to the OMOP CDM Specimen table using established data transformation protocols.
  • Analyzed mapping failures to identify specific data elements and terminology causing incompatibility.

Main Results:

  • Successfully mapped 26% of the available biospecimen records to the OMOP Specimen table.
  • Identified incompatible local codes for time points as a primary barrier to mapping.
  • Highlighted the need for expanded code sets and structural modifications within the OMOP CDM for research biospecimen data.

Conclusions:

  • Populating the OMOP CDM Specimen table with research biobank data presents significant challenges, primarily due to incompatible terminologies.
  • Recommendations include expanding OMOP reference terminology, incorporating foreign keys for enhanced data linkage, and potentially developing new tables for processed samples.
  • Enhancements to the OMOP CDM are crucial for effectively integrating biospecimen data to advance precision medicine initiatives in leukemia.