Article
NARSE goes OMOP: Mapping the dataset of the German National Registry for Rare Diseases to international standards
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Authors
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Michéle Zoch
- Institute for Medical Informatics and Biometry at the Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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Ines Reinecke
- Institute for Medical Informatics and Biometry at the Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany; Data Integration Center, Center for Medical Informatics, University Hospital Carl Gustav Carus Dresden, Dresden, Germany
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Elisa Henke
- Institute for Medical Informatics and Biometry at the Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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Yuan Peng
- Institute for Medical Informatics and Biometry at the Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
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Josef Schepers
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
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Miriam Hübner
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
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Franziska Krause
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany; Eva Luise und Horst Köhler Stiftung für Menschen mit Seltenen Erkrankungen, Bonn, Germany
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Holger Storf
- Institute of Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany
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Jens Göbel
- Institute of Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany
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Jessica Vasseur
- Institute of Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany
| Published: | September 6, 2024 |
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Outline
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1. Introduction: An estimated 446 million people worldwide suffer from rare diseases [1]. In the EU, a disease is considered rare if it affects fewer than 5 in 10,000 people [1]. It is assumed that there are up to 8,000 different condition, 90% of which have no adequate treatment methods [2]. Therefore, it is crucial to improve research on rare diseases. One option is to expand the data pool for research by using registries.
The National Registry for Rare Diseases (NARSE) is a patient registry for the consent-based collection of data from people affected by rare diseases in Germany. Its aim is to enhance patient discovery and streamline the access to information.
To support the (re-)use of the data in international observational studies, it is required to transfer it to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) [3]. OMOP offers a well-defined data model and utilizes international standards to describe Real-World Data (RWD). The aim of this work is to prepare the NARSE dataset for international research by mapping it to OMOP.
2. Methods: The NARSE dataset is based on the European Rare Disease Registry Infrastructure (ERDRI) Common Data Set (CDS) and comprises 47 data elements, organized into various topics including consent, formal criteria, personal and family background, medical history and diagnostics. Identifying data not essential for conducting observational studies were excluded from the mapping.
An interdisciplinary team developed the mapping based on a formal (nominal) consensus process. The mapping is stored within a Transition Database [3] to facilitate harmonization with other mappings. An additional file describes it in more detail.
3. Results: All data elements in scope could be mapped. On a syntactic level, 1-to-1 mappings were not always possible. For instance, there is no dedicated OMOP table for family background. Instead, the family members are stored as separate persons and the corresponding characteristics (e.g., diagnosis, cause of death) are then assigned to this person. On a semantic level, not all terminologies could seamlessly transition to OMOP-conform vocabularies. For example, despite initial efforts to align the Human Phenotype Ontology (HPO) with OMOP-conform vocabularies [4], not all HPO terms have been mapped yet.
The mapping is publicly available ([5] or https://github.com/m-zoch/narse-to-omop).
4. Discussion and conclusion: Because registries are presumed to be excellent sources for RWD, boasting higher data quality, particularly in terms of reliability [6], they serve as ideal bases for observational studies. The provided mapping facilitates patient data transfer to OMOP, thereby enabling the secondary use of registry data for research purposes. The primary challenge is to encompass various terminologies, especially for rare diseases, requiring alternative terminologies (e.g., ORPHAcodes, HPO). To represent these within OMOP, mapping to standard vocabularies (e.g., SNOMED CT, LOINC) can be employed, alongside establishing vocabularies through a community contribution process.
The availability of a publicly accessible mappings help link the national dataset to global research endeavors. Given the similarity between NARSE and ERDRI CDS, the presented mapping lays the foundation for transferring the international CDS of the European Reference Networks to OMOP.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
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