Context-Sensitive Common Data Models for Genetic Rare Diseases – A Concept

Ahmadi, Najia; Zoch, Michéle; Sedlmayr, Brita; Schuler, Katharina; Hahn, Waldemar; Sedlmayr, Martin; Wolfien, Markus

doi:10.3233/shti230443

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A CDM can harmonize data from disparate sources, by utilizing communication (e.g., FHIR) and semantic (e.g., ICD-10, SNOMED) standards, enabling operations and analyses solely based on standard methods (24). One very promising approach is the Observational Medical Outcomes Partnership (OMOP) CDM from the Observational Health Data Sciences and Informatics (OHDSI) community, which comes with FAIR compliance, an international community, and ready-to-use tools for data integration and analysis (25)(26)(27). Compared to other CDMs like Informatics for Integrating Biology & the Bedside (i2b2), OMOP CDM offers broader terminology coverage, enabling data harmonization from different sources with minimal loss of data (25,28,29).…”

Section: Introductionmentioning

confidence: 99%

How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

Ahmadi,

Zoch,

Guengoeze

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common Data Models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. Discussion This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. Conclusion The customized data structure related our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

show abstract

Section: Introductionmentioning

confidence: 99%

How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

Ahmadi,

Zoch,

Guengoeze

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

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

Ahmadi,

Zoch,

Guengoeze

et al. 2024

Orphanet J Rare Dis

View full text Add to dashboard Cite

Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. Discussion This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. Conclusion The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

show abstract

Context-Sensitive Common Data Models for Genetic Rare Diseases – A Concept

Cited by 2 publications

References 2 publications

How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

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

Contact Info

Product

Resources

About