Extending the OMOP Common Data Model and Standardized Vocabularies to Support Observational Cancer Research

Belenkaya, Rimma; Gurley, Michael; Golozar, Asieh; Dymshyts, Dmitry; Miller, Robert T.; Williams, Andrew E.; Ratwani, Shilpa; Siapos, Anastasios; Korsik, Vladislav; Warner, Jeremy L.; Campbell, W. Scott; Rivera, Donna R.; Banokina, Tatiana; Modina, Elizaveta; Bethusamy, Shantha; Patel, Meera; Chen, Ruijun; Falconer, Thomas; Park, Rae Woong; You, Seng Chan; Jeon, Hokyun; Shin, Soe Jeong; Reich, Christian

doi:10.1200/cci.20.00079

Cited by 55 publications

(38 citation statements)

References 9 publications

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“…In contrast, the core structure of the OMOP CDM is fixed and the number of tables, and their respective fields, are finite, which allows for easier and consistent application development due to the underlying rigidity of the data model. Moreover, the OMOP CDM was designed for observational data (including claims and electronic healthcare records), and a number of observational healthcare databases have already been mapped to OMOP CDM, including a PH registry [ 30 – 32 , 34 – 36 ]. In addition, a study comparing the utility of various CDMs for the purposes of comparative effectiveness research found the OMOP CDM to be the best-suited [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases

Biedermann

Ong

Davydov³

et al. 2021

BMC Med Res Methodol

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Background The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) can be used to transform observational health data to a common format. CDM transformation allows for analysis across disparate databases for the generation of new, real-word evidence, which is especially important in rare disease where data are limited. Pulmonary hypertension (PH) is a progressive, life-threatening disease, with rare subgroups such as pulmonary arterial hypertension (PAH), for which generating real-world evidence is challenging. Our objective is to document the process and outcomes of transforming registry data in PH to the OMOP CDM, and highlight challenges and our potential solutions. Methods Three observational studies were transformed from the Clinical Data Interchange Standards Consortium study data tabulation model (SDTM) to OMOP CDM format. OPUS was a prospective, multi-centre registry (2014–2020) and OrPHeUS was a retrospective, multi-centre chart review (2013–2017); both enrolled patients newly treated with macitentan in the US. EXPOSURE is a prospective, multi-centre cohort study (2017–ongoing) of patients newly treated with selexipag or any PAH-specific therapy in Europe and Canada. OMOP CDM version 5.3.1 with recent OMOP CDM vocabulary was used. Imputation rules were defined and applied for missing dates to avoid exclusion of data. Custom target concepts were introduced when existing concepts did not provide sufficient granularity. Results Of the 6622 patients in the three registry studies, records were mapped for 6457. Custom target concepts were introduced for PAH subgroups (by combining SNOMED concepts or creating custom concepts) and World Health Organization functional class. Per the OMOP CDM convention, records about the absence of an event, or the lack of information, were not mapped. Excluding these non-event records, 4% (OPUS), 2% (OrPHeUS) and 1% (EXPOSURE) of records were not mapped. Conclusions SDTM data from three registries were transformed to the OMOP CDM with limited exclusion of data and deviation from the SDTM database content. Future researchers can apply our strategy and methods in different disease areas, with tailoring as necessary. Mapping registry data to the OMOP CDM facilitates more efficient collaborations between researchers and establishment of federated data networks, which is an unmet need in rare diseases.

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Section: Introductionmentioning

confidence: 99%

Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases

Biedermann

Ong

Davydov³

et al. 2021

BMC Med Res Methodol

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“… 27 Moreover, there have been proposals for the integration of genomic or oncology data into the CDM. 28 29 The implementation of standardized data infrastructure combining imaging biomarkers with genomic and clinical phenotype information based on standardized data may facilitate precision medical research. 26 …”

Section: Discussionmentioning

confidence: 99%

Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data

et al. 2022

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Purpose Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM). Materials and Methods R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database. Results Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts. Conclusion R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.

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“…Several initiatives, such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model, are ongoing as possible solutions to transform data within disparate databases into a standardized vocabulary. 24 , 25 Knowledge of the type of data available from the data source is pertinent in order to be mindful of the completeness of the data. Major flaws in these foundational components of the study will limit any usability—regardless of the ingenuity of the study design or robustness of analyses.…”

Section: How Do You Determine High-quality Rwe?mentioning

confidence: 99%

Old dog with new tricks: An introduction to real-world evidence for pharmacists

Rahmatian

Tadrous

2021

American Journal of Health-System Pharmacy

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Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

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Extending the OMOP Common Data Model and Standardized Vocabularies to Support Observational Cancer Research

Cited by 55 publications

References 9 publications

Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases

Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases

Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data

Old dog with new tricks: An introduction to real-world evidence for pharmacists

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