Dataset Information

HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model.

ABSTRACT: Systematic application of observational data to the understanding of impacts of cancer treatments requires detailed information models allowing meaningful comparisons between treatment regimens. Unfortunately, details of systemic therapies are scarce in registries and data warehouses, primarily due to the complex nature of the protocols and a lack of standardization. Since 2011, we have been creating a curated and semi-structured website of chemotherapy regimens, HemOnc.org. In coordination with the Observational Health Data Sciences and Informatics (OHDSI) Oncology Subgroup, we have transformed a substantial subset of this content into the OMOP common data model, with bindings to multiple external vocabularies, e.g., RxNorm and the National Cancer Institute Thesaurus. Currently, there are >73,000 concepts and >177,000 relationships in the full vocabulary. Content related to the definition and composition of chemotherapy regimens has been released within the ATHENA tool (athena.ohdsi.org) for widespread utilization by the OHDSI membership. Here, we describe the rationale, data model, and initial contents of the HemOnc vocabulary along with several use cases for which it may be valuable.

SUBMITTER: Warner JL

PROVIDER: S-EPMC6697579 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model.

Warner Jeremy L JL Dymshyts Dmitry D Reich Christian G CG Gurley Michael J MJ Hochheiser Harry H Moldwin Zachary H ZH Belenkaya Rimma R Williams Andrew E AE Yang Peter C PC

Journal of biomedical informatics 20190622

Systematic application of observational data to the understanding of impacts of cancer treatments requires detailed information models allowing meaningful comparisons between treatment regimens. Unfortunately, details of systemic therapies are scarce in registries and data warehouses, primarily due to the complex nature of the protocols and a lack of standardization. Since 2011, we have been creating a curated and semi-structured website of chemotherapy regimens, HemOnc.org. In coordination with ...[more]

PMID: 31238109

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Project description:BackgroundThe 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.MethodsThree 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.ResultsOf 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.ConclusionsSDTM 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.

Project description:BackgroundThe Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) has been implemented on various claims and electronic health record (EHR) databases, but has not been applied to a hospital transactional database. This study addresses the implementation of the OMOP CDM on the U.S. Premier Hospital database.MethodsWe designed and implemented an extract, transform, load (ETL) process to convert the Premier hospital database into the OMOP CDM. Standard charge codes in Premier were mapped between the OMOP version 4.0 Vocabulary and standard charge descriptions. Visit logic was added to impute the visit dates. We tested the conversion by replicating a published study using the raw and transformed databases. The Premier hospital database was compared to a claims database, in regard to prevalence of disease.FindingsThe data transformed into the CDM resulted in 1% of the data being discarded due to data errors in the raw data. A total of 91.4% of Premier standard charge codes were mapped successfully to a standard vocabulary. The results of the replication study resulted in a similar distribution of patient characteristics. The comparison to the claims data yields notable similarities and differences amongst conditions represented in both databases.DiscussionThe transformation of the Premier database into the OMOP CDM version 4.0 adds value in conducting analyses due to successful mapping of the drugs and procedures. The addition of visit logic gives ordinality to drugs and procedures that wasn't present prior to the transformation. Comparing conditions in Premier against a claims database can provide an understanding about Premier's potential use in pharmacoepidemiology studies that are traditionally conducted via claims databases.Conclusion and next stepsThe conversion of the Premier database into the OMOP CDM 4.0 was completed successfully. The next steps include refinement of vocabularies and mappings and continual maintenance of the transformed CDM.

Dataset Information

HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model.

Publications

HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model.

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