ArticleBMC medical research methodology2021
Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
51 citing papers in PubMed, 84 citations in OpenAlex.
- Addressing Data Quality Challenges in Lung Cancer Data Within the Observational Medical Outcomes Partnership Common Data Model: Observational Study.Journal of medical Internet research · 2026Observational
- TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching.Nature communications · 2026Article
- Transforming Traditional Korean Medicine hospital EHRs into the OMOP common Data Model: methodology and implications.BMC medical informatics and decision making · 2026Article
- Prediction of antibiotic-associated cutaneous adverse drug reactions using electronic health record foundation models.NPJ digital medicine · 2026Article
- Evaluating the statistical utility and information loss in the transformation of a real-world oncology database from CDISC-SDTM to OMOP-CDM.ESMO real world data and digital oncology · 2026Article
- Investigating CAR-T Treatment Access for Multiple Myeloma Patients Using Real-World Evidence.Cancers · 2026Article
- Extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2).Critical care medicine · 2026Article
- A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.Journal of biomedical informatics · 2026Article
- Federated learning for heterogeneous electronic health record systems with cost effective participant selection.Scientific reports · 2026Article
- Large-scale manual curation and harmonization of metadata from metagenomic and cancer genomic repositories: challenges and solutions.Database : the journal of biological databases and curation · 2026Article
- The intersection of health information management and clinical registries.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Evaluating the impact of OMOP-CDM on data quality insight generation in respiratory disease management.Frontiers in big data · 2026Article
- Harmonizing patient health data into the OMOP common data model: a case study using tuberculosis data from Douala General Hospital, Cameroon.Frontiers in digital health · 2026Article
- Large-scale Manual Curation and Harmonization of Metadata from Metagenomic and Cancer Genomic Repositories: Challenges and Solutions.bioRxiv : the preprint server for biology · 2025Article
- Establishing standards: harmonising coding principles for a minimal cancer dataset in the OMOP Common Data Model.ESMO real world data and digital oncology · 2025Article
- Biological markers and psychosocial factors predict chronic pain conditions.Nature human behaviour · 2025Article
- Article
- Making the case for an International Childhood Cancer Data Partnership.Journal of the National Cancer Institute · 2025Article
- Pretrained patient trajectories for adverse drug event prediction using common data model-based electronic health records.Communications medicine · 2025Article
- Secular trends in heat related illness and excess sun exposure rates across climatic zones in the United States from 2017 to 2022.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.