Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea
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Objectives: The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to acommon data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefitriskassessments in post-market regulatory evaluation and decisions. Methods: EMRs from January 2013 to December 2016were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappingswere applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a priorOMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrialfibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed torepresent the comparative effectiveness, safety and utilization of the drugs. Results: Over 90% of records were mapped ontothe OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. Intotal, 2,419 patients from Singapore and South Korea fulfilled the study criteria, the majority of whom were warfarin users. After 3 months of follow-up, differences in cumulative incidence of bleeding and thromboembolic events were observable viathe proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfullyinform regulatory decision-making. Conclusions: While the structure of the OMOP-CDM and its accessory tools facilitatereal-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-riskassessments, may require layering on additional analytic tools and visualization techniques.
Abstract
Objectives: The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to acommon data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefitriskassessments in post-market regulatory evaluation and decisions. Methods: EMRs from January 2013 to December 2016were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappingswere applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a priorOMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrialfibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed torepresent the comparative effectiveness, safety and utilization of the drugs. Results: Over 90% of records were mapped ontothe OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. Intotal, 2,419 patients from Singapore and South Korea fulfilled the study criteria, the majority of whom were warfarin users. After 3 months of follow-up, differences in cumulative incidence of bleeding and thromboembolic events were observable viathe proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfullyinform regulatory decision-making. Conclusions: While the structure of the OMOP-CDM and its accessory tools facilitatereal-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-riskassessments, may require layering on additional analytic tools and visualization techniques.
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