Healthc Inform Res.  2022 Apr;28(2):112-122. 10.4258/hir.2022.28.2.112.

Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea

• Tan HX
• Teo DCH
• Lee D
• Kim C
• Neo JW
• Sung C
• Chahed H
• Ang PS
• Tan DSY
• Park RW
• Dorajoo SR

• KMID: 2529793
• DOI: http://doi.org/10.4258/hir.2022.28.2.112

Abstract

Objectives
The aim of this study was to characterize the benefits of converting Electronic Medical Records (EMRs) to a common data model (CDM) and to assess the potential of CDM-converted data to rapidly generate insights for benefit-risk assessments in post-market regulatory evaluation and decisions.
Methods
EMRs from January 2013 to December 2016 were mapped onto the Observational Medical Outcomes Partnership-CDM (OMOP-CDM) schema. Vocabulary mappings were applied to convert source data values into OMOP-CDM-endorsed terminologies. Existing analytic codes used in a prior OMOP-CDM drug utilization study were modified to conduct an illustrative analysis of oral anticoagulants used for atrial fibrillation in Singapore and South Korea, resembling a typical benefit-risk assessment. A novel visualization is proposed to represent the comparative effectiveness, safety and utilization of the drugs.
Results
Over 90% of records were mapped onto the OMOP-CDM. The CDM data structures and analytic code templates simplified the querying of data for the analysis. In total, 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 via the proposed visualization, surfacing insights as to the agent of preference in a given clinical setting, which may meaningfully inform regulatory decision-making.
Conclusions
While the structure of the OMOP-CDM and its accessory tools facilitate real-world data analysis, extending them to fulfil regulatory analytic purposes in the post-market setting, such as benefit-risk assessments, may require layering on additional analytic tools and visualization techniques.

Keyword

Pharmacovigilance; Health Policy; Risk Assessment; Anticoagulants; Data Visualization

Figure

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  Figure 1 Mapping from source database to target database generated by the Observational Health Data Sciences Initiative (OHDSI) Rabbit-In-a-Hat tool. CDM: common data model.

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  Figure 2 Example of mapping from local concepts to concepts in the Observational Medical Outcomes Partnership (OMOP) vocabulary. ICD-10: International Classification of Diseases 10th edition, SNOMED-CT: Systematic Nomenclature of Medicine Clinical Terms.

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  Figure 3 Differentiating between source values, source Concept IDs, and standard Concept IDs. OMOP: Observational Medical Outcomes Partnership, ICD-9: International Classification of Diseases 9th edition, ICD-10: International Classification of Diseases 10th edition.

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  Figure 4 Study overview detailing criteria for inclusion and exclusion, exposure, and outcomes.

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  Figure 5 Flow diagram showing the number of persons in the final qualifying cohorts from Singapore and South Korea.

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  Figure 6 Total cohort follow-up analysis: (A) and (B) are 100%, horizontally-stacked, utilization-adjusted bar charts of effectiveness and safety. The vertical height of each bar is proportional to the number of patients in the Singaporean and South Korean cohorts for 4 years of follow-up. Landmark analysis at 3 months: (C) and (D) are 100%, horizontally-stacked, utilization-adjusted bar charts of effectiveness and safety limited to a follow-up period of three months. The vertical height of each bar is proportional to the number of patients in the Singaporean and South Korean cohorts for 3 months of follow-up. The number of patients experiencing the events of interest are represented as proportions within each bar. Event proportions are unadjusted for confounding factors. Drug A: rivaroxaban, Drug B: warfarin.

Reference

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