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应用OMOP通用数据模型,利用来自新加坡和韩国的真实世界数据促进药品的获益-风险评估。

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

作者信息

Tan Hui Xing, Teo Desmond Chun Hwee, Lee Dongyun, Kim Chungsoo, Neo Jing Wei, Sung Cynthia, Chahed Haroun, Ang Pei San, Tan Doreen Su Yin, Park Rae Woong, Dorajoo Sreemanee Raaj

机构信息

Vigilance & Compliance Branch, Health Products Regulation Group, Health Sciences Authority, Singapore.

Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.

出版信息

Healthc Inform Res. 2022 Apr;28(2):112-122. doi: 10.4258/hir.2022.28.2.112. Epub 2022 Apr 30.

DOI:10.4258/hir.2022.28.2.112
PMID:35576979
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9117808/
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.

摘要

目的

本研究旨在描述将电子病历(EMR)转换为通用数据模型(CDM)的益处,并评估经CDM转换的数据在上市后监管评估和决策中快速生成效益风险评估见解的潜力。

方法

将2013年1月至2016年12月的电子病历映射到观察性医疗结果合作组织通用数据模型(OMOP-CDM)架构上。应用词汇映射将源数据值转换为OMOP-CDM认可的术语。对先前一项OMOP-CDM药物利用研究中使用的现有分析代码进行修改,以对新加坡和韩国用于房颤的口服抗凝剂进行实例分析,类似于典型的效益风险评估。提出了一种新颖的可视化方法来展示药物的比较有效性、安全性和利用率。

结果

超过90%的记录被映射到OMOP-CDM上。CDM数据结构和分析代码模板简化了用于分析的数据查询。新加坡和韩国共有2419名患者符合研究标准,其中大多数是华法林使用者。经过3个月的随访,通过所提出的可视化方法可以观察到出血和血栓栓塞事件累积发生率的差异,从而得出在特定临床环境中首选药物的见解,这可能会为监管决策提供有意义的信息。

结论

虽然OMOP-CDM的结构及其辅助工具便于进行真实世界数据分析,但将其扩展以满足上市后环境中的监管分析目的,如效益风险评估,可能需要叠加额外的分析工具和可视化技术。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/bfe8b3444b7d/hir-2022-28-2-112f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/ace908eb807a/hir-2022-28-2-112f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/d5238c69daf5/hir-2022-28-2-112f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/a25c5c209d06/hir-2022-28-2-112f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/26a2a431b535/hir-2022-28-2-112f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/a83b770801c0/hir-2022-28-2-112f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/bfe8b3444b7d/hir-2022-28-2-112f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/ace908eb807a/hir-2022-28-2-112f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/d5238c69daf5/hir-2022-28-2-112f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/a25c5c209d06/hir-2022-28-2-112f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/26a2a431b535/hir-2022-28-2-112f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/a83b770801c0/hir-2022-28-2-112f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9189/9117808/bfe8b3444b7d/hir-2022-28-2-112f6.jpg

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