Abstract：
BACKGROUND: The Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the United Kingdom uses the Read v2 terminology in primary care. The availability of data sources is not uniform across the United Kingdom.
OBJECTIVE: This study aims to use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We planned to do this for vaccine coverage and 2 adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Read v2, the World Health Organization\'s International Classification of Disease Tenth Revision (ICD-10) terminology, and the UK Dictionary of Medicines and Devices (dm+d).
METHODS: Exposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the United Kingdom\'s devolved nations\' health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Automated Terminology Harmonization, Extraction, and Normalization for Analytics (ATHENA) online browser. Lead analysts from each nation then confirmed or added to the mappings identified. These mappings were then used to report AEIs in a common format. We reported rates for windows of 0-2 and 3-28 days postvaccine every 28 days.
RESULTS: We listed the mappings between Read v2, SNOMED CT, ICD-10, and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST, we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED CT codes, of which we selected 47 (84%), and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED CT codes and 9 Read v2 codes, of which we selected 10 (17%) and 4 (44%), respectively, to include in our repeated cross-sectional studies.
CONCLUSIONS: This approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies, such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 is sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall, the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.
OBJECTIVE: This study aims to use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We planned to do this for vaccine coverage and 2 adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Read v2, the World Health Organization\'s International Classification of Disease Tenth Revision (ICD-10) terminology, and the UK Dictionary of Medicines and Devices (dm+d).
METHODS: Exposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the United Kingdom\'s devolved nations\' health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Automated Terminology Harmonization, Extraction, and Normalization for Analytics (ATHENA) online browser. Lead analysts from each nation then confirmed or added to the mappings identified. These mappings were then used to report AEIs in a common format. We reported rates for windows of 0-2 and 3-28 days postvaccine every 28 days.
RESULTS: We listed the mappings between Read v2, SNOMED CT, ICD-10, and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST, we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED CT codes, of which we selected 47 (84%), and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED CT codes and 9 Read v2 codes, of which we selected 10 (17%) and 4 (44%), respectively, to include in our repeated cross-sectional studies.
CONCLUSIONS: This approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies, such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 is sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall, the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.
摘要：
背景：数据和连接COVID-19疫苗药物警戒（DaC-VaP）英国范围内的合作旨在监测疫苗的摄取和有效性，并使用常规临床和管理数据提供药物警戒。为了监控这些，可能需要进行汇总分析。然而,术语的变化是一个障碍，因为英格兰使用系统化的医学临床术语命名法(SNOMEDCT)，而英国其他地区在初级保健中使用Readv2术语。整个英国的数据源可用性并不统一。
目的：本研究旨在使用观察性医学结果伙伴关系（OMOP）通用数据模型（CDM）中的概念映射来识别记录的常见概念，并在重复的横断面研究中报告这些概念。我们计划这样做是为了疫苗覆盖率和2个关注的不良事件（AEIs），脑静脉窦血栓形成（CVST）和过敏反应。我们确定了概念映射到SNOMEDCT，阅读v2，世界卫生组织的国际疾病分类第十次修订（ICD-10）术语，和英国药品和器械词典(dm+d)。
方法：选择药物警戒研究对DaC-VaP感兴趣的暴露和结果。从观察健康数据科学和信息学（OHDSI）自动术语协调中确定了这些变量与英国权力下放国家/地区使用的不同术语的映射，Extraction,和NormalizationforAnalytics(ATHENA)在线浏览器。然后，来自每个国家的首席分析师确认或添加到确定的映射中。然后使用这些映射以通用格式报告AEI。我们报告了每28天接种疫苗后0-2天和3-28天的窗口率。
结果：我们列出了Readv2、SNOMEDCT、ICD-10和dm+d。对于疫苗暴露，我们发现了从OMOP到我们临床术语的清晰映射，尽管dm+d在搜索时OMOP没有列出代码。我们找到了一份CVST和过敏反应代码列表。对于CVST,我们必须使用更广泛的脑静脉血栓形成概念性方法,包括Readv2.我们识别了56个SNOMEDCT代码,其中我们选择了47个(84%)，和15读取v2代码。对于过敏反应，我们的精确搜索确定了60个SNOMEDCT代码和9个Readv2代码，其中我们选择了10个(17%)和4个(44%)，分别,纳入我们重复的横断面研究。
结论：这种方法可以在OMOPCDM中使用不同术语的映射，而无需对整个数据库进行编目。然而,阅读v2的概念粒度比某些术语小，比如SNOMEDCT。此外,OMOPCDM无法弥补临床编码系统的局限性.Readv2和ICD-10的粒度都不足以使CVST能够被特别标记。因此，任何汇总分析都必须在脑血管静脉血栓形成的特异性较低的水平上进行.总的来说,此CDM内的映射是有用的，和我们的方法可以用于快速合作，只有有限数量的概念池。
目的：本研究旨在使用观察性医学结果伙伴关系（OMOP）通用数据模型（CDM）中的概念映射来识别记录的常见概念，并在重复的横断面研究中报告这些概念。
方法：选择药物警戒研究对DaC-VaP感兴趣的暴露和结果。
结果：我们列出了Readv2、SNOMEDCT、
结论：这种方法可以在OMOPCDM中使用不同术语的映射，而无需对整个数据库进行编目。
