使用COVID-19疫苗摄取和重要不良事件的通用数据模型的方法学问题:卡塔尔世界杯8强波胆分析英国%A Delanerolle、Gayathri %A Williams、Robert %A Stipancic、Ana %A Byford、Rachel %A Forbes、Anna %A Tsang、Ruby S M %A Anand、Sneha N %A Bradley、Declan %A Murphy、Siobhán %A Akbari、Ashley %A Bedston、Stuart %A Lyons、Ronan A %A Owen、Rhiannon %A Torabi、Fatemeh %A Beggs、Jillian %A Chuter、Antony %A Balharry、Dominique %A Joy、Mark %A Sheikh、Aziz %A Hobbs、F D Richard %A de Lusignan、Simon %+ Nuffield部门牛津大学初级保健卫生科学学院,英国牛津沃尔顿路鹰屋,44 01865 617283,simon.delusignan@phc.ox.ac.uk %K医学系统化命名法%K COVID-19疫苗%K COVID-19 %K窦血栓形成%K过敏反应%K药物警戒%K疫苗服用%K医疗结果%K临床编码系统%K健康数据库%K健康信息%K临床结果%K疫苗效果%K数据模型%D 2022 %7 22.8.2022 %9原稿%J JMIR Form Res %G英文%X背景:建立了全英国范围内的COVID-19疫苗药物警戒(DaC-VaP)合作,以监测疫苗的使用和有效性,并利用常规临床和管理数据提供药物警戒。为了监视这些,可能需要合并分析。然而,术语的变化带来了一个障碍,因为英国使用的是医学临床术语系统化命名法(SNOMED CT),而英国其他地区在初级保健中使用的是Read v2术语。在英国,数据来源的可用性并不统一。目的:本研究旨在使用观察性医疗结果伙伴关系(OMOP)公共数据模型(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个不良事件(AEIs),脑静脉窦血栓形成(CVST)和过敏反应。 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. %M 35786634 %R 10.2196/37821 %U https://formative.www.mybigtv.com/2022/8/e37821 %U https://doi.org/10.2196/37821 %U http://www.ncbi.nlm.nih.gov/pubmed/35786634
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