TY -的盟Delanerolle Gayathri AU -威廉姆斯,罗伯特•AU - Stipancic安娜AU - Byford,瑞秋盟——《福布斯》,安娜盟——曾荫权,Ruby S M AU - Anand, Sneha N AU -布拉德利,Declan AU -墨菲,西沃恩·AU -阿克巴里,阿什利盟——Bedston斯图尔特AU -里昂,罗南AU -欧文,里安农AU - Torabi,今天非盟-贝格斯说,吉利安盟——啧,安东尼盟——Balharry多米尼克盟——快乐,马克盟——酋长,阿齐兹AU -霍布斯,F D理查德AU - de LusignanSimon PY - 2022 DA - 2022/8/22 TI -使用COVID-19疫苗摄取和重要不良事件的通用数据模型的方方法问题:可行性研究的数据和连接COVID-19疫苗药物警戒在英国的乔- Res JMIR形式SP - e37821六世- 6 - 8 KW -系统化的医学术语千瓦COVID-19疫苗KW - COVID-19千瓦窦血栓KW -过敏KW -药物警戒KW -疫苗吸收KW -医疗结果KW -临床编码系统KW -健康数据库KW -健康信息KW -临床结果KW -疫苗效果KW -数据模型AB -背景:英国范围内的COVID-19疫苗药物警戒(DaC-VaP)合作旨在监测疫苗的使用和有效性，并使用常规临床和管理数据提供药物警戒。为了监控这些，可能需要合并分析。然而，术语的变化带来了一个障碍，因为英国使用医学临床术语系统命名法(SNOMED CT)，而英国其他地区在初级保健中使用Read v2术语。数据来源的可用性在英国各地并不统一。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。我们计划这样做的疫苗覆盖率和2个不良事件(AEIs)，脑静脉窦血栓形成(CVST)和过敏反应。我们确定了SNOMED CT、Read v2、世界卫生组织国际疾病分类第十版(ICD-10)术语以及英国药物和设备词典(dm+d)的概念映射。 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. SN - 2561-326X UR - https://formative.www.mybigtv.com/2022/8/e37821 UR - https://doi.org/10.2196/37821 UR - http://www.ncbi.nlm.nih.gov/pubmed/35786634 DO - 10.2196/37821 ID - info:doi/10.2196/37821 ER -