@Article{info:doi/10.2196/37821,作者=“Delanerolle, Gayathri和Williams, Robert和Stipancic, Ana和Byford, Rachel和Forbes, Anna和Tsang, Ruby S M和Anand, Sneha N和Bradley, Declan和Murphy, Siobh a和Akbari, Ashley和Bedston, Stuart和Lyons, Ronan a和Owen, Rhiannon和Torabi, Fatemeh和Beggs, Jillian和Chuter, Antony和Balharry, Dominique和Joy, Mark和Sheikh, Aziz和Hobbs, F D Richard和de Lusignan, Simon”,标题=“使用COVID-19疫苗摄入和重要不良事件的通用数据模型的方法学问题:数据和连接性在英国COVID-19疫苗药物警戒的可行性研究”,期刊=“JMIR Form Res”,年=“2022”,月=“8”,日=“22”,卷=“6”,数=“8”,页=“e37821”,关键词=“医学系统命名法”;COVID-19疫苗;COVID-19;静脉窦血栓形成;速发型过敏反应;药物警戒;疫苗吸收;医疗结果;临床编码系统; health database; health information; clinical outcome; vaccine effect; data model", 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. ", issn="2561-326X", doi="10.2196/37821", url="https://formative.www.mybigtv.com/2022/8/e37821", url="https://doi.org/10.2196/37821", url="http://www.ncbi.nlm.nih.gov/pubmed/35786634" }
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