MacRae Clare, Whittaker Hannah, Mukherjee Mome, Daines Luke, Morgan Ann, Iwundu Chukwuma, Alsallakh Mohammed, Vasileiou Eleftheria, O'Rourke Eimear, Williams Alexander T, Stone Philip W, Sheikh Aziz, Quint Jennifer K
Usher Institute, University of Edinburgh, Edinburgh, UK.
National Heart and Lung Institute, Imperial College London, London, UK.
Pragmat Obs Res. 2022 Feb 16;13:1-8. doi: 10.2147/POR.S353400. eCollection 2022.
Electronic health record (EHR) databases provide rich, longitudinal data on interactions with healthcare providers and can be used to advance research into respiratory conditions. However, since these data are primarily collected to support health care delivery, clinical coding can be inconsistent, resulting in inherent challenges in using these data for research purposes.
We systematically searched existing international literature and UK code repositories to find respiratory disease codelists for asthma from January 2018, and chronic obstructive pulmonary disease and respiratory tract infections from January 2020, based on prior searches. Medline searches using key terms provided in article lists. Full-text articles, supplementary files, and reference lists were examined for codelists, and codelists repositories were searched. A reproducible methodology for codelists creation was developed with recommended lists for each disease created based on multidisciplinary expert opinion and previously published literature.
Medline searches returned 1126 asthma articles, 70 COPD articles, and 90 respiratory infection articles, with 3%, 22% and 5% including codelists, respectively. Repository searching returned 12 asthma, 23 COPD, and 64 respiratory infection codelists. We have systematically compiled respiratory disease codelists and from these derived recommended lists for use by researchers to find the most up-to-date and relevant respiratory disease codelists that can be tailored to individual research questions.
Few published papers include codelists, and where published diverse codelists were used, even when answering similar research questions. Whilst some advances have been made, greater consistency and transparency across studies using routine data to study respiratory diseases are needed.
电子健康记录（EHR）数据库提供了与医疗服务提供者互动的丰富纵向数据，可用于推进对呼吸道疾病的研究。然而，由于这些数据主要是为支持医疗服务而收集的，临床编码可能不一致，导致在将这些数据用于研究目的时存在内在挑战。
我们系统地检索了现有国际文献和英国代码库，根据之前的检索结果，查找2018年1月以来哮喘的呼吸道疾病代码列表，以及2020年1月以来慢性阻塞性肺疾病和呼吸道感染的代码列表。使用文章列表中提供的关键词在Medline中进行检索。检查全文文章、补充文件和参考文献列表以查找代码列表，并搜索代码列表库。开发了一种可重复的代码列表创建方法，根据多学科专家意见和先前发表的文献为每种疾病创建推荐列表。
Medline检索返回了1126篇哮喘文章、70篇慢性阻塞性肺疾病文章和90篇呼吸道感染文章，分别有3%、22%和5%包含代码列表。库搜索返回了12个哮喘、23个慢性阻塞性肺疾病和64个呼吸道感染代码列表。我们系统地编制了呼吸道疾病代码列表，并从中得出推荐列表，供研究人员使用，以找到可根据个别研究问题进行定制的最新和相关呼吸道疾病代码列表。
很少有已发表的论文包含代码列表，即使在回答类似研究问题时，已发表的论文使用的代码列表也各不相同。虽然已经取得了一些进展，但在使用常规数据研究呼吸道疾病的研究中，仍需要更大的一致性和透明度。
