Infectious Diseases, Townsville Hospital, Townsville, Queensland, Australia.
College of Medicine and Dentistry, Division of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia.
BMJ Open. 2020 Mar 18;10(3):e034845. doi: 10.1136/bmjopen-2019-034845.
To design a linked hospital database using administrative and clinical information to describe associations that predict infectious diseases outcomes, including long-term mortality.
A retrospective cohort of Townsville Hospital inpatients discharged with an International Classification of Diseases and Related Health Problems 10th Revision Australian Modification code for an infectious disease between 1 January 2006 and 31 December 2016 was assembled. This used linked anonymised data from: hospital administrative sources, diagnostic pathology, pharmacy dispensing, public health and the National Death Registry. A Created Study ID was used as the central identifier to provide associations between the cohort patients and the subsets of granular data which were processed into a relational database. A web-based interface was constructed to allow data extraction and evaluation to be performed using editable Structured Query Language.
The database has linked information on 41 367 patients with 378 487 admissions and 1 869 239 diagnostic/procedure codes. Scripts used to create the database contents generated over 24 000 000 database rows from the supplied data. Nearly 15% of the cohort was identified as Aboriginal or Torres Strait Islanders. Invasive staphylococcal, pneumococcal and Group A streptococcal infections and influenza were common in this cohort. The most common comorbidities were smoking (43.95%), diabetes (24.73%), chronic renal disease (17.93%), cancer (16.45%) and chronic pulmonary disease (12.42%). Mortality over the 11-year period was 20%.
This complex relational database reutilising hospital information describes a cohort from a single tropical Australian hospital of inpatients with infectious diseases. In future analyses, we plan to explore analyses of risks, clinical outcomes, healthcare costs and antimicrobial side effects in site and organism specific infections.
设计一个使用管理和临床信息的关联医院数据库，以描述预测传染病结果的关联因素，包括长期死亡率。
收集了 2006 年 1 月 1 日至 2016 年 12 月 31 日期间因传染病而在汤斯维尔医院出院的患者的回顾性队列，他们的国际疾病分类和相关健康问题第十次修订澳大利亚修改版代码为传染病。该队列使用来自医院管理来源、诊断病理学、药房配药、公共卫生和国家死亡登记处的链接匿名数据。使用创建的研究 ID 作为中央标识符，以提供队列患者与细粒度数据子集之间的关联，这些数据子集被处理成关系数据库。构建了一个基于网络的接口，允许使用可编辑的结构化查询语言进行数据提取和评估。
该数据库链接了 41367 名患者的信息，这些患者有 378487 次住院和 1869239 次诊断/手术代码。用于创建数据库内容的脚本从提供的数据中生成了超过 2400 万行数据库行。该队列中近 15%的人被认定为土著或托雷斯海峡岛民。侵袭性葡萄球菌、肺炎球菌和 A 组链球菌感染和流感在该队列中很常见。最常见的合并症是吸烟（43.95%）、糖尿病（24.73%）、慢性肾功能不全（17.93%）、癌症（16.45%）和慢性肺部疾病（12.42%）。在 11 年期间的死亡率为 20%。
这个复杂的关系数据库重新利用医院信息，描述了来自澳大利亚热带地区的一家医院的传染病住院患者的队列。在未来的分析中，我们计划探索针对特定部位和特定病原体感染的风险、临床结果、医疗保健成本和抗菌药物副作用的分析。
