Johnson M, Hounkpatin H, Fraser S, Culliford D, Uniacke M, Roderick P
NIHR CLAHRC Wessex Methodological Hub, Faculty of Health Sciences, University of Southampton, Southampton, UK.
NIHR CLAHRC Wessex, Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton, UK.
BMC Med Inform Decis Mak. 2017 Jul 11;17(1):106. doi: 10.1186/s12911-017-0503-8.
NHS England has mandated the use in hospital laboratories of an automated early warning algorithm to create a consistent method for the detection of acute kidney injury (AKI). It generates an 'alert' based on changes in serum creatinine level to notify attending clinicians of a possible incident case of the condition, and to provide an assessment of its severity. We aimed to explore the feasibility of secondary data analysis to reproduce the algorithm outside of the hospital laboratory, and to describe the epidemiology of AKI across primary and secondary care within a region.
Using the Hampshire Health Record Analytical database, a patient-anonymised database linking primary care, secondary care and hospital laboratory data, we applied the algorithm to one year (1st January-31st December 2014) of retrospective longitudinal data. We developed database queries to modularise the collection of data from various sectors of the local health system, recreate the functions of the algorithm and undertake data cleaning.
Of a regional population of 642,337 patients, 176,113 (27.4%) had two or more serum creatinine test results available, with testing more common amongst older age groups. We identified 5361 (or 0.8%) with incident AKI indicated by the algorithm, generating a total of 13,845 individual AKI alerts. A cross-sectional assessment of each patient's first alert found that more than two-thirds of cases originated in the community, of which nearly half did not lead to a hospital admission.
It is possible to reproduce the algorithm using linked primary care, secondary care and hospital laboratory data, although data completeness, data quality and technical issues must be overcome. Linked data is essential to follow the significant proportion of people with AKI who transition from primary to secondary care, and can be used to assess clinical outcomes and the impact of interventions across the health system. This study emphasises that the development of data systems bridging across different sectors of the health and social care system can provide benefits for researchers, clinicians, healthcare providers and commissioners.
英国国民医疗服务体系（NHS）英格兰地区已强制要求医院实验室使用自动早期预警算法，以创建一种一致的方法来检测急性肾损伤（AKI）。该算法根据血清肌酐水平的变化生成“警报”，以通知主治医生可能出现的该病症病例，并对其严重程度进行评估。我们旨在探讨二次数据分析在医院实验室之外重现该算法的可行性，并描述一个地区内初级和二级医疗保健中AKI的流行病学情况。
利用汉普郡健康记录分析数据库，这是一个将初级医疗、二级医疗和医院实验室数据相链接的患者匿名数据库，我们将该算法应用于一年（2014年1月1日至12月31日）的回顾性纵向数据。我们开发了数据库查询，以模块化方式从当地卫生系统的各个部门收集数据，重现算法的功能并进行数据清理。
在该地区642,337名患者中，176,113名（27.4%）有两项或更多血清肌酐检测结果，检测在老年人群中更为常见。我们通过算法识别出5361名（或0.8%）新发AKI患者，共生成13,845条个体AKI警报。对每位患者首次警报的横断面评估发现，超过三分之二的病例起源于社区，其中近一半未导致住院治疗。
使用链接的初级医疗、二级医疗和医院实验室数据重现该算法是可行的，尽管必须克服数据完整性、数据质量和技术问题。链接数据对于追踪从初级医疗过渡到二级医疗的相当一部分AKI患者至关重要，并且可用于评估临床结果以及整个卫生系统中干预措施的影响。这项研究强调，跨卫生和社会护理系统不同部门的数据系统开发可为研究人员、临床医生、医疗保健提供者和专员带来益处。
