NDLI: Using a linked database for epidemiology across the primary and secondary care divide: acute kidney injury

Content Provider	Springer Nature : BioMed Central
Author	Johnson, M. Hounkpatin, H. Fraser, S. Culliford, D. Uniacke, M. Roderick, P.
Abstract	Background 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. Methods 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. Results 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. Conclusion 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.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-017-0503-8.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-017-0503-8
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	17
Language	English
Publisher	BioMed Central
Publisher Date	2017-07-11
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Acute kidney injury Epidemiology NHS AKI algorithm Linked data
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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