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

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          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12911-017-0503-8) contains supplementary material, which is available to authorized users.

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          The definition of acute kidney injury and its use in practice

          Mark E. Thomas, Caroline Blaine, Anne Dawnay … (2015)
          Acute kidney injury (AKI) is a common syndrome that is independently associated with increased mortality. A standardized definition is important to facilitate clinical care and research. The definition of AKI has evolved rapidly since 2004, with the introduction of the Risk, Injury, Failure, Loss, and End-stage renal disease (RIFLE), AKI Network (AKIN), and Kidney Disease Improving Global Outcomes (KDIGO) classifications. RIFLE was modified for pediatric use (pRIFLE). They were developed using both evidence and consensus. Small rises in serum creatinine are independently associated with increased mortality, and hence are incorporated into the current definition of AKI. The recent definition from the international KDIGO guideline merged RIFLE and AKIN. Systematic review has found that these definitions do not differ significantly in their performance. Health-care staff caring for children or adults should use standard criteria for AKI, such as the pRIFLE or KDIGO definitions, respectively. These efforts to standardize AKI definition are a substantial advance, although areas of uncertainty remain. The new definitions have enabled the use of electronic alerts to warn clinicians of possible AKI. Novel biomarkers may further refine the definition of AKI, but their use will need to produce tangible improvements in outcomes and cost effectiveness. Further developments in AKI definitions should be informed by research into their practical application across health-care providers. This review will discuss the definition of AKI and its use in practice for clinicians and laboratory scientists.
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            Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury.

            Edward Siew, Michael E. Matheny, T Alp Ikizler … (2010)
            Studies of acute kidney injury usually lack data on pre-admission kidney function and often substitute an inpatient or imputed serum creatinine as an estimate for baseline renal function. In this study, we compared the potential error introduced by using surrogates such as (1) an estimated glomerular filtration rate of 75 ml/min per 1.73 m(2) (suggested by the Acute Dialysis Quality Initiative), (2) a minimum inpatient serum creatinine value, and (3) the first admission serum creatinine value, with values computed using pre-admission renal function. The study covered a 12-month period and included a cohort of 4863 adults admitted to the Vanderbilt University Hospital. Use of both imputed and minimum baseline serum creatinine values significantly inflated the incidence of acute kidney injury by about half, producing low specificities of 77-80%. In contrast, use of the admission serum creatinine value as baseline significantly underestimated the incidence by about a third, yielding a low sensitivity of 39%. Application of any surrogate marker led to frequent misclassification of patient deaths after acute kidney injury and differences in both in-hospital and 60-day mortality rates. Our study found that commonly used surrogates for baseline serum creatinine result in bi-directional misclassification of the incidence and prognosis of acute kidney injury in a hospital setting.
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              The use of routinely collected computer data for research in primary care: opportunities and challenges.

              Simon de Lusignan, Chris Weel (2006)
              Routinely collected primary care data has underpinned research that has helped define primary care as a specialty. In the early years of the discipline, data were collected manually, but digital data collection now makes large volumes of data readily available. Primary care informatics is emerging as an academic discipline for the scientific study of how to harness these data. This paper reviews how data are stored in primary care computer systems; current use of large primary care research databases; and, the opportunities and challenges for using routinely collected primary care data in research. (1) Growing volumes of routinely recorded data. (2) Improving data quality. (3) Technological progress enabling large datasets to be processed. (4) The potential to link clinical data in family practice with other data including genetic databases. (5) An established body of know-how within the international health informatics community. (1) Research methods for working with large primary care datasets are limited. (2) How to infer meaning from data. (3) Pace of change in medicine and technology. (4) Integrating systems where there is often no reliable unique identifier and between health (person-based records) and social care (care-based records-e.g. child protection). (5) Achieving appropriate levels of information security, confidentiality, and privacy. Routinely collected primary care computer data, aggregated into large databases, is used for audit, quality improvement, health service planning, epidemiological study and research. However, gaps exist in the literature about how to find relevant data, select appropriate research methods and ensure that the correct inferences are drawn.
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                Author and article information

                Contributors
                M. Johnson:
                ORCID: http://orcid.org/0000-0001-5597-6615
                Matt.Johnson@soton.ac.uk
                H. Hounkpatin: H.O.Hounkpatin@soton.ac.uk
                S. Fraser: S.Fraser@soton.ac.uk
                D. Culliford: D.J.Culliford@soton.ac.uk
                M. Uniacke: Mark.Uniacke@porthosp.nhs.uk
                P. Roderick: pjr@soton.ac.uk
                Journal
                Journal ID (nlm-ta): BMC Med Inform Decis Mak
                Journal ID (iso-abbrev): BMC Med Inform Decis Mak
                Title: BMC Medical Informatics and Decision Making
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1472-6947
                Publication date (Electronic): 11 July 2017
                Publication date PMC-release: 11 July 2017
                Publication date Collection: 2017
                Volume: 17
                Electronic Location Identifier: 106
                Affiliations
                [1 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, , NIHR CLAHRC Wessex Methodological Hub, Faculty of Health Sciences, University of Southampton, ; Southampton, UK
                [2 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, , NIHR CLAHRC Wessex, Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, ; Southampton, UK
                [3 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, , Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, ; Southampton, UK
                [4 ]ISNI 0000 0004 0392 0072, GRID grid.415470.3, , Wessex Kidney Centre, Queen Alexandra Hospital, ; Portsmouth, UK
                Author information
                http://orcid.org/0000-0001-5597-6615
                Article
                Publisher ID: 503
                DOI: 10.1186/s12911-017-0503-8
                PMC ID: 5504785
                SO-VID: ef8f3808-011b-4803-938e-19cfd34f87bc
                Copyright © © The Author(s). 2017
                License:

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 21 February 2017
                Date accepted : 30 June 2017
                Funding
                Funded by: NIHR CLAHRC Wessex (GB)
                Categories
                Subject: Research Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2017

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: acute kidney injury,epidemiology,nhs aki algorithm,linked data
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: acute kidney injury, epidemiology, nhs aki algorithm, linked data

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