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

Using a linked database for epidemiology across the primary and secondary care divide: acute kidney injury
Using a linked database for epidemiology across the primary and secondary care divide: acute kidney injury
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.
acute kidney injury, epidemiology, NHS AKI algorithm, linked data
Johnson, Matthew James
d272ca76-f017-4457-96f5-daf6a7af6adf
Hounkpatin, Hilda
5612e5b4-6286-48c8-b81f-e96d1148681d
Fraser, Simon
135884b6-8737-4e8a-a98c-5d803ac7a2dc
Culliford, David
25511573-74d3-422a-b0ee-dfe60f80df87
Uniacke, Mark
97710d53-5941-41c9-8ade-c973ecc63905
Roderick, Paul
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Johnson, Matthew James
d272ca76-f017-4457-96f5-daf6a7af6adf
Hounkpatin, Hilda
5612e5b4-6286-48c8-b81f-e96d1148681d
Fraser, Simon
135884b6-8737-4e8a-a98c-5d803ac7a2dc
Culliford, David
25511573-74d3-422a-b0ee-dfe60f80df87
Uniacke, Mark
97710d53-5941-41c9-8ade-c973ecc63905
Roderick, Paul
dbb3cd11-4c51-4844-982b-0eb30ad5085a

Johnson, Matthew James, Hounkpatin, Hilda, Fraser, Simon, Culliford, David, Uniacke, Mark and Roderick, Paul (2017) Using a linked database for epidemiology across the primary and secondary care divide: acute kidney injury. BMC Medical Informatics and Decision Making, 17 (106). (doi:10.1186/s12911-017-0503-8).

Record type: Article

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.

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Accepted/In Press date: 30 June 2017
e-pub ahead of print date: 11 July 2017
Published date: 11 July 2017
Keywords: acute kidney injury, epidemiology, NHS AKI algorithm, linked data

Identifiers

Local EPrints ID: 412598
URI: http://eprints.soton.ac.uk/id/eprint/412598
PURE UUID: 0e16da41-d247-4ea1-b03b-46eb087b1ebc
ORCID for Simon Fraser: ORCID iD orcid.org/0000-0002-4172-4406
ORCID for David Culliford: ORCID iD orcid.org/0000-0003-1663-0253
ORCID for Paul Roderick: ORCID iD orcid.org/0000-0001-9475-6850

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Date deposited: 24 Jul 2017 16:32
Last modified: 22 Nov 2021 02:56

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Contributors

Author: Matthew James Johnson
Author: Simon Fraser ORCID iD
Author: David Culliford ORCID iD
Author: Mark Uniacke
Author: Paul Roderick ORCID iD

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