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

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
4 tweeters
facebook
1 Facebook page

Citations

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2 Dimensions

Readers on

mendeley
24 Mendeley
Title
Using a linked database for epidemiology across the primary and secondary care divide: acute kidney injury
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0503-8
Pubmed ID
Authors

M. Johnson, H. Hounkpatin, S. Fraser, D. Culliford, M. Uniacke, P. Roderick

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 33%
Other 3 13%
Student > Master 3 13%
Student > Ph. D. Student 2 8%
Student > Postgraduate 2 8%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Medicine and Dentistry 10 42%
Psychology 3 13%
Agricultural and Biological Sciences 2 8%
Nursing and Health Professions 2 8%
Computer Science 1 4%
Other 2 8%
Unknown 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 July 2017.
All research outputs
#7,130,952
of 13,595,689 outputs
Outputs from BMC Medical Informatics and Decision Making
#592
of 1,224 outputs
Outputs of similar age
#108,463
of 263,164 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 9 outputs
Altmetric has tracked 13,595,689 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,224 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,164 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.