| 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. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 50% |
| Chile | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 56 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 27% |
| Student > Ph. D. Student | 6 | 11% |
| Student > Bachelor | 6 | 11% |
| Student > Master | 4 | 7% |
| Student > Doctoral Student | 3 | 5% |
| Other | 8 | 14% |
| Unknown | 14 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 25% |
| Nursing and Health Professions | 7 | 13% |
| Social Sciences | 6 | 11% |
| Psychology | 4 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Other | 7 | 13% |
| Unknown | 16 | 29% |
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
#13,209,924
of 22,988,380 outputs
Outputs from BMC Medical Informatics and Decision Making
#928
of 2,003 outputs
Outputs of similar age
#151,898
of 312,560 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 43 outputs
Altmetric has tracked 22,988,380 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,003 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 52% of its peers.
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We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.