COMPLETED: Using routinely collected information to look at the impact of Acute Kidney Injury (AKI)

Theme 4

Public Health and Primary Care

Research aims and questions

Hospital laboratories use an automated early warning algorithm for the detection of acute kidney injury (AKI). It generates an ‘alert’ to notify 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.

Start date: 31/01/2015 End date: 31/03/2019


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

Predicting Risk of Recurrent Acute Kidney Injury: A Systematic Review.


We have demonstrated the feasibility of using linked data from hospital and community settings to reproduce the NHS England automated early warning algorithm, having identified the incidence and characteristics of both HA-AKI and CA-AKI in a regional population.
Considerable effort was required to overcome technical and data quality issues but having worked as a multidisciplinary team drawing upon informatics and clinical expertise was an advantage, allowing us to investigate and make clinical judgments based upon ‘real-world’ data rather than relying upon application of standard clinical rules.

This study underlines the potential benefits of using linked data to research the epidemiology of a condition that frequently crosses the primary/secondary care divide, but also highlights issues around data sharing, data quality and system interoperability, and the wider benefits of developing healthcare data systems that can bridge across different sectors of the health and social care system.
In AKI, linked data could represent an important regional complement to the clinical use of the algorithm but could also be instrumental in the developmentand validation of tools to predict risk in the general population, thus demonstrating the value that informatics can bring to the overall aims of high quality clinical management of AKI and its prevention in those at high risk of the condition.

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Dr David Culliford

Dr Julie Parkes

Hilda Hounkpatin, PhD, MPH

Matt Johnson

Professor Paul Roderick

Hampshire Hospitals NHS Foundation Trust

NHS Fareham and Gosport CCG

NHS North East Hampshire and Farnham CCG

NHS North Hampshire CCG

NHS Portsmouth CCG

NHS South Eastern Hampshire CCG

NHS Southampton City CCG

NHS West Hampshire CCG

University Hospital Southampton NHS Foundation Trust

University of Southampton

The Hampshire Acute Kidney Injury Study Protocol