Epidemiology of AKI: Utilizing Large Databases to Determine the Burden of AKI

Simon Sawhney, Simon D Fraser

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Large observational databases linking kidney function and other routine patient health data are increasingly being used to study acute kidney injury (AKI). Routine health care data show an apparent rise in the incidence of population AKI and an increase in acute dialysis. Studies also report an excess in mortality and adverse renal outcomes after AKI, although with variation depending on AKI severity, baseline, definition of renal recovery, and the time point during follow-up. However, differences in data capture, AKI awareness, monitoring, recognition, and clinical practice make comparisons between health care settings and periods difficult. In this review, we describe the growing role of large databases in determining the incidence and prognosis of AKI and evaluating initiatives to improve the quality of care in AKI. Using examples, we illustrate this use of routinely collected health data and discuss the strengths, limitations, and implications for researchers and clinicians.
Original languageEnglish
Pages (from-to)194-204
Number of pages11
JournalAdvances in Chronic Kidney Disease
Issue number4
Publication statusPublished - 1 Jul 2017

Bibliographical note

S.S. is supported by a Clinical Research Training Fellowship from the Wellcome Trust (Ref 102729/Z/13/Z). We also acknowledge the support from The Farr Institute of Health Informatics Research. The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly government), the Chief Scientist Office (Scottish government Health Directorates), and the Wellcome Trust (MRC grant nos: Scotland MR/K007017/1). The funders of this study had no role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.


  • acute kidney injury
  • incidence
  • prognosis
  • big-data
  • quality improvement


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