Identifying acute kidney injury in the community--a novel informatics approach

J Nephrol. 2016 Feb;29(1):93-8. doi: 10.1007/s40620-015-0190-4. Epub 2015 Mar 17.

Abstract

Background: Acute kidney injury (AKI) is a serious and common problem that is associated with high mortality. Currently nearly all efforts at improving outcomes in AKI have been focused on secondary care. We now know that a large number of patients most likely develop the condition in primary care. To our knowledge there has been no previous attempts to approach this topic from the primary care perspective.

Aim: To test the utility of novel informatics software to identify patients with AKI in the community.

Setting and method: We carried out a retrospective audit of patients in one urban practice in Leicestershire using novel informatics software. The audit data was run on two occasions, once for high-risk patients between 4th July 2010 through until 30th September 2013, and once for low risk patients for the period of 27th October 2011 through until 21st January 2014.

Results: During the period of the data collection the average practice list size was 12,420, with 235 and 19 AKI episodes in the high and low risk groups respectively. The annual AKI incidence was 27.9/1000 in the high-risk group, 1.22/1000 in the low risk group, and 10.6/1000 overall. The most common associated factor was sepsis in 170 patients, followed by dehydration in 54 patients.

Conclusion: We have shown it is possible to identify patients with AKI in the community using informatics software. Our data suggests that AKI in the community is much more common than previously thought and demonstrates the need to better understand this condition from the primary care perspective.

Keywords: Acute kidney injury; Medical informatics; Primary care.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acute Kidney Injury / diagnosis*
  • Acute Kidney Injury / epidemiology*
  • Adolescent
  • Adult
  • Age Distribution
  • Aged
  • Aged, 80 and over
  • Community Health Services*
  • Computational Biology / methods*
  • Data Mining
  • Databases, Factual
  • England / epidemiology
  • Female
  • General Practice
  • Humans
  • Incidence
  • Male
  • Middle Aged
  • Retrospective Studies
  • Risk Assessment
  • Risk Factors
  • Sex Distribution
  • Software
  • Time Factors
  • Urban Health
  • Urban Health Services*
  • Young Adult