Maximising acute kidney injury alerts: a cross-sectional comparison with the clinical diagnosis

Simon Sawhney, Angharad Marks, Tariq Ali, Laura Clark, Nick Fluck, Gordon J Prescott, William G. Simpson, Corri Black

Research output: Contribution to journalArticle

12 Citations (Scopus)
6 Downloads (Pure)

Abstract

Background

Acute kidney injury (AKI) is serious and widespread across healthcare (1 in 7 hospital admissions) but recognition is often delayed causing avoidable harm. Nationwide automated biochemistry alerts for AKI stages 1-3 have been introduced in England to improve recognition. We explored how these alerts compared with clinical diagnosis in different hospital settings.

Methods

We used a large population cohort of 4464 patients with renal impairment. Each patient had case-note review by a nephrologist, using RIFLE criteria to diagnose AKI and chronic kidney disease (CKD). We identified and staged AKI alerts using the new national NHS England AKI algorithm and compared this with nephrologist diagnosis across hospital settings.

Results

Of 4464 patients, 525 had RIFLE AKI, 449 had mild AKI, 2185 had CKD (without AKI) and 1305 were of uncertain chronicity. NHS AKI algorithm criteria alerted for 90.5% of RIFLE AKI, 72.4% of mild AKI, 34.1% of uncertain cases and 14.0% of patients who actually had CKD.The algorithm identified AKI particularly well in intensive care (95.5%) and nephrology (94.6%), but less well on surgical wards (86.4%). Restricting the algorithm to stage 2 and 3 alerts reduced the over-diagnosis of AKI in CKD patients from 14.0% to 2.1%, but missed or delayed alerts in two-thirds of RIFLE AKI patients.

Conclusion

Automated AKI detection performed well across hospital settings, but was less sensitive on surgical wards. Clinicians should be mindful that restricting alerts to stages 2-3 may identify fewer CKD patients, but including stage 1 provides more sensitive and timely alerting.
Original languageEnglish
Article number0131909
Number of pages11
JournalPloS ONE
Volume10
Issue number6
Early online date30 Jun 2015
DOIs
Publication statusPublished - 30 Jun 2015

Fingerprint

Acute Kidney Injury
kidneys
kidney diseases
Chronic Renal Insufficiency
England
Biochemistry
Kidney
Nephrology
Critical Care
biochemistry
health services
Delivery of Health Care
Wounds and Injuries
Population

Keywords

  • acute kidney injury
  • diagnosis
  • mass screening

Cite this

Maximising acute kidney injury alerts : a cross-sectional comparison with the clinical diagnosis . / Sawhney, Simon; Marks, Angharad; Ali, Tariq; Clark, Laura; Fluck, Nick; Prescott, Gordon J; Simpson, William G.; Black, Corri.

In: PloS ONE, Vol. 10, No. 6, 0131909, 30.06.2015.

Research output: Contribution to journalArticle

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abstract = "BackgroundAcute kidney injury (AKI) is serious and widespread across healthcare (1 in 7 hospital admissions) but recognition is often delayed causing avoidable harm. Nationwide automated biochemistry alerts for AKI stages 1-3 have been introduced in England to improve recognition. We explored how these alerts compared with clinical diagnosis in different hospital settings.MethodsWe used a large population cohort of 4464 patients with renal impairment. Each patient had case-note review by a nephrologist, using RIFLE criteria to diagnose AKI and chronic kidney disease (CKD). We identified and staged AKI alerts using the new national NHS England AKI algorithm and compared this with nephrologist diagnosis across hospital settings.ResultsOf 4464 patients, 525 had RIFLE AKI, 449 had mild AKI, 2185 had CKD (without AKI) and 1305 were of uncertain chronicity. NHS AKI algorithm criteria alerted for 90.5{\%} of RIFLE AKI, 72.4{\%} of mild AKI, 34.1{\%} of uncertain cases and 14.0{\%} of patients who actually had CKD.The algorithm identified AKI particularly well in intensive care (95.5{\%}) and nephrology (94.6{\%}), but less well on surgical wards (86.4{\%}). Restricting the algorithm to stage 2 and 3 alerts reduced the over-diagnosis of AKI in CKD patients from 14.0{\%} to 2.1{\%}, but missed or delayed alerts in two-thirds of RIFLE AKI patients.ConclusionAutomated AKI detection performed well across hospital settings, but was less sensitive on surgical wards. Clinicians should be mindful that restricting alerts to stages 2-3 may identify fewer CKD patients, but including stage 1 provides more sensitive and timely alerting.",
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author = "Simon Sawhney and Angharad Marks and Tariq Ali and Laura Clark and Nick Fluck and Prescott, {Gordon J} and Simpson, {William G.} and Corri Black",
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AU - Ali, Tariq

AU - Clark, Laura

AU - Fluck, Nick

AU - Prescott, Gordon J

AU - Simpson, William G.

AU - Black, Corri

N1 - Copyright: © 2015 Sawhney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: Participant level de-identified data used for this study are held by Grampian Data Safe Haven. These data are available provided the necessary permissions have been obtained. Depending on the nature of the request, this may include Caldicott guardian, NHS Grampian Research and Development, and local Research Ethics Committee approval. Further information is available at http://www.abdn.ac.uk/iahs/facilities/gr​ampian-data-safe-haven.php and requests for data may be made to Dr Corri Black on behalf of Grampian Data Safe Haven, corri.black@abdn.ac.uk. Funding: SS is supported by a Clinical Research Training Fellowship from the Wellcome Trust (Ref 102729/Z/13/Z).

PY - 2015/6/30

Y1 - 2015/6/30

N2 - BackgroundAcute kidney injury (AKI) is serious and widespread across healthcare (1 in 7 hospital admissions) but recognition is often delayed causing avoidable harm. Nationwide automated biochemistry alerts for AKI stages 1-3 have been introduced in England to improve recognition. We explored how these alerts compared with clinical diagnosis in different hospital settings.MethodsWe used a large population cohort of 4464 patients with renal impairment. Each patient had case-note review by a nephrologist, using RIFLE criteria to diagnose AKI and chronic kidney disease (CKD). We identified and staged AKI alerts using the new national NHS England AKI algorithm and compared this with nephrologist diagnosis across hospital settings.ResultsOf 4464 patients, 525 had RIFLE AKI, 449 had mild AKI, 2185 had CKD (without AKI) and 1305 were of uncertain chronicity. NHS AKI algorithm criteria alerted for 90.5% of RIFLE AKI, 72.4% of mild AKI, 34.1% of uncertain cases and 14.0% of patients who actually had CKD.The algorithm identified AKI particularly well in intensive care (95.5%) and nephrology (94.6%), but less well on surgical wards (86.4%). Restricting the algorithm to stage 2 and 3 alerts reduced the over-diagnosis of AKI in CKD patients from 14.0% to 2.1%, but missed or delayed alerts in two-thirds of RIFLE AKI patients.ConclusionAutomated AKI detection performed well across hospital settings, but was less sensitive on surgical wards. Clinicians should be mindful that restricting alerts to stages 2-3 may identify fewer CKD patients, but including stage 1 provides more sensitive and timely alerting.

AB - BackgroundAcute kidney injury (AKI) is serious and widespread across healthcare (1 in 7 hospital admissions) but recognition is often delayed causing avoidable harm. Nationwide automated biochemistry alerts for AKI stages 1-3 have been introduced in England to improve recognition. We explored how these alerts compared with clinical diagnosis in different hospital settings.MethodsWe used a large population cohort of 4464 patients with renal impairment. Each patient had case-note review by a nephrologist, using RIFLE criteria to diagnose AKI and chronic kidney disease (CKD). We identified and staged AKI alerts using the new national NHS England AKI algorithm and compared this with nephrologist diagnosis across hospital settings.ResultsOf 4464 patients, 525 had RIFLE AKI, 449 had mild AKI, 2185 had CKD (without AKI) and 1305 were of uncertain chronicity. NHS AKI algorithm criteria alerted for 90.5% of RIFLE AKI, 72.4% of mild AKI, 34.1% of uncertain cases and 14.0% of patients who actually had CKD.The algorithm identified AKI particularly well in intensive care (95.5%) and nephrology (94.6%), but less well on surgical wards (86.4%). Restricting the algorithm to stage 2 and 3 alerts reduced the over-diagnosis of AKI in CKD patients from 14.0% to 2.1%, but missed or delayed alerts in two-thirds of RIFLE AKI patients.ConclusionAutomated AKI detection performed well across hospital settings, but was less sensitive on surgical wards. Clinicians should be mindful that restricting alerts to stages 2-3 may identify fewer CKD patients, but including stage 1 provides more sensitive and timely alerting.

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