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.
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 language | English |
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Article number | 0131909 |
Number of pages | 11 |
Journal | PloS ONE |
Volume | 10 |
Issue number | 6 |
Early online date | 30 Jun 2015 |
DOIs | |
Publication status | Published - 30 Jun 2015 |
Bibliographical note
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 creditedData 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/grampian-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).
Keywords
- acute kidney injury
- diagnosis
- mass screening
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Corri Black
- School of Medicine, Medical Sciences & Nutrition, Aberdeen Centre for Health Data Science
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Personal Chair (Clinical)
- School of Medicine, Medical Sciences & Nutrition, Grampian Data Safe Haven (DaSH)
- School of Medicine, Medical Sciences & Nutrition, Chronic Disease Research Group
- School of Medicine, Medical Sciences & Nutrition, Farr Aberdeen
Person: Clinical Academic
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Simon Sawhney
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Senior Clinical Lecturer
- School of Medicine, Medical Sciences & Nutrition, Aberdeen Centre for Health Data Science
- School of Medicine, Medical Sciences & Nutrition, Farr Aberdeen
- School of Medicine, Medical Sciences & Nutrition, Grampian Data Safe Haven (DaSH)
Person: Clinical Academic
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Grampian Data Safe Haven (DaSH)
Katie Wilde (Manager)
Institute of Applied Health SciencesResearch Facilities: Facility