Abstract
Background Early recognition of acute kidney injury (AKI) is important. It frequently develops first in the community. KDIGO-based AKI e-alert criteria may help clinicians recognize AKI in hospitals, but their suitability for application in the community is unknown.
Methods In a large renal cohort (n = 50 835) in one UK health authority, we applied the NHS England AKI ‘e-alert’ criteria to identify and follow three AKI groups: hospital-acquired AKI (HA-AKI), community-acquired AKI admitted to hospital within 7 days (CAA-AKI) and community-acquired AKI not admitted within 7 days (CANA-AKI). We assessed how AKI criteria operated in each group, based on prior blood tests (number and time lag). We compared 30-day, 1- and 5-year mortality, 90-day renal recovery and chronic renal replacement therapy (RRT).
Results In total, 4550 patients met AKI e-alert criteria, 61.1% (2779/4550) with HA-AKI, 22.9% (1042/4550) with CAA-AKI and 16.0% (729/4550) with CANA-AKI. The median number of days since last blood test differed between groups (1, 52 and 69 days, respectively). Thirty-day mortality was similar for HA-AKI and CAA-AKI, but significantly lower for CANA-AKI (24.2, 20.2 and 2.6%, respectively). Five-year mortality was high in all groups, but followed a similar pattern (67.1, 64.7 and 46.2%). Differences in 5-year mortality among those not admitted could be explained by adjusting for comorbidities and restricting to 30-day survivors (hazard ratio 0.91, 95% confidence interval 0.80–1.04, versus hospital AKI). Those with CANA-AKI (versus CAA-AKI) had greater non-recovery at 90 days (11.8 versus 3.5%, P < 0.001) and chronic RRT at 5 years (3.7 versus 1.2%, P < 0.001).
Conclusions KDIGO-based AKI criteria operate differently in hospitals and in the community. Some patients may not require immediate admission but are at substantial risk of a poor long-term outcome.
Original language | English |
---|---|
Pages (from-to) | 922-929 |
Number of pages | 8 |
Journal | Nephrology Dialysis Transplantation |
Volume | 31 |
Issue number | 6 |
Early online date | 7 Apr 2016 |
DOIs | |
Publication status | Published - Jun 2016 |
Bibliographical note
ACKNOWLEDGEMENTSWe acknowledge the data management support of Grampian Data Safe Haven (DaSH) and the associated financial support of NHS Research Scotland, through NHS Grampian investment in the Grampian DaSH. 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).
Keywords
- acute kidney injury
- delivery of health care
- epidemiology
- primary health care
- survival analysis
Fingerprint
Dive into the research topics of 'KDIGO-based acute kidney injury criteria operate differently in hospitals and the community—findings from a large population cohort'. Together they form a unique fingerprint.Profiles
-
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
-
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