Acute kidney injury-how does automated detection perform?
BACKGROUND
Early detection of acute kidney injury (AKI) is important for safe clinical practice. NHS England is implementing a nationwide automated AKI detection system based on changes in blood creatinine. Little has been reported on the similarities and differences of AKI patients detected by this algorithm and other definitions of AKI in the literature.


METHODS
We assessed the NHS England AKI algorithm and other definitions using routine biochemistry in our own health authority in Scotland in 2003 (adult population 438 332). Linked hospital episode codes (ICD-10) were used to identify patients where AKI was a major clinical diagnosis. We compared how well the algorithm detected this subset of AKI patients in comparison to other definitions of AKI. We also evaluated the potential 'alert burden' from using the NHS England algorithm in comparison to other AKI definitions.


RESULTS
Of 127 851 patients with at least one blood test in 2003, the NHS England AKI algorithm identified 5565 patients. The combined NHS England algorithm criteria detected 91.2% (87.6-94.0) of patients who had an ICD-10 AKI code and this was better than any individual AKI definition. Some of those not captured could be identified by algorithm modifications to identify AKI in retrospect after recovery, but this would not be practical in real-time. Any modifications also increased the number of alerted patients (2-fold in the most sensitive model).


CONCLUSIONS
The NHS England AKI algorithm performs well as a diagnostic adjunct in clinical practice. In those without baseline data, AKI may only be seen in biochemistry in retrospect, therefore proactive clinical care remains essential. An alternative algorithm could increase the diagnostic sensitivity, but this would also produce a much greater burden of patient alerts.
Division of Applied Renal Research Collaboration, University of Aberdeen, Aberdeen, UK NHS Grampian, Aberdeen, UK.
Simon
S
Sawhney
NHS Grampian, Aberdeen, UK.
Nick
N
Fluck
Division of Applied Renal Research Collaboration, University of Aberdeen, Aberdeen, UK NHS Grampian, Aberdeen, UK.
Angharad
A
Marks
Division of Applied Renal Research Collaboration, University of Aberdeen, Aberdeen, UK.
Gordon
G
Prescott
NHS Grampian, Aberdeen, UK.
William
W
Simpson
London School of Hygiene and Tropical Medicine, London, UK.
Laurie
L
Tomlinson
Division of Applied Renal Research Collaboration, University of Aberdeen, Aberdeen, UK NHS Grampian, Aberdeen, UK.
Corri
C
Black
D058186:Acute Kidney Injury / Q000175:diagnosis* / Q000453:epidemiology*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D007677:Kidney Function Tests; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D012606:Scotland / Q000453:epidemiology
acute kidney injury; diagnosis; epidemiology; screening