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Acute kidney injury-how does automated detection perform?
Simon Sawhney1, Nick Fluck2, Angharad Marks1
1Division of Applied Renal Research Collaboration, University of Aberdeen, Aberdeen, UK NHS Grampian, Aberdeen, UK.
Summary
The NHS England acute kidney injury (AKI) algorithm effectively detects AKI patients using blood creatinine, outperforming individual definitions. However, proactive care is still essential for early AKI detection.
Area of Science:
- Nephrology
- Clinical Biochemistry
- Health Informatics
Background:
- Early detection of acute kidney injury (AKI) is crucial for patient safety.
- NHS England is implementing an automated AKI detection system using blood creatinine levels.
- Limited data exists on how this algorithm compares to other AKI definitions.
Purpose of the Study:
- To evaluate the NHS England AKI algorithm against other definitions.
- To assess its performance in detecting AKI patients within a Scottish health authority.
- To compare the potential alert burden of different AKI detection methods.
Main Methods:
- Routine biochemistry data from 2003 for 438,332 adults were analyzed.
- Linked ICD-10 codes identified patients with AKI as a major diagnosis.
- The NHS England algorithm's detection rate was compared to other AKI definitions.
Main Results:
- The NHS England algorithm identified 5565 AKI patients out of 127,851 with blood tests.
- It detected 91.2% of ICD-10 coded AKI cases, surpassing individual definitions.
- Modifications for retrospective detection improved sensitivity but increased alert burden twofold.
Conclusions:
- The NHS England AKI algorithm is a valuable diagnostic aid.
- Proactive clinical care is vital, especially when baseline data is unavailable.
- Alternative algorithms may enhance sensitivity but significantly increase alert burden.
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