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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 thi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617372/ https://www.ncbi.nlm.nih.gov/pubmed/25925702 http://dx.doi.org/10.1093/ndt/gfv094 |
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author | Sawhney, Simon Fluck, Nick Marks, Angharad Prescott, Gordon Simpson, William Tomlinson, Laurie Black, Corri |
author_facet | Sawhney, Simon Fluck, Nick Marks, Angharad Prescott, Gordon Simpson, William Tomlinson, Laurie Black, Corri |
author_sort | Sawhney, Simon |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4617372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-46173722015-10-26 Acute kidney injury—how does automated detection perform? Sawhney, Simon Fluck, Nick Marks, Angharad Prescott, Gordon Simpson, William Tomlinson, Laurie Black, Corri Nephrol Dial Transplant CLINICAL SCIENCE 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. Oxford University Press 2015-11 2015-04-28 /pmc/articles/PMC4617372/ /pubmed/25925702 http://dx.doi.org/10.1093/ndt/gfv094 Text en © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | CLINICAL SCIENCE Sawhney, Simon Fluck, Nick Marks, Angharad Prescott, Gordon Simpson, William Tomlinson, Laurie Black, Corri Acute kidney injury—how does automated detection perform? |
title | Acute kidney injury—how does automated detection perform? |
title_full | Acute kidney injury—how does automated detection perform? |
title_fullStr | Acute kidney injury—how does automated detection perform? |
title_full_unstemmed | Acute kidney injury—how does automated detection perform? |
title_short | Acute kidney injury—how does automated detection perform? |
title_sort | acute kidney injury—how does automated detection perform? |
topic | CLINICAL SCIENCE |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617372/ https://www.ncbi.nlm.nih.gov/pubmed/25925702 http://dx.doi.org/10.1093/ndt/gfv094 |
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