Cargando…
Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis
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...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488369/ https://www.ncbi.nlm.nih.gov/pubmed/26125553 http://dx.doi.org/10.1371/journal.pone.0131909 |
_version_ | 1782379143690715136 |
---|---|
author | Sawhney, Simon Marks, Angharad Ali, Tariq Clark, Laura Fluck, Nick Prescott, Gordon J. Simpson, William G. Black, Corri |
author_facet | Sawhney, Simon Marks, Angharad Ali, Tariq Clark, Laura Fluck, Nick Prescott, Gordon J. Simpson, William G. Black, Corri |
author_sort | Sawhney, Simon |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4488369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44883692015-07-02 Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis Sawhney, Simon Marks, Angharad Ali, Tariq Clark, Laura Fluck, Nick Prescott, Gordon J. Simpson, William G. Black, Corri PLoS One Research Article 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. Public Library of Science 2015-06-30 /pmc/articles/PMC4488369/ /pubmed/26125553 http://dx.doi.org/10.1371/journal.pone.0131909 Text en © 2015 Sawhney et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited. |
spellingShingle | Research Article Sawhney, Simon Marks, Angharad Ali, Tariq Clark, Laura Fluck, Nick Prescott, Gordon J. Simpson, William G. Black, Corri Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis |
title | Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis |
title_full | Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis |
title_fullStr | Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis |
title_full_unstemmed | Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis |
title_short | Maximising Acute Kidney Injury Alerts – A Cross-Sectional Comparison with the Clinical Diagnosis |
title_sort | maximising acute kidney injury alerts – a cross-sectional comparison with the clinical diagnosis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488369/ https://www.ncbi.nlm.nih.gov/pubmed/26125553 http://dx.doi.org/10.1371/journal.pone.0131909 |
work_keys_str_mv | AT sawhneysimon maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis AT marksangharad maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis AT alitariq maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis AT clarklaura maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis AT flucknick maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis AT prescottgordonj maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis AT simpsonwilliamg maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis AT blackcorri maximisingacutekidneyinjuryalertsacrosssectionalcomparisonwiththeclinicaldiagnosis |