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Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions
BACKGROUND: Reducing readmissions is an international priority in healthcare. Acute kidney injury (AKI) is common, serious and also a global concern. This analysis evaluates AKI as a candidate risk factor for unplanned readmissions and determines the reasons for readmissions. METHODS: GLOMMS-II is a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217258/ https://www.ncbi.nlm.nih.gov/pubmed/28061831 http://dx.doi.org/10.1186/s12882-016-0430-4 |
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author | Sawhney, Simon Marks, Angharad Fluck, Nick McLernon, David J. Prescott, Gordon J. Black, Corri |
author_facet | Sawhney, Simon Marks, Angharad Fluck, Nick McLernon, David J. Prescott, Gordon J. Black, Corri |
author_sort | Sawhney, Simon |
collection | PubMed |
description | BACKGROUND: Reducing readmissions is an international priority in healthcare. Acute kidney injury (AKI) is common, serious and also a global concern. This analysis evaluates AKI as a candidate risk factor for unplanned readmissions and determines the reasons for readmissions. METHODS: GLOMMS-II is a large population cohort from one health authority in Scotland, combining hospital episode data and complete serial biochemistry results through data-linkage. 16453 people (2623 with AKI and 13830 without AKI) from GLOMMS-II who survived an index hospital admission in 2003 were used to identify the causes of and predict readmissions. The main outcome was “unplanned readmission or death” within 90 days of discharge. In a secondary analysis, the outcome was limited to readmissions with acute pulmonary oedema. 26 candidate predictors during the index admission included AKI (defined and staged 1–3 using an automated e-alert algorithm), prior AKI episodes, baseline kidney function, index admission circumstances and comorbidities. Prediction models were developed and assessed using multivariable logistic regression (stepwise variable selection), C statistics, bootstrap validation and decision curve analysis. RESULTS: Three thousand sixty-five (18.6%) patients had the main outcome (2702 readmitted, 363 died without readmission). The outcome was strongly predicted by AKI. Multivariable odds ratios for AKI stage 3; 2 and 1 (vs no AKI) were 2.80 (2.22–3.53); 2.23 (1.85–2.68) and 1.50 (1.33–1.70). Acute pulmonary oedema was the reason for readmission in 26.6% with AKI and eGFR < 60; and 4.0% with no AKI and eGFR ≥ 60. The best stepwise model from all candidate predictors had a C statistic of 0.698 for the main outcome. In a secondary analysis, a model for readmission with acute pulmonary oedema had a C statistic of 0.853. In decision curve analysis, AKI improved clinical utility when added to any model, although the incremental benefit was small when predicting the main outcome. CONCLUSIONS: AKI is a strong, consistent and independent risk factor for unplanned readmissions – particularly readmissions with acute pulmonary oedema. Pre-emptive planning at discharge should be considered to minimise avoidable readmissions in this high risk group. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12882-016-0430-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5217258 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52172582017-01-09 Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions Sawhney, Simon Marks, Angharad Fluck, Nick McLernon, David J. Prescott, Gordon J. Black, Corri BMC Nephrol Research Article BACKGROUND: Reducing readmissions is an international priority in healthcare. Acute kidney injury (AKI) is common, serious and also a global concern. This analysis evaluates AKI as a candidate risk factor for unplanned readmissions and determines the reasons for readmissions. METHODS: GLOMMS-II is a large population cohort from one health authority in Scotland, combining hospital episode data and complete serial biochemistry results through data-linkage. 16453 people (2623 with AKI and 13830 without AKI) from GLOMMS-II who survived an index hospital admission in 2003 were used to identify the causes of and predict readmissions. The main outcome was “unplanned readmission or death” within 90 days of discharge. In a secondary analysis, the outcome was limited to readmissions with acute pulmonary oedema. 26 candidate predictors during the index admission included AKI (defined and staged 1–3 using an automated e-alert algorithm), prior AKI episodes, baseline kidney function, index admission circumstances and comorbidities. Prediction models were developed and assessed using multivariable logistic regression (stepwise variable selection), C statistics, bootstrap validation and decision curve analysis. RESULTS: Three thousand sixty-five (18.6%) patients had the main outcome (2702 readmitted, 363 died without readmission). The outcome was strongly predicted by AKI. Multivariable odds ratios for AKI stage 3; 2 and 1 (vs no AKI) were 2.80 (2.22–3.53); 2.23 (1.85–2.68) and 1.50 (1.33–1.70). Acute pulmonary oedema was the reason for readmission in 26.6% with AKI and eGFR < 60; and 4.0% with no AKI and eGFR ≥ 60. The best stepwise model from all candidate predictors had a C statistic of 0.698 for the main outcome. In a secondary analysis, a model for readmission with acute pulmonary oedema had a C statistic of 0.853. In decision curve analysis, AKI improved clinical utility when added to any model, although the incremental benefit was small when predicting the main outcome. CONCLUSIONS: AKI is a strong, consistent and independent risk factor for unplanned readmissions – particularly readmissions with acute pulmonary oedema. Pre-emptive planning at discharge should be considered to minimise avoidable readmissions in this high risk group. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12882-016-0430-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-06 /pmc/articles/PMC5217258/ /pubmed/28061831 http://dx.doi.org/10.1186/s12882-016-0430-4 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Sawhney, Simon Marks, Angharad Fluck, Nick McLernon, David J. Prescott, Gordon J. Black, Corri Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions |
title | Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions |
title_full | Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions |
title_fullStr | Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions |
title_full_unstemmed | Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions |
title_short | Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions |
title_sort | acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217258/ https://www.ncbi.nlm.nih.gov/pubmed/28061831 http://dx.doi.org/10.1186/s12882-016-0430-4 |
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