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Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs

PURPOSE: Comparisons between institutions of intensive care unit (ICU) length of stay (LOS) are significantly confounded by individual patient characteristics, and currently there is a paucity of methods available to calculate risk-adjusted metrics. METHODS: We extracted de-identified data from the...

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Autores principales: Straney, Lahn D., Udy, Andrew A., Burrell, Aidan, Bergmeir, Christoph, Huckson, Sue, Cooper, D. James, Pilcher, David V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413040/
https://www.ncbi.nlm.nih.gov/pubmed/28464035
http://dx.doi.org/10.1371/journal.pone.0176570
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author Straney, Lahn D.
Udy, Andrew A.
Burrell, Aidan
Bergmeir, Christoph
Huckson, Sue
Cooper, D. James
Pilcher, David V.
author_facet Straney, Lahn D.
Udy, Andrew A.
Burrell, Aidan
Bergmeir, Christoph
Huckson, Sue
Cooper, D. James
Pilcher, David V.
author_sort Straney, Lahn D.
collection PubMed
description PURPOSE: Comparisons between institutions of intensive care unit (ICU) length of stay (LOS) are significantly confounded by individual patient characteristics, and currently there is a paucity of methods available to calculate risk-adjusted metrics. METHODS: We extracted de-identified data from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for admissions between January 1 2011 and December 31 2015. We used a mixed-effects log-normal regression model to predict LOS using patient and admission characteristics. We calculated a risk-adjusted LOS ratio (RALOSR) by dividing the geometric mean observed LOS by the exponent of the expected Ln-LOS for each site and year. The RALOSR is scaled such that values <1 indicate a LOS shorter than expected, while values >1 indicate a LOS longer than expected. Secondary mixed effects regression modelling was used to assess the stability of the estimate in units over time. RESULTS: During the study there were a total of 662,525 admissions to 168 units (median annual admissions = 767, IQR:426–1121). The mean observed LOS was 3.21 days (median = 1.79 IQR = 0.92–3.52) over the entire period, and declined on average 1.97 hours per year (95%CI:1.76–2.18) from 2011 to 2015. The RALOSR varied considerably between units, ranging from 0.35 to 2.34 indicating large differences after accounting for case-mix. CONCLUSIONS: There are large disparities in risk-adjusted LOS among Australian and New Zealand ICUs which may reflect differences in resource utilization.
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spelling pubmed-54130402017-05-14 Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs Straney, Lahn D. Udy, Andrew A. Burrell, Aidan Bergmeir, Christoph Huckson, Sue Cooper, D. James Pilcher, David V. PLoS One Research Article PURPOSE: Comparisons between institutions of intensive care unit (ICU) length of stay (LOS) are significantly confounded by individual patient characteristics, and currently there is a paucity of methods available to calculate risk-adjusted metrics. METHODS: We extracted de-identified data from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for admissions between January 1 2011 and December 31 2015. We used a mixed-effects log-normal regression model to predict LOS using patient and admission characteristics. We calculated a risk-adjusted LOS ratio (RALOSR) by dividing the geometric mean observed LOS by the exponent of the expected Ln-LOS for each site and year. The RALOSR is scaled such that values <1 indicate a LOS shorter than expected, while values >1 indicate a LOS longer than expected. Secondary mixed effects regression modelling was used to assess the stability of the estimate in units over time. RESULTS: During the study there were a total of 662,525 admissions to 168 units (median annual admissions = 767, IQR:426–1121). The mean observed LOS was 3.21 days (median = 1.79 IQR = 0.92–3.52) over the entire period, and declined on average 1.97 hours per year (95%CI:1.76–2.18) from 2011 to 2015. The RALOSR varied considerably between units, ranging from 0.35 to 2.34 indicating large differences after accounting for case-mix. CONCLUSIONS: There are large disparities in risk-adjusted LOS among Australian and New Zealand ICUs which may reflect differences in resource utilization. Public Library of Science 2017-05-02 /pmc/articles/PMC5413040/ /pubmed/28464035 http://dx.doi.org/10.1371/journal.pone.0176570 Text en © 2017 Straney 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Straney, Lahn D.
Udy, Andrew A.
Burrell, Aidan
Bergmeir, Christoph
Huckson, Sue
Cooper, D. James
Pilcher, David V.
Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs
title Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs
title_full Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs
title_fullStr Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs
title_full_unstemmed Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs
title_short Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs
title_sort modelling risk-adjusted variation in length of stay among australian and new zealand icus
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5413040/
https://www.ncbi.nlm.nih.gov/pubmed/28464035
http://dx.doi.org/10.1371/journal.pone.0176570
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