ArticlePloS one2017
Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Modelling the number of ICU beds, ICU length of stay, and ICU admission rates in Australia and New Zealand through to 2040: The PrognostICate Study.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026Article
- Development and validation of a prediction model of length of ICU stay for benchmarking: a retrospective cohort study.Scientific reports · 2026Article
- Factors Affecting the Length of Stay in the Intensive Care Unit among Adults in Saudi Arabia: A Cross-Sectional Study.Journal of clinical medicine · 2023Article
- Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise.BMC medical research methodology · 2023Article
- Thirty years of ANZICS CORE: A clinical quality success story.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2023Article
- ICU scoring systems.Intensive care medicine · 2023Article
- "The ICU efficiency plot": a novel graphical measure of ICU performance in Australia and New Zealand.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2021Article
- Young Adults Among Patients Admitted to Polish Intensive Care Units in the Silesian ICU Registry.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeComparisons 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.
methodsWe 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.
resultsDuring 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.
conclusionsThere are large disparities in risk-adjusted LOS among Australian and New Zealand ICUs which may reflect differences in resource utilization.
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