Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs.
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ABSTRACT: 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.
SUBMITTER: Straney LD
PROVIDER: S-EPMC5413040 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs.
Straney Lahn D LD Udy Andrew A AA Burrell Aidan A Bergmeir Christoph C Huckson Sue S Cooper D James DJ Pilcher David V DV
PloS one 20170502 5
<h4>Purpose</h4>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.<h4>Methods</h4>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 ...[more]
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