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

PLoS One. 2017 May 2;12(5):e0176570. doi: 10.1371/journal.pone.0176570. eCollection 2017.

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.

MeSH terms

  • Aged
  • Australia
  • Female
  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Length of Stay / statistics & numerical data*
  • Male
  • Middle Aged
  • Models, Statistical
  • New Zealand
  • Risk Assessment

Grants and funding

This research was funded in part by the Monash Institute of Medical Engineering (MIME) under a MIME seed funding grant (2016-37). (LS, CB) [https://www.monash.edu/mime/seed]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.