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Open Access Research article

Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010

Patricia J Solomon1*, Jessica Kasza2, John L Moran3 and the Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation (CORE)

Author Affiliations

1 School of Mathematical Sciences, University of Adelaide, North Terrace, 5005 Adelaide, Australia

2 Department of Epidemiology and Preventive Medicine, Monash University, 3004 Melbourne, Australia

3 Department of Intensive Care Medicine, The Queen Elizabeth Hospital, 28 Woodville Road, Woodville, 5011 Adelaide, Australia

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BMC Medical Research Methodology 2014, 14:53  doi:10.1186/1471-2288-14-53

Published: 22 April 2014

Abstract

Background

The Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD) collects voluntary data on patient admissions to Australian and New Zealand intensive care units (ICUs). This paper presents an in-depth statistical analysis of risk-adjusted mortality of ICU admissions from 2000 to 2010 for the purpose of identifying ICUs with unusual performance.

Methods

A cohort of 523,462 patients from 144 ICUs was analysed. For each ICU, the natural logarithm of the standardised mortality ratio (log-SMR) was estimated from a risk-adjusted, three-level hierarchical model. This is the first time a three-level model has been fitted to such a large ICU database anywhere. The analysis was conducted in three stages which included the estimation of a null distribution to describe usual ICU performance. Log-SMRs with appropriate estimates of standard errors are presented in a funnel plot using 5% false discovery rate thresholds. False coverage-statement rate confidence intervals are also presented. The observed numbers of deaths for ICUs identified as unusual are compared to the predicted true worst numbers of deaths under the model for usual ICU performance.

Results

Seven ICUs were identified as performing unusually over the period 2000 to 2010, in particular, demonstrating high risk-adjusted mortality compared to the majority of ICUs. Four of the seven were ICUs in private hospitals. Our three-stage approach to the analysis detected outlying ICUs which were not identified in a conventional (single) risk-adjusted model for mortality using SMRs to compare ICUs. We also observed a significant linear decline in mortality over the decade. Distinct yearly and weekly respiratory seasonal effects were observed across regions of Australia and New Zealand for the first time.

Conclusions

The statistical approach proposed in this paper is intended to be used for the review of observed ICU and hospital mortality. Two important messages from our study are firstly, that comprehensive risk-adjustment is essential in modelling patient mortality for comparing performance, and secondly, that the appropriate statistical analysis is complicated.

Keywords:
Hierarchical models; Hospital comparisons; Intensive care performance; Multiple comparisons; Risk-adjusted mortality; Seasonal effects; Variance components