ArticleBMC medical research methodology2014
Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010.
Article in BMC medical research methodology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluations of statistical methods for outlier detection when benchmarking in clinical registries: a systematic review.BMJ open · 2023Pooled it
- Article
- Ninety-Day Cost, Mortality and Hospital Disparities in Ischemic Stroke: Real-World Evidence from a Czech Administrative Database.Healthcare (Basel, Switzerland) · 2026Article
- Accuracy of site benchmarking in clinical quality registries of varying size.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Variation in severity-adjusted resource use and outcome in intensive care units.Intensive care medicine · 2022Article
- Modelling hospital outcome: problems with endogeneity.BMC medical research methodology · 2021Article
- Identifying unusual performance in Australian and New Zealand intensive care units from 2000 to 2010.BMC medical research methodology · 2014Article
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsA 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.
resultsSeven 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.
conclusionsThe 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.
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