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The ANZROD model: better benchmarking of ICU outcomes and detection of outliers

2016; Elsevier BV; Volume: 18; Issue: 1 Linguagem: Inglês

10.1016/s1441-2772(23)00928-6

2652-9335

Eldho Paul, Michael Bailey, Jessica Kasza, David Pilcher,

Cardiac, Anesthesia and Surgical Outcomes

Objective: To compare the impact of the 2013 Australian and New Zealand Risk of Death (ANZROD) model and the 2002 Acute Physiology and Chronic Health Evaluation (APACHE) III-j model as risk-adjustment tools for benchmarking performance and detecting outliers in Australian and New Zealand intensive care units. Methods: Data were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database for all ICUs that contributed data between 1 January 2010 and 31 December 2013. Annual standardised mortality ratios (SMRs) were calculated for ICUs using the ANZROD and APACHE III-j models. They were plotted on funnel plots separately for each hospital type, with ICUs above the upper 99.8% control limit considered as potential outliers with worse performance than their peer group. Overdispersion parameters were estimated for both models. Overall fi t was assessed using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Outlier association with mortality was assessed using a logistic regression model. Results: The ANZROD model identified more outliers than the APACHE III-j model during the study period. The numbers of outliers in rural, metropolitan, tertiary and private hospitals identified by the ANZROD model were 3, 2, 6 and 6, respectively; and those identified by the APACHE III-j model were 2, 0, 1 and 1, respectively. The degree of overdispersion was less for the ANZROD model compared with the APACHE III-j model in each year. The ANZROD model showed better overall fi t to the data, with smaller AIC and BIC values than the APACHE III-j model. Outlier ICUs identified using the ANZROD model were more strongly associated with increased mortality. Conclusion: The ANZROD model reduces variability in SMRs due to casemix, as measured by overdispersion, and facilitates more consistent identification of true outlier ICUs, compared with the APACHE III-j model.