Eldho Paul1, Michael Bailey2, Jessica Kasza3, David Pilcher2. 1. Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia. eldho.paul@monash.edu. 2. Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia. 3. Biostatistics Unit, Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
Abstract
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 fit 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 fit 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.
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 fit 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 fit 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.
Authors: Jesse D Raffa; Alistair E W Johnson; Zach O'Brien; Tom J Pollard; Roger G Mark; Leo A Celi; David Pilcher; Omar Badawi Journal: Crit Care Med Date: 2022-03-25 Impact factor: 9.296
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Authors: Lowell Ling; Chun Ming Ho; Pauline Yeung Ng; King Chung Kenny Chan; Hoi Ping Shum; Cheuk Yan Chan; Alwin Wai Tak Yeung; Wai Tat Wong; Shek Yin Au; Kit Hung Anne Leung; Jacky Ka Hing Chan; Chi Keung Ching; Oi Yan Tam; Hin Hung Tsang; Ting Liong; Kin Ip Law; Manimala Dharmangadan; Dominic So; Fu Loi Chow; Wai Ming Chan; Koon Ngai Lam; Kai Man Chan; Oi Fung Mok; Man Yee To; Sze Yuen Yau; Carmen Chan; Ella Lei; Gavin Matthew Joynt Journal: J Intensive Care Date: 2021-01-06