Building the future of ICU care: Is our digital foundation strong enough? A multicentre survey of Australian and New Zealand intensive care units

Decreasing Case-Fatality But Not Death Following Admission to ICUs in Australia, 2005-2018

The relationship between nursing skill mix and severity of illness of patients admitted in Australian and New Zealand intensive care units

After-hours discharge from the intensive care unit (ICU) is associated with adverse patient outcomes including increased ICU readmissions and mortality. Since Australian and New Zealand data were last published, overall ICU patient mortality has decreased; however it is unknown whether changes in discharge practices have contributed to these improved outcomes. Our aim was to examine trends over time in discharge timing and the contemporary associations with mortality and ICU readmission. Retrospective cohort study using data from the Australian and New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) for patients admitted to Australian and New Zealand ICUs between January 2005 and December 2012. Data collected included patient characteristics, time of ICU discharge, hospital mortality and ICU readmissions. Between 1 January 2005 and 31 December 2012, there were 710,535 patients available for analysis, of whom 109,384 (15.4 %) were discharged after-hours (1800-0600 hours). There were no changes in timing of ICU discharge over the 8 years of the study. Patients discharged after-hours had a higher hospital mortality (6.4 versus 3.6 %; P < 0.001) and more ICU readmissions (5.1 versus 4.5 %; P < 0.001) than patients discharged in-hours. Although post-ICU mortality for all patients declined during the study period, the risk associated with after-hours discharge remained elevated throughout (odds ratio 1.34, 95 % confidence intervals 1.30-1.38). After-hours discharge remains an important independent predictor of hospital mortality and readmission to ICU. Despite widespread dissemination this evidence has not translated into fewer after-hours discharges or reduction in risk in Australian and New Zealand hospitals.

The Australian and New Zealand Intensive Care Society Adult Patient Database (ANZICS APD) is one of the largest databases of its kind in the world and collects individual admissions' data from intensive care units (ICUs) around Australia and New Zealand. Use of this database for monitoring and comparing the performance of ICUs, quantified by the standardised mortality ratio, poses several theoretical and computational challenges, which are addressed in this paper. In particular, the expected number of deaths must be appropriately estimated, the ICU casemix adjustment must be adequate, statistical variation must be fully accounted for, and appropriate adjustment for multiple comparisons must be made. Typically, one or more of these issues have been neglected in ICU comparison studies. Our approach to the analysis proceeds by fitting a random coefficient hierarchical logistic regression model for the inhospital death of each patient, with patients clustered within ICUs. We anticipate the majority of ICUs will be estimated as performing 'usually' after adjusting for important clinical covariates. We take as a starting point the ideas in Ohlssen et al and estimate an appropriate null model that we expect these ICUs to follow, taking a frequentist rather than a Bayesian approach. This methodology allows us to rigorously account for the aforementioned statistical issues and to determine if there are any ICUs contributing to the Australian and New Zealand Intensive Care Society database that have comparatively unusual performance. In addition to investigating the yearly performance of the ICUs, we also estimate changes in individual ICU performance between 2009 and 2010 by adjusting for regression-to-the-mean.

Knowledge about patients with acute liver failure (ALF) in Australia and New Zealand (ANZ) is lacking. To evaluate whether the pattern of ALF would be similar to previous studies and whether, despite potentially low transplantation rates, mortality would be comparable. We obtained data from the ANZ Intensive Care Society Adult Patient Database and the ANZ Liver Transplant Registry for 10 years commencing 2005 and analysed for patient outcomes. During the study period, 1 022 698 adults were admitted to intensive care units across ANZ, of which 723 had ALF. The estimated annual incidence of ALF over this period was 3.4/million people and increased over time (P = 0.001). ALF patients had high illness severity (Acute Physiology And Chronic Health Evaluation III 79.8 vs 50.1 in non-ALF patients; P < 0.0001) and were more likely to be younger, female, pregnant and immunosuppressed. ALF was an independent predictor of mortality (odds ratio 1.5 (1.26-1.79); P < 0.0001). At less than 23%, the use of liver transplantation was low, but the mortality of 39% was similar to previous studies. ALF is a rare but increasing diagnosis in ANZ intensive care units. Low transplantation rates in ANZ for ALF do not appear to be associated with higher mortality rates than reported in the literature.

Intensive care unit (ICU) outcomes have been the subject of controversy. The objective was to model hospital mortality and ICU length-of-stay time-change of patients recorded in the Australian and New Zealand Intensive Care Society adult patient database. Retrospective, cohort study of prospectively collected data on index patient admissions. Australian and New Zealand ICUs, 1993-2003. The Australian and New Zealand Intensive Care Society adult patient database, which contains data for 223,129 patients. None. Hospital mortality and ICU length of stay were modeled using logistic and linear regression, respectively, with determination (80%) and validation (20%) data sets. Model adequacy was assessed by discrimination (receiver operating characteristic curve area, AZ) and calibration (Hosmer-Lemeshow C) for mortality and R2 for length of stay. Predictor variables included patient demographics, severity score, surgical and ventilation status, ICU categories, and geographical locality. The data set comprised 223,129 patients: Their mean (SD) age was 59.2 (18.9) yrs, 41.7% were female, their mean (SD) Acute Physiology and Chronic Health Evaluation (APACHE) III score was 53 (31), they had 16.1% overall mortality rate, and 45.7% were mechanically ventilated. ICU length of stay was 3.6 (5.6) days. A(Z), C statistic, and R2 for developmental and validation model data sets were 0.88, 17.64 (p = .02), and 0.18; and 0.88, 12.32 (p = .26), and 0.18, respectively. Variables with mortality impact (p < or = .001) were age (odds ratio [OR] 1.023), gender (OR 1.16; males vs. females), APACHE III score (OR 1.06), mechanical ventilation (OR 1.66), and surgical status (elective, OR 0.17; emergency, OR 0.47; compared with nonsurgical). ICU level and locality had significant mortality-time effects. Similar variables were found to predict length of stay. Risk-adjusted mortality declined, during 1993-2003, from 0.19 (95% confidence interval 0.17-0.21) to 0.15 (0.13-0.16) and similarly for ventilated patients: 0.26 (0.24-0.29) to 0.23 (0.21-0.25). Predicted mean ICU length of stay (days) demonstrated minimal overall time-change: 3.4 (2.2) in 1993 to 3.5 (2.7) in 2003, peaking at 3.7 (2.4) in 2000. Overall hospital mortality rate in patients admitted to Australian and New Zealand ICUs decreased 4% over 11 yrs. A similar trend occurred for mechanically ventilated patients. Length of stay changed minimally over this period.

The mortality outcome of mechanical ventilation, a key intervention in the critically ill, has been variously reported to be determined by intensive care patient volume. We determined the volume-(mortality)-outcome relationship of mechanically ventilated patients whose records were contributed to the Australian and New Zealand Intensive Care Society Adult Patient Database. Retrospective cohort study of 208,810 index patient admissions from 136 Australian and New Zealand intensive care units in the same number of hospitals over the course of 1995-2009. The patient-volume effect on hospital mortality, overall and at the level of patient (nonsurgical, elective surgical, and emergency surgical) and intensive care unit (rural/regional, metropolitan, tertiary, and private) descriptors, was determined by random-effects logistic regression adjusting for illness severity and demographic and geographical predictors. Annualized patient volume was modeled both as a categorical (deciles) and, with calendar year, a continuous variable using fractional polynomials. The patients were of mean age of 59 yrs (SD, 19 yrs), Acute Physiology and Chronic Health Evaluation III score 66 (32), and 39.4% female, with a hospital mortality of 22.4%. Overall and at both the patient and intensive care unit descriptor levels, no progressive decline in mortality was demonstrated across the annual patient volume range (12-932). Over the whole database, mortality odds ratio for the last volume decile (801-932 patients) was 1.26 (95% confidence interval, 1.06-1.50; p = .009) compared with the first volume decile (12-101 patients). Calendar year mortality decreases were evident (odds ratio, 0.96; 95% confidence interval, 0.94-0.98; p = .0001). Using fractional polynomials, modest curvilinear mortality increases (range, 5%-8%) across the volume range were noted over the whole database for nonsurgical patients and at the tertiary intensive care unit level. No inverse volume-(mortality)-outcome relationship was apparent for ventilated patients in the Australian and New Zealand Intensive Care Society database. Mechanisms for mortality increments with patient volume were not identified but warrant further study.

The impact of body mass index on long-term survival after ICU admission due to COVID-19: A retrospective multicentre study

Risk prediction of hospital mortality for adult patients admitted to Australian and New Zealand intensive care units: Development and validation of the Australian and New Zealand Risk of Death model

Family visitation policies, facilities, and support in Australia and New Zealand intensive care units: A multicentre, registry-linked survey

Time series analysis has seen limited application in the biomedical Literature. The utility of conventional and advanced time series estimators was explored for intensive care unit (ICU) outcome series. Monthly mean time series, 1993-2006, for hospital mortality, severity-of-illness score (APACHE III), ventilation fraction and patient type (medical and surgical), were generated from the Australia and New Zealand Intensive Care Society adult patient database. Analyses encompassed geographical seasonal mortality patterns, series structural time changes, mortality series volatility using autoregressive moving average and Generalized Autoregressive Conditional Heteroscedasticity models in which predicted variances are updated adaptively, and bivariate and multivariate (vector error correction models) cointegrating relationships between series. The mortality series exhibited marked seasonality, declining mortality trend and substantial autocorrelation beyond 24 lags. Mortality increased in winter months (July-August); the medical series featured annual cycling, whereas the surgical demonstrated long and short (3-4 months) cycling. Series structural breaks were apparent in January 1995 and December 2002. The covariance stationary first-differenced mortality series was consistent with a seasonal autoregressive moving average process; the observed conditional-variance volatility (1993-1995) and residual Autoregressive Conditional Heteroscedasticity effects entailed a Generalized Autoregressive Conditional Heteroscedasticity model, preferred by information criterion and mean model forecast performance. Bivariate cointegration, indicating long-term equilibrium relationships, was established between mortality and severity-of-illness scores at the database level and for categories of ICUs. Multivariate cointegration was demonstrated for {log APACHE III score, log ICU length of stay, ICU mortality and ventilation fraction}. A system approach to understanding series time-dependence may be established using conventional and advanced econometric time series estimators.

Development and implementation of a high-quality clinical database: the Australian and New Zealand Intensive Care Society Adult Patient Database

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.

Timing of onset and burden of persistent critical illness in Australia and New Zealand: a retrospective, population-based, observational study

Volume-outcome relationships for tracheostomies in Australia and New Zealand Intensive Care Units: A registry-based retrospective study.