Intensive care admissions for adults with treated kidney failure in Australia: A national retrospective cohort study.

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

Previous studies have addressed patients in whom treatment is withheld or withdrawn after a period of intensive care unit management. However, no studies have investigated the epidemiology of patients with treatment limitations in place at the time of intensive care unit admission. To report the epidemiology and outcome of patients with treatment limitations at intensive care unit admission and to identify characteristics associated with survival and discharge to home. Retrospective database study using data from the Australian and New Zealand Intensive Care Society Adult Patient Database. Australian and New Zealand intensive care units. One hundred eighty-seven thousand four hundred and one intensive care patients collected over a 3-yr period, 5,989 (3.2%) of whom had treatment limitations at admission to the intensive care unit. Retrospective database study with no interventions. Data collected included patient characteristics, length of stay, mortality, and discharge destination. Mean intensive care unit bed days were used as a surrogate for resource consumption. Between January 1, 2007, and December 31, 2009, 5,989 (3.2%) patients were reported to the Australia and New Zealand Intensive Care Society Adult Patient Database who had treatment limitation orders at admission to intensive care unit. Mortality was 53% (95% confidence interval 51.7%-54.3%) compared with 9% (95% confidence interval 8.9%-9.1%) in patients admitted for full active management (p ≤ .001). Overall, 30% of patients with treatment limitations were discharged directly to their homes. Intensive care unit bed day usage was similar between the two groups. Within the treatment limitation group, younger patients, those with less comorbid diseases, less acute physiological disturbance, and those admitted following elective surgery, were more likely to survive and be discharged home. Admission diagnosis was an important determinant of outcome with intracranial or subarachnoid hemorrhage predicting a extremely high mortality. Patients with treatment limitations on intensive care unit admission comprise approximately 2,000 patients per year in Australia and New Zealand. Despite such limitations, almost half of these patients survive their hospital admission and a third return directly to their home.

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

Intensive care admissions following rapid response team reviews in patients with COVID-19 in Australia

Characteristics and outcomes of patients admitted to regional and rural intensive care units in Australia

Characteristics and outcomes of adults with acute brain injuries admitted to intensive care units in Australia and New Zealand from 2013 to 2022

BackgroundRisk adjusted mortality for intensive care units (ICU) is usually estimated via logistic regression. Random effects (RE) or hierarchical models have been advocated to estimate provider risk-adjusted mortality on the basis that standard estimators increase false outlier classification. The utility of fixed effects (FE) estimators (separate ICU-specific intercepts) has not been fully explored.MethodsUsing a cohort from the Australian and New Zealand Intensive Care Society Adult Patient Database, 2009–2010, the model fit of different logistic estimators (FE, random-intercept and random-coefficient) was characterised: Bayesian Information Criterion (BIC; lower values better), receiver-operator characteristic curve area (AUC) and Hosmer-Lemeshow (H-L) statistic. ICU standardised hospital mortality ratios (SMR) and 95%CI were compared between models. ICU site performance (FE), relative to the grand observation-weighted mean (GO-WM) on odds ratio (OR), risk ratio (RR) and probability scales were assessed using model-based average marginal effects (AME).ResultsThe data set consisted of 145355 patients in 128 ICUs, years 2009 (47.5%) & 2010 (52.5%), with mean(SD) age 60.9(18.8) years, 56% male and ICU and hospital mortalities of 7.0% and 10.9% respectively. The FE model had a BIC = 64058, AUC = 0.90 and an H-L statistic P-value = 0.22. The best-fitting random-intercept model had a BIC = 64457, AUC = 0.90 and H-L statistic P-value = 0.32 and random-coefficient model, BIC = 64556, AUC = 0.90 and H-L statistic P-value = 0.28. Across ICUs and over years no outliers (SMR 95% CI excluding null-value = 1) were identified and no model difference in SMR spread or 95%CI span was demonstrated. Using AME (OR and RR scale), ICU site-specific estimates diverged from the GO-WM, and the effect spread decreased over calendar years. On the probability scale, a majority of ICUs demonstrated calendar year decrease, but in the for-profit sector, this trend was reversed.ConclusionsThe FE estimator had model advantage compared with conventional RE models. Using AME, between and over-year ICU site-effects were easily characterised.

Rehabilitation outcomes of survivors of cardiac arrest admitted to ICUs in Australia and New Zealand (ROSC ANZ): A data linkage study

Opioid administration and weaning practices in mechanically ventilated adult intensive care unit patients: A retrospective analysis.

To study variation in mortality associated with time and day of admission to the intensive care unit (ICU). Retrospective cohort analysis using the Australian and New Zealand Intensive Care Society Adult Patient Database. 245,057 admissions to 41 Australian ICUs from January 2000 to December 2008. Observed mortality and standardised mortality ratio (SMR) based on Acute Physiology and Chronic Health Evaluation III, 10th iteration (APACHE III-j) scores. Subgroup analysis was performed on the basis of elective surgical or emergency admission to ICU. 48% of patients were admitted after hours (18:00-05:59) and 20% of patients were admitted on weekends (Saturday and Sunday). Patients admitted after hours had a 17% hospital mortality rate compared with 14% of patients admitted in hours (P < 0.001); and SMRs of 0.92 (95% CI, 0.91-0.93) and 0.83 (95% CI, 0.83-0.84), respectively. Weekend admissions had a 20% hospital mortality rate compared with 14% on weekdays (P < 0.001), with SMRs of 0.95 (95% CI, 0.94-0.97) and 0.92 (95% CI, 0.92-0.93), respectively. Variation in outcome with time of admission to ICU was accounted for predominantly by elective surgical patients. Patients admitted to ICUs in Australia after hours and on weekends have a higher observed and risk-adjusted mortality than patients admitted at other times. Further research is required to determine the causes and relationship to resource availability and staffing.

Frailty Screening in Critical Care at Scale

Background: There is limited information on changes in the epidemiology and outcome of patients with asthma admitted to intensive care units (ICUs) in the last decade. A database sampling intensive...

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

BackgroundThe majority of organ donations in Australia occur in the DonateLife Network of hospitals, but limited monitoring at other sites may allow donation opportunities to be missed. Our aim was to estimate expected donor numbers using routinely collected data from the Australian and New Zealand Intensive Care Society Adult Patient Database and determine whether unrecognized potential donors might exist in non-DonateLife hospitals.MethodsAll deaths at 150 Australian intensive care units (ICUs) contributing to the Australian and New Zealand Intensive Care Society Adult Patient Database were analyzed between January 2010 and December 2015. Donor numbers were extracted from the Australian and New Zealand Organ Donor registry. A univariate linear regression model was developed to estimate expected donor numbers in DonateLife hospitals, then applied to non-DonateLife hospitals.ResultsOf 33 614 deaths at 71 DonateLife hospitals, 6835 (20%) met criteria as “ICU deaths potentially suitable to be donors,” and 1992 (6%) were actual donors. There was a consistent relationship between these groups (R2 = 0.626, P < 0.001) allowing the development of a prediction model which adequately estimated expected donors. Of 8077 deaths in 79 non-DonateLife ICUs, 452 (6%) met criteria as potentially suitable donors. Applying the prediction model developed in DonateLife hospitals, the estimated expected donors in non-DonateLife hospitals was 130. However, there were only 75 actual donors.ConclusionsIt is possible to estimate the expected number of Australian organ donors using routinely collected registry data. These findings suggest that there may be a small but significant pool of underutilized potential donors in non-DonateLife hospitals. This may provide an opportunity to increase donation rates.

BackgroundIt is unclear if the impact of frailty on mortality differs between patients with viral pneumonitis due to COVID-19 or other causes. We aimed to determine if a difference exists between patients with and without COVID-19 pneumonitis.MethodsThis multicentre, retrospective, cohort study using the Australian and New Zealand Intensive Care Society Adult Patient Database included patients aged ≥ 16 years admitted to 153 ICUs between 01/012020 and 12/31/2021 with admission diagnostic codes for viral pneumonia or acute respiratory distress syndrome, and Clinical Frailty Scale (CFS). The primary outcome was hospital mortality.ResultsA total of 4620 patients were studied, and 3077 (66.6%) had COVID-19. The patients with COVID-19 were younger (median [IQR] 57.0 [44.7–68.3] vs. 66.1 [52.0–76.2]; p < 0.001) and less frail (median [IQR] CFS 3 [2–4] vs. 4 [3–5]; p < 0.001) than non-COVID-19 patients. The overall hospital mortality was similar between the patients with and without COVID-19 (14.7% vs. 14.9%; p = 0.82). Frailty alone as a predictor of mortality showed only moderate discrimination in differentiating survivors from those who died but was similar between patients with and without COVID-19 (AUROC 0.68 vs. 0.66; p = 0.42). Increasing frailty scores were associated with hospital mortality, after adjusting for Australian and New Zealand Risk of Death score and sex. However, the effect of frailty was similar in patients with and without COVID-19 (OR = 1.29; 95% CI: 1.19–1.41 vs. OR = 1.24; 95% CI: 1.11–1.37).ConclusionThe presence of frailty was an independent risk factor for mortality. However, the impact of frailty on outcomes was similar in COVID-19 patients compared to other causes of viral pneumonitis.Graphical