Regional access to a centralized extracorporeal membrane oxygenation (ECMO) service in Victoria, Australia.

Introduction: Victoria, Australia provides a centralised state ECMO service, supported by ambulance retrieval. Equity of access to this service has not been previously described. Objective: Describe the characteristics of ECMO recipients and quantify geographical and socioeconomic influence on access. Design: Retrospective observational study with spatial mapping. Participants and setting: Adult (≥18 years) ECMO recipients from July 2016-June 2022. Data from administrative Victorian Admissions Episodes Database analysed in conjunction with Australian Urban Research Infrastructure Network population data and choropleth mapping. Presumed ECMO modes were inferred from cardiopulmonary bypass and pre-hospital cardiac arrest codes. Spatial autoregressive models including Moran's test used for spatial lag testing. Outcomes: Demographics and outcomes of ECMO recipients; ECMO incidence by patient residence (Statistical-Area Level 2, SA-2) and Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD); and ECMO utilisation adjusted for patient factors and linear distance from the central ECMO referral site. Results: 631 adults received ECMO over 6 years, after exclusion of paediatric (n = 242), duplicate (n = 135), and interstate or incomplete (n = 72) records. Mean age was 51.8 years, and 68.8 % were male. Overall ECMO incidence was 3.00 ± 3.95 per 105 population. 135 (21.4 %) were presumed VA-ECMO, 59 (9.3 %) presumed ECPR, and 437 (69.3 %) presumed VV-ECMO. Spatial lag was non-significant after adjusting for patient characteristics. Distance from the central referral site (dy/dx = 0.19, 95% CI -0.41-0.04, p = 0.105) and IRSAD score (dy/dx = 0.17, 95% CI -0.19-0.53, p = 0.359) did not predict ECMO utilisation. Conclusion: Victorian ECMO incidence rates were low. We did not find evidence of inequity of access to ECMO irrespective of regional area or socioeconomic status.

Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise.

BACKGROUND: Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared. METHODS: From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established. RESULTS: The ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics. CONCLUSIONS: Inference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no "one best model"; thus, ICU "performance" is determined by model choice and any rankings thereupon should be circumspect.

COBRA: COde Blue Retrospective Audit in a metropolitan hospital.

BACKGROUND: Inhospital cardiac arrest (IHCA) is an uncommon but challenging problem. AIMS: To investigate the management and outcomes of IHCA, and to investigate the effect of introducing a medical emergency team (MET) on IHCA prevalence. METHODS: Retrospective medical record review of 176 adult IHCA episodes at Box Hill Hospital, a university-affiliated public hospital in metropolitan Melbourne, from July 2012 to June 2017. Inpatients receiving cardiopulmonary resuscitation for IHCA, in inpatient wards, intensive care unit, cardiac catheterisation laboratory and operating theatres were included. Data collected included demographics, resuscitation management and outcomes. Average treatment effect (ATE) was derived from margins estimates and linear regression fitted to hospital outcome, adjusted for IHCA factors. An exponentially weighed moving average control chart was used to explore IHCA prevalence over time. RESULTS: There were 65.3% of IHCA patients who died in hospital. IHCA prevalence was unchanged after the introduction of a dedicated MET service. Factors associated with higher likelihood of survival to discharge were initial cardiac of rhythm ventricular tachycardia (VT) (ATE 0.10 (95% CI = -0.03 to 0.25)) or ventricular fibrillation (VF) (ATE 0.28 (95% CI = 0.11-0.46)), cardiac monitoring at the time of arrest (ATE 0.06 (95%CI = -0.04 to 0.16)) and time to return of spontaneous circulation (ATE 0.023 (95% CI = 0.015-0.031)). CONCLUSIONS: IHCA is uncommon and is associated with high mortality. IHCA prevalence was unchanged after the introduction of a dedicated MET service. Factors associated with improved survival to hospital discharge were initial rhythm VT or VF, cardiac monitoring and shorter resuscitation times.

Hospital Acquired Complications in South Australian major public hospitals.

The prevalence of Hospital Acquired Complications (HACs) within major hospitals and intensive care units (ICUs) is often used as an indication of care quality. We performed a retrospective cohort study of acute care separations from four adult public hospitals in the state of South Australia, Australia. Data were derived from the Integrated South Australian Activity Collection (ISAAC) database, subdivided into those admitted to ICU or non-ICU (Ward) in tertiary referral or (other major) metropolitan hospitals. During the five-year study period (1 July 2013 to 30 June 2018), there were 471,934 adult separations with 65,133 HAC events reported in 43,987 (9.32%) at a mean rate of 13.8 (95% confidence interval (CI) 13.7 to 13.9) HAC events per 100 separations and 18.5 (95% CI 18.4 to 18.7) per 1000 bed days. The Ward cohort accounted for the majority (430,583 (91.2%)) of separations, in-hospital deaths (6928 (66.4%)) and HAC events (29,826 (67.8%)). The smaller ICU cohort (41,351 (8.76%)) had a higher mortality rate (8.46% versus 1.61%; P < 0.001), longer length of stay (median 10.0 (interquartile range (IQR) 6.0-18.0) days versus 4.0 (IQR 3.0-8.0) days P < 0.001), and higher HAC prevalence (62.1 (95% CI 61.3 to 62.9) versus 9.16 (95% CI 9.07 to 9.25) per 100 separations P < 0.001). Both ICU and Ward HAC prevalence rates were higher in tertiary referral than major metropolitan hospitals (P < 0.001). In conclusion, higher HAC prevalence rates in the ICU and tertiary referral cohorts may be due to high-risk patient cohorts, variable provision of care, or both, and warrants urgent clinical investigation and further research.

Performance of hospital administrative data for detection of sepsis in Australia: The sepsis coding and documentation (SECOND) study.

BACKGROUND: Sepsis is the world's leading cause of death and its detection from a range of data and coding sources, consistent with consensus clinical definition, is desirable. OBJECTIVE: To evaluate the performance of three coding definitions (explicit, implicit, and newly proposed synchronous method) for sepsis derived from administrative data compared to a clinical reference standard. METHOD: Extraction of administrative coded data from Australian metropolitan teaching hospital with 25,000 annual overnight admissions compared to clinical review of medical records; 313 (27.9%) randomly selected adult multi-day stay hospital separations from 1,123 separations with acute infection during July 2019. Estimated prevalence and performance metrics, including positive (PPV) and negative predictive values (NPV), and area under the receiver operator characteristic curve (ROC). RESULTS: Clinical prevalence of sepsis was estimated at 10.7 (95% CI = 10.3-11.3) per 100 separations, and mortality rate of 11.6 (95% CI = 10.3-13.0) per 100 sepsis separations. Explicit method for case detection had high PPV (93.2%) but low NPV (55.8%) compared to the standard implicit method (74.1 and 66.3%, respectively) and proposed synchronous method (80.4% and 80.0%) compared to a standard clinical case definition. ROC for each method: 0.618 (95% CI = 0.538-0.654), 0.698 (95% CI = 0.648-0.748), and 0.802 (95% CI = 0.757-0.846), respectively. CONCLUSION: In hospitalised Australian patients with community-onset sepsis, the explicit method for sepsis case detection underestimated prevalence. Implicit methods were consistent with consensus definition for sepsis, and proposed synchronous method had better performance.

Hospital-acquired complications: the relative importance of hospital- and patient-related factors.

OBJECTIVE: To quantify the prevalence of hospital-acquired complications; to determine the relative influence of patient- and hospital-related factors on complication rates. DESIGN, PARTICIPANTS: Retrospective analysis of administrative data (Integrated South Australian Activity Collection; Victorian Admitted Episodes Dataset) for multiple-day acute care episodes for adults in public hospitals. SETTING: Thirty-eight major public hospitals in South Australia and Victoria, 2015-2018. MAIN OUTCOME MEASURES: Hospital-acquired complication rates, overall and by complication class, by hospital and hospital type (tertiary referral, major metropolitan service, major regional service); variance in rates (intra-class correlation coefficient, ICC) at the patient, hospital, and hospital type levels as surrogate measures of their influence on rates. RESULTS: Of 1 558 978 public hospital episodes (10 029 918 bed-days), 151 486 included a total of 214 286 hospital-acquired complications (9.72 [95% CI, 9.67-9.77] events per 100 episodes; 2.14 [95% CI, 2.13-2.15] events per 100 bed-days). Complication rates were highest in tertiary referral hospitals (12.7 [95% CI, 12.6-12.8] events per 100 episodes) and for episodes including intensive care components (37.1 [95% CI, 36.7-37.4] events per 100 episodes). For all complication classes, inter-hospital variation was determined more by patient factors (overall ICC, 0.55; 95% CI, 0.53-0.57) than by hospital factors (ICC, 0.04; 95% CI, 0.02-0.07) or hospital type (ICC, 0.01; 95% CI, 0.001-0.03). CONCLUSIONS: Hospital-acquired complications were recorded for 9.7% of hospital episodes, but patient-related factors played a greater role in determining their prevalence than the treating hospital.

Clinical evaluation of the national hospital-acquired complication programme.

BACKGROUND: The national hospital-acquired complication programme captures complications arising from patient-related and hospital-related factors, but the proportion of the two is unclear. AIM: Health services are encouraged to evaluate data from the national hospital-acquired complications (HAC) programme and identify strategies to mitigate them. METHODS: A retrospective chart review compared HAC extracted from administrative data. The setting was a 430-bed university-affiliated metropolitan hospital. Records from 260 participants with, and 462 without, reported HAC from 2619 multi-day stay adults were reviewed. The main outcome measures were prevalence and positive predictive value (PPV) of HAC methodology. RESULTS: No errors of HAC coding or classification were identified. Four hundred and twenty-three HAC events were reported in 260 records; most commonly delirium (n = 57; 13.4%), pneumonia (n = 46; 10.9%), blood stream infection (n = 39; 9.2%), hypoglycaemia (n = 33; 7.8%) and cardiac arrhythmias (n = 33; 7.8%). One hundred and eight (25.5%) 'HAC' events in 69 separations (95% confidence interval (CI) = 2.05-3.33 per 100 separations) were false positive, and 43 of 462 (95% CI = 6.72-12.22 per 100 separations) were false negative. Prevalence of total (reported plus missing) HAC was 16.06 (95% CI = 14.02-19.52), reported HAC was 9.93 (95% CI = 8.76-11.21), potentially preventable HAC was 1.68 (95% CI = 1.22-2.26) and healthcare errors was 0.31 (95% CI = 0.13-1.30) per 100 separations. PPV of HAC for true clinical events was 0.74 (0.68-0.79), preventable events 0.18 (0.13-0.23) and healthcare error 0.03 (0.01-0.06). CONCLUSIONS: Prevalence of HAC events was higher than expected, but PPV for healthcare errors was low, suggesting provision of care is a less common cause of HAC events than patient factors. HAC may be an indicator of hospital admission complexity rather than HAC.

Modelling hospital outcome: problems with endogeneity.

BACKGROUND: Mortality modelling in the critical care paradigm traditionally uses logistic regression, despite the availability of estimators commonly used in alternate disciplines. Little attention has been paid to covariate endogeneity and the status of non-randomized treatment assignment. Using a large registry database, various binary outcome modelling strategies and methods to account for covariate endogeneity were explored. METHODS: Patient mortality data was sourced from the Australian & New Zealand Intensive Society Adult Patient Database for 2016. Hospital mortality was modelled using logistic, probit and linear probability (LPM) models with intensive care (ICU) providers as fixed (FE) and random (RE) effects. Model comparison entailed indices of discrimination and calibration, information criteria (AIC and BIC) and binned residual analysis. Suspect covariate and ventilation treatment assignment endogeneity was identified by correlation between predictor variable and hospital mortality error terms, using the Stata™ "eprobit" estimator. Marginal effects were used to demonstrate effect estimate differences between probit and "eprobit" models. RESULTS: The cohort comprised 92,693 patients from 124 intensive care units (ICU) in calendar year 2016. Patients mean age was 61.8 (SD 17.5) years, 41.6% were female and APACHE III severity of illness score 54.5(25.6); 43.7% were ventilated. Of the models considered in predicting hospital mortality, logistic regression (with or without ICU FE) and RE logistic regression dominated, more so the latter using information criteria indices. The LPM suffered from many predictions outside the unit [0,1] interval and both poor discrimination and calibration. Error terms of hospital length of stay, an independent risk of death score and ventilation status were correlated with the mortality error term. Marked differences in the ventilation mortality marginal effect was demonstrated between the probit and the "eprobit" models which were scenario dependent. Endogeneity was not demonstrated for the APACHE III score. CONCLUSIONS: Logistic regression accounting for provider effects was the preferred estimator for hospital mortality modelling. Endogeneity of covariates and treatment variables may be identified using appropriate modelling, but failure to do so yields problematic effect estimates.

Hospital-acquired complications in critically ill patients.

Background: The national hospital-acquired complications (HAC) system has been promoted as a method to identify health care errors that may be mitigated by clinical interventions. Objectives: To quantify the rate of HAC in multiday stay adults admitted to major hospitals. Design: Retrospective observational analysis of 5-year (July 2014 - June 2019) administrative dataset abstracted from medical records. Setting: All 47 hospitals with on-site intensive care units (ICUs) in the State of Victoria. Participants: All adults (aged ≥ 18 years) stratified into planned or unplanned, surgical or medical, ICU or other ward, and by hospital peer group (tertiary referral, metropolitan, regional). Main outcome measures: HAC rates in ICU compared with ward, and mixed-effects regression estimates of the association between HAC and i) risk of clinical deterioration, and ii) admission hospital site (intraclass correlation coefficient [ICC] > 0.3). Results: 211 120 adult ICU separations with mean hospital mortality of 7.3% (95% CI, 7.2-7.4%) reported 110 132 (42.6%) HAC events (commonly, delirium, infection, arrhythmia and respiratory failure) in 62 945 records (29.8%). Higher HAC rates were reported in elective (cardiac [50.3%] and non-cardiac [40.6%]) surgical subgroups compared with emergency medical subgroup (23.9%), and in tertiary (35.4%) compared with non-tertiary (22.7%) hospitals. HAC was strongly associated with on-admission patient characteristics (P < 0.001), but was weakly associated with hospital site (ICC, 0.08; 95% CI, 0.05-0.11). Conclusions: Critically ill patients have a high burden of HAC events, which appear to be associated with patient admission characteristics. HAC may an indicator of hospital admission complexity rather than hospital-acquired complications.

Safety of the endotracheal tube for prolonged mechanical ventilation.

RATIONALE: The endotracheal tube (ETT) is the most common route for invasive mechanical ventilation (MV) yet controversy attends its long-term safety. OBJECTIVE: Assess the safety of ETT compared with tracheostomy tube (TT) for MV support in the intensive care unit (ICU). METHODS: Retrospective analysis of five year national dataset of 128,977 adults (age > 15-years) admitted for MV therapy with tracheostomy tube (TT; n = 4772) or without (ETT; n = 124,204), excluding those with neurological diagnoses or likely to require a surgical airway (n = 27,466), in 93 public health service ICUs across Australia, between July 2013-June 2018. MEASUREMENTS: Hospital survival (including liberation from MV) for ETT Group compared with TT Group using a probit regression model adjusted for confounding using fixed, endogenous and non-random treatment assignment covariates, and their interactions; analysed and plotted as marginal effects by duration of MV. RESULTS: Median duration of MV was 2 (IQR =1-4) days, predominantly via ETT (124,205; 96.3%), and 21,620 (16.7%) died. Temporal trend for ETT increased (OR = 1.06 per year, 95%CI =1.03-1.10) compared to TT, even for prolonged (>3 weeks) MV (38.1%). Higher risk-adjusted mortality was associated with longer duration of MV and after 9 days of MV with retention of ETT compared with TT - average (mortality) treatment effect 12.6% (95%CI =10.7-14.5). The latter was not significant after 30 days of MV. CONCLUSIONS: The safety of ETT compared with TT beyond short-term MV (≤9-days) is uncertain and requires prospective evaluation with additional data.

Severe acute maternal morbidity trends in Victoria, 2001-2017.

BACKGROUND: The incidence of severe acute maternal morbidity (SAMM) is one method of measuring the complexity of maternal health and monitoring maternal outcomes. Monitoring trends may provide a quantitative method for assessing health care at local, regional, or jurisdictional levels and identify issues for further investigation. AIMS: Identify temporal trends for SAMM event rates and maternal outcomes over 17 years in the state of Victoria, Australia. MATERIALS AND METHODS: All maternal public health service admissions were extracted from an administrative dataset from July 2000 to June 2017. SAMM-related diagnoses were defined by matching as closely as possible with published definitions. Outcomes included annual SAMM event rates, hospital survival, and hospital length of stay (LOS). Temporal trends were analysed using mixed-effects generalised linear models. RESULTS: There were 854 777 live births and 1.21 million pregnancy-related hospital admissions which included 34 008 SAMM events in 29 273 records and in 3.42% (95%CI = 3.39-3.46) of births. Most common were severe pre-eclampsia (0.87% of births), severe postpartum haemorrhage (0.59%), and sepsis (0.62%). SAMM-related admissions were associated with longer LOS and higher mortality risk (P < 0.001). Maternal mortality ratio remained unchanged at 8.6 fatalities per 100 000 births (P = 0.65). CONCLUSION: Over 17 years, there was a significant increase in birth rate and SAMM-related events in Victoria. Administrative data may provide a pragmatic approach for monitoring SAMM-related events in maternal health services.

Readmissions to Intensive Care: A Prospective Multicenter Study in Australia and New Zealand.

OBJECTIVES: To determine factors independently associated with readmission to ICU and the independent association of readmission with subsequent mortality. DESIGN: Prospective multicenter observational study. SETTING: Forty ICUs in Australia and New Zealand. PATIENTS: Consecutive adult patients discharged alive from ICU to hospital wards between September 2009 and February 2010. INTERVENTIONS: Measurement of hospital mortality. MEASUREMENTS AND MAIN RESULTS: We studied 10,210 patients and 674 readmissions. The median age was 63 years (interquartile range, 49-74), and 6,224 (61%) were male. The majority of readmissions were unplanned (84.1%) but only deemed preventable in a minority (8.9%) of cases. Time to first readmission was shorter for unplanned than planned readmission (3.2 vs 6.9 d; p < 0.001). Primary diagnosis changed between admission and readmission in the majority of patients (60.2%) irrespective of planned (58.2%) or unplanned (60.6%) status. Using recurrent event analysis incorporating patient frailty, we found no association between readmissions and hospital survival (hazard ratios: first readmission 0.88, second readmission 0.90, third readmission 0.44; p > 0.05). In contrast, age (hazard ratio, 1.03), a medical diagnosis (hazard ratio, 1.43), inotrope use (hazard ratio, 3.47), and treatment limitation order (hazard ratio, 17.8) were all independently associated with outcome. CONCLUSIONS: In this large prospective study, readmission to ICU was not an independent risk factor for mortality.

The timing of discharge from the intensive care unit and subsequent mortality. A prospective, multicenter study.

RATIONALE: Previous studies suggested an association between after-hours intensive care unit (ICU) discharge and increased hospital mortality. Their retrospective design and lack of correction for patient factors present at the time of discharge make this association problematic. OBJECTIVES: To determine factors independently associated with mortality after ICU discharge. METHODS: This was a prospective, multicenter, binational observational study involving 40 ICUs in Australia and New Zealand. Participants were consecutive adult patients discharged alive from the ICU between September 2009 and February 2010. MEASUREMENTS AND MAIN RESULTS: We studied 10,211 patients discharged alive from the ICU. Median age was 63 years (interquartile range, 49-74), 6,224 (61%) were male, 5,707 (56%) required mechanical ventilation, and their median Acute Physiology and Chronic Health Evaluation III risk of death was 9% (interquartile range, 3-25%). A total of 8,539 (83.6%) patients were discharged in-hours (06:00-18:00) and 1,672 (16.4%) after-hours (18:00-06:00). Of these, 408 (4.8%) and 124 (7.4%), respectively, subsequently died in hospital (P < 0.001). After risk adjustment for markers of illness severity at time of ICU discharge including limitations of medical therapy (LOMT) orders, the time of discharge was no longer a significant predictor of mortality. The presence of a LOMT order was the strongest predictor of death (odds ratio, 35.4; 95% confidence interval, 27.5-45.6). CONCLUSIONS: In this large, prospective, multicenter, binational observational study, we found that patient status at ICU discharge, particularly the presence of LOMT orders, was the chief predictor of hospital survival. In contrast to previous studies, the timing of discharge did not have an independent association with mortality.

Outcomes of older people receiving intensive care in Victoria.

UNLABELLED: OBJECTIVE To assess trends in service use and outcome of critically ill older people (aged ≥ 65 years) admitted to an intensive care unit (ICU). DESIGN, PATIENTS AND SETTING: Retrospective cohort analysis of administrative data on older patients discharged from ICUs at all 23 adult public hospitals with onsite ICUs in Victoria between 1 July 1999 and 30 June 2011. Subgroups examined included those aged ≥ 80 years, major diagnosis categories, and those receiving mechanical ventilation. MAIN OUTCOME MEASURES: Resource use and hospital survival; also length of stay (LOS) and discharge destination trends. RESULTS: Over 12 years, 108,171 people aged ≥ 65 years were admitted to ICUs; of these, 49,912 (46.1%) received mechanical ventilation and 17,772 (16.4%) died. Despite an increase in the older age population (2.5% per annum) and acute care admissions (7.3% per annum) over the period studied, there was a net reversal in prevalence trends for ICU admissions (- 1.7% per annum; P = 0.04) and admissions of patients requiring mechanical ventilation (- 1.6% per annum) in the 8 years since 2004. Annual risk-adjusted mortality fell (odds ratio, 0.97 per year; 95% CI, 0.96-0.97 per year; P < 0.001) without prolongation of hospital or ICU LOS (P = 0.49) or discharge to residential aged care (RAC). Similar trends were noted in all a priori subgroups. CONCLUSIONS: Improved hospital survival without an increase in demand for ICU admission or RAC or an increase in LOS suggests there has been improvement in the care of the older age population.

A brief review of recent trends in Victorian intensive care, 2000-2011.

BACKGROUND: Review of resource use and patient outcomes of intensive care unit services over time provides insights into service delivery and safety. OBJECTIVE: To examine temporal trends in resource consumption and risk-adjusted mortality of adult ICU patients in Victoria. DESIGN, PARTICIPANTS AND SETTING: Retrospective cohort study of 214 619 adult ICU admissions recorded from 23 major hospitals over 12 years from 1 July 1999 to 30 June 2011. OUTCOMES: Primary outcomes were population rates of ICU admission and mechanical ventilation (MV), ICU and hospital length of stay, and hospital survival. Secondary outcomes included average ICU and MV bed numbers. Administrative data were derived from the Victorian Admitted Episodes Dataset and the Australian Bureau of Statistics. The Critical Care Outcome Prediction Equation informed estimates for risk-adjusted mortality. Temporal mortality trends were evaluated for outcome estimates and hierarchical logisticregression trends were evaluated for risk-adjusted mortality. RESULTS: Of ICU admissions, 104 103 (48.5%) were patients who received MV, and 87.6% ICU admissions were adults who survived to hospital discharge. There was a decline in the risk-adjusted mortality (odds ratio, 0.967 per year; 95% CI, 0.963-0.971; P<0.001). Similar results were found in 17 hospitals (74%) and in nine of 10 major diagnostic subgroups. There was an increase of 5.2 occupied ICU beds per year (range, ?4.2 ICU beds per year; P=0.002). Despite ICU admissions being a minority cohort (2.5% of public hospital admissions) this group used 8.6% of hospital bed-days and attracted 19.5% of funding. CONCLUSIONS: There was an increase in ICU resource availability and evidence of improvement in hospital survival, suggesting improved quality of care. These evaluation methods may be useful in monitoring statewide capacity, service delivery and patient safety.

Development and validation of the critical care outcome prediction equation, version 4.

OBJECTIVE: To revise and validate the accuracy of the critical care outcome prediction equation (COPE) model, version 4. DESIGN, PARTICIPANTS AND SETTING: Observational cohort analysis of 214 616 adult consecutive intensive care unit admissions recorded from 23 ICUs over 12 years. Data derived from the Victorian Admitted Episode Database (VAED) were used to identify treatment-independent risk factors consistently associated with hospital mortality. A revised version of the COPE-4 model using a random intercept hierarchical logistic regression model was developed in a sample of 35 878 (16.7%) consecutive ICU separations. MAIN OUTCOME MEASURES: Accuracy was tested by comparing observed and predicted mortality in the remaining 178 741 (83.3%) records and in 23 institutional cohorts. Stability was assessed using the standardised mortality ratio, Hosmer-Lemeshow H10 statistic, calibration plot and Brier score. RESULTS: The COPE-4 model had satisfactory overall discrimination with an area under receiver operating characteristic curve of 0.82 for both datasets. The development and validation datasets demonstrated good overall calibration with H10 statistics of 13.38 (P = 0.10) and 14.84 (P = 0.06) and calibration plot slopes of 0.99 and 1.034, respectively. Discrimination was satisfactory in all 23 hospitals and one or more calibration criteria were achieved in 19 hospitals (83%). CONCLUSIONS: COPE-4 model prediction of hospital mortality for ICU admissions has satisfactory performance for use as a risk-adjustment tool in Victoria. Model refinement may further improve its performance.

The role of the medical emergency team in end-of-life care: a multicenter, prospective, observational study.

OBJECTIVE: To investigate the role of medical emergency teams in end-of-life care planning. DESIGN: One month prospective audit of medical emergency team calls. SETTING: Seven university-affiliated hospitals in Australia, Canada, and Sweden. PATIENTS: Five hundred eighteen patients who received a medical emergency team call over 1 month. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 652 medical emergency team calls in 518 patients, with multiple calls in 99 (19.1%) patients. There were 161 (31.1%) patients with limitations of medical therapy during the study period. The limitation of medical therapy was instituted in 105 (20.3%) and 56 (10.8%) patients before and after the medical emergency team call, respectively. In 78 patients who died with a limitation of medical therapy in place, the last medical emergency team review was on the day of death in 29.5% of patients, and within 2 days in another 28.2%.Compared with patients who did not have a limitation of medical therapy, those with a limitation of medical therapy were older (80 vs. 66 yrs; p < .001), less likely to be male (44.1% vs. 55.7%; p = .014), more likely to be medical admissions (70.8% vs. 51.3%; p < .001), and less likely to be admitted from home (74.5% vs. 92.2%, p < .001). In addition, those with a limitation of medical therapy were less likely to be discharged home (22.4% vs. 63.6%; p < .001) and more likely to die in hospital (48.4% vs. 12.3%; p < .001). There was a trend for increased likelihood of calls associated with limitations of medical therapy to occur out of hours (51.0% vs. 43.8%, p = .089). CONCLUSIONS: Issues around end-of-life care and limitations of medical therapy arose in approximately one-third of calls, suggesting a mismatch between patient needs for end-of-life care and resources at participating hospitals. These calls frequently occur in elderly medical patients and out of hours. Many such patients do not return home, and half die in hospital. There is a need for improved advanced care planning in our hospitals, and to confirm our findings in other organizations.

Is ED length of stay before ICU admission related to patient mortality?

OBJECTIVE: To describe and identify the relationship between ED length of stay (LOS) and mortality after ICU admission. METHODS: We undertook a retrospective cohort study of records from the Australian and New Zealand Intensive Care Society Adult Patient Database (from 1 January 2000 to 31 December 2006). Data from 45 hospitals and 48 803 ED patients directly transferred to ICU were included. Patients were divided into ED LOS<8 h and ED LOS>or=8 h. Univariate and multivariate analyses were performed. RESULTS: Median ED LOS was 3.9 h (interquartile range 2.0-6.8). Patients transferred within 8 h (80.9%) were younger (P<0.001) and more seriously ill (higher mortality and mechanical ventilation rate) than those transferred>or=8 h. There was no clear relationship between ED LOS and hospital survival for patients admitted directly to ICU (odds ratio=1.01 per hour, 95% confidence intervals 0.99-1.02). CONCLUSION: Although 20% of critically ill patients spend more than 8 h in ED before transfer to ICU, we were unable to demonstrate an adverse relationship between time in ED and hospital mortality.