Thirty years of ANZICS CORE: A clinical quality success story.

In 2023, the Australian and New Zealand Intensive Care Society (ANZICS) Registry run by the Centre for Outcomes and Resources Evaluation (CORE) turns 30 years old. It began with the Adult Patient Database, the Australian and New Zealand Paediatric Intensive Care Registry, and the Critical Care Resources Registry, and it now includes Central Line Associated Bloodstream Infections Registry, the Extra-Corporeal Membrane Oxygenation Database, and the Critical Health Resources Information System. The ANZICS Registry provides comparative case-mix reports, risk-adjusted clinical outcomes, process measures, and quality of care indicators to over 200 intensive care units describing more than 200 000 adult and paediatric admissions annually. The ANZICS CORE outlier management program has been a major contributor to the improved patient outcomes and provided significant cost savings to the healthcare sector. Over 200 peer-reviewed papers have been published using ANZICS Registry data. The ANZICS Registry was a vital source of information during the COVID-19 pandemic. Upcoming developments include reporting of long-term survival and patient-reported outcome and experience measures.

Measuring the Impact of ICU Strain on Mortality, After-Hours Discharge, Discharge Delay, Interhospital Transfer, and Readmission in Australia With the Activity Index.

OBJECTIVES: ICU resource strain leads to adverse patient outcomes. Simple, well-validated measures of ICU strain are lacking. Our objective was to assess whether the "Activity index," an indicator developed during the COVID-19 pandemic, was a valid measure of ICU strain. DESIGN: Retrospective national registry-based cohort study. SETTING: One hundred seventy-five public and private hospitals in Australia (June 2020 through March 2022). SUBJECTS: Two hundred seventy-seven thousand seven hundred thirty-seven adult ICU patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data from the Australian and New Zealand Intensive Care Society Adult Patient Database were matched to the Critical Health Resources Information System. The mean daily Activity index of each ICU (census total of "patients with 1:1 nursing" + "invasive ventilation" + "renal replacement" + "extracorporeal membrane oxygenation" + "active COVID-19," divided by total staffed ICU beds) during the patient's stay in the ICU was calculated. Patients were categorized as being in the ICU during very quiet (Activity index < 0.1), quiet (0.1 to < 0.6), intermediate (0.6 to < 1.1), busy (1.1 to < 1.6), or very busy time-periods (≥ 1.6). The primary outcome was in-hospital mortality. Secondary outcomes included after-hours discharge from the ICU, readmission to the ICU, interhospital transfer to another ICU, and delay in discharge from the ICU. Median Activity index was 0.87 (interquartile range, 0.40-1.24). Nineteen thousand one hundred seventy-seven patients died (6.9%). In-hospital mortality ranged from 2.4% during very quiet to 10.9% during very busy time-periods. After adjusting for confounders, being in an ICU during time-periods with higher Activity indices, was associated with an increased risk of in-hospital mortality (odds ratio [OR], 1.49; 99% CI, 1.38-1.60), after-hours discharge (OR, 1.27; 99% CI, 1.21-1.34), readmission (OR, 1.18; 99% CI, 1.09-1.28), interhospital transfer (OR, 1.92; 99% CI, 1.72-2.15), and less delay in ICU discharge (OR, 0.58; 99% CI, 0.55-0.62): findings consistent with ICU strain. CONCLUSIONS: The Activity index is a simple and valid measure that identifies ICUs in which increasing strain leads to progressively worse patient outcomes.

The impact of language barriers & interpreters on critical care patient outcomes.

BACKGROUND: In a multicultural society, the impact of language proficiency and interpreter use on critical care patient outcomes is unknown. OBJECTIVE: To investigate the relationship between English language preference, requirement for an interpreter and in-hospital mortality amongst non-elective intensive care unit (ICU) patients. METHOD: Adult patients admitted to all 23 public ICUs within the state of Victoria, Australia from July 2007 to June 2018, were extracted from The Australian New Zealand Intensive Care Society Adult Patient Database. De-identified patient data was matched using probabilistic methods and statistical linkage keys to the Victorian Admitted Episodes Database. Patients were classified into one of three groups: 'English preferred', 'English not preferred' and 'Interpreter required'. RESULTS: 126,891 ICU admissions were analysed, of whom 3394 (3%) were in the 'English not preferred' group and 6355 (5%) in the 'Interpreter required' group. Compared to the 'English preferred', both the 'English not preferred' and 'Interpreter required' groups were older, had more co-morbidities and higher severity of illness scores. In-hospital mortality was 13.1% in the 'English preferred' group, 19.6% in the 'English not preferred' group and 16.7% in the 'Interpreter required' group. However, after adjusting for sex, severity of illness and socio-economic status, the 'English not preferred' group remained with a higher risk adjusted mortality (OR 1.21, 95%CI 1.07-1.36, P = 0.002), whereas the 'Interpreter required' group had a lower adjusted risk of mortality (OR 0.81, 95%CI 0.74-0.89, P < 0.001). CONCLUSION: Being identified as having a requirement for an interpreter was associated with improved outcomes for adults admitted to public hospital ICUs in Victoria. Interpreter services should be more readily available in the hospital setting. It is recommended that patients, family members and clinicians actively use interpreter services when English is not the preferred language of an ICU patient.

Twelve-month mortality outcomes for Indigenous and non-Indigenous people admitted to intensive care units in Australia: a registry-based data linkage study.

OBJECTIVE: To compare longer term (12-month) mortality outcomes for Indigenous and non-Indigenous people admitted to intensive care units (ICUs) in Australia. DESIGN, SETTING, PARTICIPANTS: Retrospective registry-based data linkage cohort study; analysis of all admissions of adults (16 years or older) to Australian ICUs, 1 January 2017 - 31 December 2019, as recorded in the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD), linked using the SLK-581 key to National Death Index data. MAIN OUTCOME MEASURES: Unadjusted and adjusted mortality risk, censored at twelve months from the start of index ICU admission. Secondary outcomes were unadjusted and adjusted mortality twelve months from admission to the ICU. RESULTS: The APD recorded 330 712 eligible ICU admissions during 2017-2019 (65% of all ICU admissions registered), of which 11 322 were of Indigenous people (3.4%). Median age at admission was lower for Indigenous patients (51.2 [IQR, 36.7-63.6] years) than for non-Indigenous patients (66.5 [IQR, 52.7-76.1] years). Unadjusted mortality risk was similar for Indigenous and non-Indigenous patients (hazard ratio, 1.01; 95% CI, 0.97-1.06), but was higher for Indigenous patients after adjusting for age, admission diagnosis, illness severity, hospital type, jurisdiction, remoteness and socio-economic status (adjusted hazard ratio, 1.20; 95% CI, 1.14-1.27). Twelve-month mortality was higher for Indigenous than non-Indigenous patients (adjusted odds ratio, 1.24; 95% CI, 1.16-1.33). CONCLUSIONS: Twelve-month mortality outcomes are poorer for people admitted to ICUs in Australia than for the general population. Further, after adjusting for age and other factors, survival outcomes are poorer for Indigenous than non-Indigenous people admitted to ICUs. Critical illness may therefore contribute to shorter life expectancy among Indigenous Australians.

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

OBJECTIVE: The objective of this study was to describe family visitation policies, facilities, and support in Australia and New Zealand (ANZ) intensive care units (ICUs). METHODS: A survey was distributed to all Australian and New Zealand ICUs reporting to the Australian and New Zealand Intensive Care Society Centre for Outcomes and Resources Evaluation Critical Care Resources (CCR) Registry in 2018. Data were obtained from the survey and from data reported to the CCR Registry. For this study, open visiting (OV) was defined as allowing visitors for more than 14 h per day. SETTING AND PARTICIPANTS: This study included all Australian and New Zealand ICUs reporting to CCR in 2018. MAIN OUTCOME MEASURES: The main outcome measures were family access to the ICU and visiting hours, characteristics of the ICU waiting area, and information provided to and collected from the relatives. FINDINGS: Fifty-six percent (95/170) of ICUs contributing to CCR responded, representing 44% of ANZ ICUs and a range of rural, metropolitan, tertiary, and private ICUs. Visiting hours ranged from 1.5 to 24 h per day, with 68 (72%) respondent ICUs reporting an OV policy, of which 64 (67%) ICUs were open to visitors 24 h a day. A waiting room was part of the ICU for 77 (81%) respondent ICUs, 74 (78%) reported a separate dedicated room for family meetings, and 83 (87%) reported available social worker services. Most ICUs reported facilities for sleeping within or near the hospital. An information booklet was provided by 64 (67%) ICUs. Only six (6%) ICUs required personal protective equipment for all visitors, and 76 (80%) required personal protective equipment for patients with airborne precautions. CONCLUSIONS: In 2018, the majority of ANZ ICUs reported liberal visiting policies, with substantial facilities and family support.

Increasing ICU capacity to accommodate higher demand during the COVID-19 pandemic.

OBJECTIVES: To describe the short term ability of Australian intensive care units (ICUs) to increase capacity in response to heightened demand caused by the COVID-19 pandemic. DESIGN: Survey of ICU directors or delegated senior clinicians (disseminated 30 August 2021), supplemented by Australian and New Zealand Intensive Care Society (ANZICS) registry data. SETTING: All 194 public and private Australian ICUs. MAIN OUTCOME MEASURES: Numbers of currently available and potentially available ICU beds in case of a surge; available levels of ICU-relevant equipment and staff. RESULTS: All 194 ICUs responded to the survey. The total number of currently open staffed ICU beds was 2183. This was 195 fewer (8.2%) than in 2020; the decline was greater for rural/regional (18%) and private ICUs (18%). The reported maximal ICU bed capacity (5623) included 813 additional physical ICU bed spaces and 2627 in surge areas outside ICUs. The number of available ventilators (7196) exceeded the maximum number of ICU beds. The reported number of available additional nursing staff would facilitate the immediate opening of 383 additional physical ICU beds (47%), but not the additional bed spaces outside ICUs. CONCLUSIONS: The number of currently available staffed ICU beds is lower than in 2020. Equipment shortfalls have been remediated, with sufficient ventilators to equip every ICU bed. ICU capacity can be increased in response to demand, but is constrained by the availability of appropriately trained staff. Fewer than half the potentially additional physical ICU beds could be opened with currently available staff numbers while maintaining pre-pandemic models of care.

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

INTRODUCTION: Rehabilitation outcomes in cardiac arrest survivors are largely unknown, with no data comparing out-of-hospital cardiac arrests (OHCA) and in-hospital cardiac arrests (IHCA). This study aimed to describe and compare inpatient rehabilitation outcomes in these patients who were admitted from intensive care units (ICU). METHODS: A retrospective linkage and analysis of cardiac arrest patients in the Australian and New Zealand Intensive Care Society Adult Patient Database and the Australasian Rehabilitation Outcomes Centre inpatient dataset discharged to inpatient rehabilitation between January 2017 and June 2018. Primary outcome was the functional improvement during rehabilitation (difference between the Functional Independence Measurement (FIM) score on admission and discharge). Multivariate regression analyses were performed to determine factors associated with functional improvement. RESULTS: In the 240 (84 OHCA and 156 IHCA) patients included, the median length of inpatient rehabilitation was 15 days [1st-3rd quartile (Q1-Q3): 9-24]. OHCA patients were more likely to be admitted to rehabilitation for neurological issues (41.7%) and IHCA for medical reasons (51.9%). Median (Q1-Q3) change in total FIM scores was similar between the two groups (24.5[10-37]) vs 21[11-31], adjusted p = 0.20), with most of the FIM change seen in the motor items, and this was only associated with a lower admission FIM score. The majority of OHCA and IHCA patients were discharged home (91.5% and 89.7%, respectively), although with an increased need for a carer at home compared to baseline (27.2% to 55.6%). CONCLUSION: Patients discharged from ICU following OHCA and IHCA achieved reasonable and similar functional improvement during inpatient rehabilitation.

Linkage of Australian national registry data using a statistical linkage key.

BACKGROUND: Data from clinical registries may be linked to gain additional insights into disease processes, risk factors and outcomes. Identifying information varies from full names, addresses and unique identification codes to statistical linkage keys to no direct identifying information at all. A number of databases in Australia contain the statistical linkage key 581 (SLK-581). Our aim was to investigate the ability to link data using SLK-581 between two national databases, and to compare this linkage to that achieved with direct identifiers or other non-identifying variables. METHODS: The Australian and New Zealand Society of Cardiothoracic Surgeons database (ANZSCTS-CSD) contains fully identified data. The Australian and New Zealand Intensive Care Society database (ANZICS-APD) contains non-identified data together with SLK-581. Identifying data is removed at participating hospitals prior to central collation and storage. We used the local hospital ANZICS-APD data at a large single tertiary centre prior to deidentification and linked this to ANZSCTS-CSD data. We compared linkage using SLK-581 to linkage using non-identifying variables (dates of admission and discharge, age and sex) and linkage using a complete set of unique identifiers. We compared the rate of match, rate of mismatch and clinical characteristics between unmatched patients using the different methods. RESULTS: There were 1283 patients eligible for matching in the ANZSCTS-CSD. 1242 were matched using unique identifiers. Using non-identifying variables 1151/1242 (92.6%) patients were matched. Using SLK-581, 1202/1242 (96.7%) patients were matched. The addition of non-identifying data to SLK-581 provided few additional patients (1211/1242, 97.5%). Patients who did not match were younger, had a higher mortality risk and more non-standard procedures vs matched patients. The differences between unmatched patients using different matching strategies were small. CONCLUSION: All strategies provided an acceptable linkage. SLK-581 improved the linkage compared to non-identifying variables, but was not as successful as direct identifiers. SLK-581 may be used to improve linkage between national registries where identifying information is not available or cannot be released.

Surge capacity of intensive care units in case of acute increase in demand caused by COVID-19 in Australia.

OBJECTIVES: To assess the capacity of intensive care units (ICUs) in Australia to respond to the expected increase in demand associated with COVID-19. DESIGN: Analysis of Australian and New Zealand Intensive Care Society (ANZICS) registry data, supplemented by an ICU surge capability survey and veterinary facilities survey (both March 2020). SETTINGS: All Australian ICUs and veterinary facilities. MAIN OUTCOME MEASURES: Baseline numbers of ICU beds, ventilators, dialysis machines, extracorporeal membrane oxygenation machines, intravenous infusion pumps, and staff (senior medical staff, registered nurses); incremental capability to increase capacity (surge) by increasing ICU bed numbers; ventilator-to-bed ratios; number of ventilators in veterinary facilities. RESULTS: The 191 ICUs in Australia provide 2378 intensive care beds during baseline activity (9.3 ICU beds per 100 000 population). Of the 175 ICUs that responded to the surge survey (with 2228 intensive care beds), a maximal surge would add an additional 4258 intensive care beds (191% increase) and 2631 invasive ventilators (120% increase). This surge would require additional staffing of as many as 4092 senior doctors (245% increase over baseline) and 42 720 registered ICU nurses (269% increase over baseline). An additional 188 ventilators are available in veterinary facilities, including 179 human model ventilators. CONCLUSIONS: The directors of Australian ICUs report that intensive care bed capacity could be near tripled in response to the expected increase in demand caused by COVID-19. But maximal surge in bed numbers could be hampered by a shortfall in invasive ventilators and would also require a large increase in clinician and nursing staff numbers.

Degree of hyperglycemia independently associates with hospital mortality and length of stay in critically ill, nondiabetic patients: Results from the ANZICS CORE binational registry.

PURPOSE: Hyperglycemia (HG) in critically ill patients influences clinical outcomes and hospitalization costs. We aimed to describe association of HG with hospital mortality and length of stay in large scale, real-world scenario. MATERIALS: From The Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD) we included 739,152 intensive care unit (ICU) patients admitted during 2007-2016. Hyperglycemia was quatified using midpoint blood glucose level (MBGL). Association with outcomes (hospital mortality and length of stay (LOS)) was tested using multivariable, mixed effects, 2-level hierarchical regression. RESULTS: Degree of HG (defined using MBGL as a continuous variable) was significantly associated with hospital mortality and longer hospital stay in a dose-dependent fashion. The fourth, third and second MBGL (compared to the first) quartiles were associated with hospital mortality (odds ratio 1.34, 1.05 and 0.97, respectively) and longer hospital stay (1.56, 1.38 and 0.93 days, respectively). These associations were stronger associations in trauma (especially head injury), neurological disease and coma patients. Significant variation across ICUs was observed for all associations. CONCLUSIONS: In this largest study of nondiabetic ICU patients, HG was associated with both study outcomes. This association was differential across ICUs and diagnostic categories.

Independent Association of Glucose Variability With Hospital Mortality in Adult Intensive Care Patients: Results From the Australia and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Binational Registry.

Wide variations in blood glucose excursions in critically ill patients may influence adverse outcomes such as hospital mortality. However, whether blood glucose variability is independently associated with mortality or merely captures the excess risk attributable to hyperglycemic and hypoglycemic episodes is not established. We investigated whether blood glucose variability independently predicted hospital mortality in nonhyperglycemic critical care patients. DESIGN: Retrospective, registry data analyses of outcomes. SETTING: Large, binational registry (Australia and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Adult Patient Database repository) of 176 ICUs across Australia and New Zealand. PATIENTS: We used 10-year data on nonhyperglycemic patients registered in the Australia and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Adult Patient Database repository (n = 290,966). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Glucose variability was captured using glucose width defined as the difference between highest and lowest blood glucose concentration within first 24 hours of ICU admission. We used hierarchical, mixed effects logistic regression models that accounted for ICU variation and several fixed-effects covariates. Glucose width was specifically and independently associated with hospital mortality. The association of blood glucose variability with mortality remained significant (odds ratio for highest vs lowest quartile of glucose, 1.43; 95% CI, 1.32-1.55; p < 0.001) even after adjusting for the baseline risk of mortality, midpoint blood glucose level, occurrence of hypoglycemia and inter-ICU variation. Mixed effects modeling showed that there was a statistically significant variation in this association across ICUs. CONCLUSIONS: Our study demonstrates that glucose variability is independently associated with hospital mortality in critically ill adult patients. Inclusion of correction for glucose variability in glycemic control protocols needs to be investigated in future studies.

Predicting Expected Organ Donor Numbers in Australian Hospitals Outside of the Donate-Life Network Using the ANZICS Adult Patient Database.

BACKGROUND: The 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. METHODS: All 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. RESULTS: Of 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 (R = 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. CONCLUSIONS: It 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.

Identification and assessment of potentially high-mortality intensive care units using the ANZICS Centre for Outcome and Resource Evaluation clinical registry.

PURPOSE: A hospital's highest-risk patients are managed in the intensive care unit. Outcomes are determined by patients' severity of illness, existing comorbidities and by processes of care delivered. The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation (CORE) manages a binational clinical registry to benchmark performance, and report and assess ICUs which appear to have worse outcomes than others. METHODS: A descriptive retrospective cohort study was undertaken to detail processes, outcomes, limitations and practical lessons learnt from monitoring ICU performance throughout Australia and New Zealand. All ICUs contributing to the ANZICS Adult Patient Database between 2009 and 2014 were included. A potential outlier ICU was defined as one with a statistically significantly higher standardised mortality ratio (SMR) than its peer group. RESULTS: There were 757 188 admissions to 168 ICUs. Of these, 27 ICUs (16%) were identified as potential outlier ICUs at least once. Data quality problems led to inaccurate or artificially elevated SMRs at 16/27 ICUs. Variation in diagnostic casemix partly or completely explained the elevated SMR at 15/27 ICUs. At nine ICUs where data quality and casemix differences did not explain the elevated SMR, process-of-care problems were identified. CONCLUSIONS: A combination of routine monitoring techniques, statistical analysis and contextual interpretation of findings is required to ensure potential outlier ICUs are appropriately identified. This ensures engagement and understanding from clinicians and jurisdictional health departments, while contributing to the improvement of ICU practices throughout Australia and New Zealand.

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

BACKGROUND: Critical care physicians recognise persistent critical illness as a specific syndrome, yet few data exist for the timing of the transition from acute to persistent critical illness. Defining the onset of persistent critical illness as the time at which diagnosis and illness severity at intensive care unit (ICU) arrival no longer predict outcome better than do simple pre-ICU patient characteristics, we measured the timing of this onset at a population level in Australia and New Zealand, and the variation therein, and assessed the characteristics, burden of care, and hospital outcomes of patients with persistent critical illness. METHODS: In this retrospective, population-based, observational study, we used data for ICU admission in Australia and New Zealand from the Australian and New Zealand Intensive Care Society Adult Patient Database. We included all patients older than 16 years of age admitted to a participating ICU. We excluded patients transferred from another hospital and those admitted to an ICU for palliative care or awaiting organ donation. The primary outcome was in-hospital mortality. Using statistical methods in evenly split development and validation samples for risk score development, we examined the ability of characteristics to predict in-hospital mortality. FINDINGS: Between Jan, 2000, and Dec, 2014, we studied 1 028 235 critically ill patients from 182 ICUs across Australia and New Zealand. Among patients still in an ICU, admission diagnosis and physiological derangements, which accurately predicted outcome on admission (area under the receiver operating characteristics curve 0·898 [95% CI 0·897-0·899] in the validation cohort), progressively lost their predictive ability and no longer predicted outcome more accurately than did simple antecedent patient characteristics (eg, age, sex, or chronic health status) after 10 days in the ICU, thus empirically defining the onset of persistent critical illness. This transition occurred between day 7 and day 22 across diagnosis-based subgroups and between day 6 and day 15 across risk-of-death-based subgroups. Cases of persistent critical illness accounted for only 51 509 (5·0%) of the 1 028 235 patients admitted to an ICU, but for 1 029 345 (32·8%) of 3 138 432 ICU bed-days and 2 197 108 (14·7%) of 14 961 693 hospital bed-days. Overall, 12 625 (24·5%) of 51 509 patients with persistent critical illness died and only 23 968 (46·5%) of 51 509 were discharged home. INTERPRETATION: Onset of persistent critical illness can be empirically measured at a population level. Patients with this condition consume vast resources, have high mortality, have much less chance of returning home than do typical ICU patients, and require dedicated future research. ICU clinicians should be aware that the risk of in-hospital mortality can change quickly over the first 2 weeks of an ICU course and be sure to incorporate such changes in their decision making and prognostication. FUNDING: None.

Prevalence of thrombocytosis in critically ill patients and its association with symptomatic acute pulmonary embolism. A multicentre registry study.

It is uncertain whether thrombocytosis without underlying myeloproliferative diseases is associated with an increased risk of acute pulmonary embolism (PE). We investigated the relationship between thrombocytosis and risk of symptomatic acute PE, and whether Pulmonary Embolism Severity Index (PESI) was reliable in predicting mortality of acute PE. This multicentre registry study involved a total of 609,367 critically ill patients admitted to 160 intensive care units (ICUs) in Australia or New Zealand between 2006 and 2011. Forward stepwise logistic regression was used to assess the relationship between risk of acute PE and platelet counts on intensive care unit (ICU) admission. Acute PE (n=3387) accounted for 0.9% of all emergency ICU admissions. Over 20% of all PE required mechanical ventilation, 4.2% had cardiac arrest, and the mortality was high (14.8%). Thrombocytosis, defined by a platelet count >500x109 per litre, occurred in 2.1% of the patients and was more common in patients with acute PE than other diagnoses (3.4 vs. 2.0%). The platelet counts explained about 4.5% of the variability and had a linear relationship with the risk of acute PE (odds ratio 1.19 per 100x109 per litre increment in platelet count, 95% confidence interval 1.06-1.34), after adjusting for other covariates. The PESI had a reasonable discriminative ability (area under receiver-operating-characteristic curve = 0.78) and calibration to predict mortality across a wide range of severity of acute PE. In summary, thrombocytosis was associated with an increased risk of symptomatic acute PE. PESI was useful in predicting mortality across a wide range of severity of acute PE.

Omission of early thromboprophylaxis and mortality in critically ill patients: a multicenter registry study.

BACKGROUND: VTE is a preventable cause of death within hospitals. This study aimed to assess the association between omission of early thromboprophylaxis for > 24 h after ICU admission and mortality in critically ill patients. METHODS: This study involved 175,665 critically ill adult patients admitted to 134 ICUs in Australia and New Zealand between 2006 and 2010. RESULTS: The crude ICU and hospital mortality in patients who did not receive thromboprophylaxis within 24 h of ICU admission was higher than those who were treated with early thromboprophylaxis (7.6% vs 6.3%, P = .001; 11.2% vs 10.6%, P = .003, respectively), despite the former patients being associated with a slightly lower acuity of illness (mean APACHE [Acute Physiology and Chronic Health Evaluation] III model predicted mortality, 13% vs 14%; P = .001). The association between omission of early thromboprophylaxis and hospital mortality remained significant after adjusting for other covariates (OR, 1.22; 95% CI, 1.15-1.30; P = .001), particularly for patients with multiple trauma, sepsis, cardiac arrest, and preexisting metastatic cancer. The estimated attributable mortality effect of omitting early thromboprophylaxis for patients with multiple trauma, sepsis, cardiac arrest, and preexisting metastatic cancer was 3.9% (95% CI, 2.2-5.6), 8.0% (95% CI, 5.6-10.4), 15.4% (95% CI, 11.1-19.8), and 9.4% (95% CI, 6.4-12.4), respectively. CONCLUSIONS: Omission of thromboprophylaxis within the first 24 h of ICU admission without obvious reasons was associated with an increased risk of mortality in critically ill adult patients.

Dose-related effect of smoking on mortality in critically ill patients: a multicentre cohort study.

PURPOSE: It is uncertain whether smoking has an independent dose-related adverse effect on mortality in critically ill patients. This study assessed whether the intensity of smoking history, measured in pack-years, has a dose-related effect on mortality in critically ill patients. METHODS: In this multicentre cohort study data were collected from six tertiary intensive care units (ICU) in Australia and New Zealand. RESULTS: Of the 8,962 patients considered in the study, data on patients' smoking status and smoking history were available from 5,063 and 2,865 patients, respectively. Male gender, and chronic respiratory, liver and cardiovascular diseases were over-represented among smokers compared to non-smokers. Smokers had a higher risk of requiring mechanical ventilation and dying in hospital than non-smokers (10.7% vs. 6.7%, p=0.001), particularly after emergency admission. Smokers also had a longer ICU stay than non-smokers (mean 3.2 days, interquartile range 0.8-3.2 vs. 2.8 days, interquartile range 0.8-2.9; p=0.024). After adjusting for age, gender, elective surgical admission, severity of acute illness, and severe chronic illnesses, the intensity of smoking history remained significantly associated with the risk of dying in hospital. This was in a relatively linear fashion (odds ratio 1.08 per 10 pack-years increment, 95% confidence interval 1.02-1.15; p=0.02). Further grouping of smokers into active smokers and ex-smokers, or including patients with unknown smoking status in the sensitivity analyses did not change the association between the intensity of smoking history and mortality. CONCLUSIONS: Smoking has a dose-related adverse effect on mortality of critically ill patients after adjusting for other confounders.

Designing and implementing an Australian and New Zealand intensive care data audit study.

PURPOSE: The aim of this pilot audit study is to develop and test a model to examine existing adult patient database (APD) data quality. DESIGN/METHODOLOGY/APPROACH: A database was created to audit 50 records per site to determine accuracy. The audited records were randomly selected from the calendar year 2004 and four sites participated in the pilot audit study. A total of 41 data elements were assessed for data quality--those elements required for APACHE II scoring system. FINDINGS: Results showed that the audit was feasible; missing audit data were an unplanned problem; analysis was complicated owing to the way the APACHE calculations are performed and 50 records per site was too time-consuming. ORIGINALITY/VALUE: This is the first audit study of intensive care data within the ANZICS APD and demonstrates how to determine data quality in a large database containing individual patient records.