A double-blind randomised feasibility trial of angiotensin-2 in cardiac surgery.

Acute kidney injury is common after cardiac surgery. Vasoplegic hypotension may contribute to kidney injury, and different vasopressors may have variable effects on kidney function. We conducted a double-blind, randomised feasibility trial comparing peri-operative angiotensin-2 with noradrenaline. We randomly allocated 60 patients at two centres to a blinded equipotent angiotensin-2 or noradrenaline infusion intra-operatively and for up to 48 h postoperatively, titrated to mean arterial pressure of 70-80 mmHg. Primary feasibility outcomes included consent rate, protocol adherence, infusion duration, mean arterial pressure maintenance in the target range and major adverse outcomes. Secondary outcomes included kidney injury rate. The consent rate was 47%. Protocol adherence was 100% in the angiotensin-2 group and 94% in the noradrenaline group. Study drug duration was median (IQR [range]) 217 (160-270 [30-315]) vs. 185 (135-301 [0-480]) min (p = 0.78) min intra-operatively, and 5 (0-16 [0-48]) vs. 14.5 (4.8-29 [0-48]) hours (p = 0.075) postoperatively for angiotensin-2 and noradrenaline, respectively. The mean arterial pressure target was achieved postoperatively in 25 of 28 (89%) of the angiotensin-2 group and 27 of 32 (84%) of the noradrenaline group. One participant had a stroke, one required extracorporeal support and three required renal replacement therapy, all in the noradrenaline group (p = 0.99, p = 0.99 and p = 0.1). Acute kidney injury occurred in 7 of 28 in the angiotensin-2 group vs. 12 of 32 patients in the noradrenaline group (p = 0.31). This pilot study suggests that a trial comparing angiotensin-2 with noradrenaline is feasible. Its findings justify further investigations of angiotensin-2 in cardiac surgery.

Functional Outcomes in Patients Admitted to the Intensive Care Unit with Traumatic Brain Injury and Exposed to Hyperoxia: A Retrospective Multicentre Cohort Study.

BACKGROUND: Supplemental oxygen administration to critically ill patients is ubiquitous in the intensive care unit (ICU). Uncertainty persists as to whether hyperoxia is benign in patients with traumatic brain injury (TBI), particularly in regard to their long-term functional neurological outcomes. METHODS: We conducted a retrospective multicenter cohort study of invasively ventilated patients with TBI admitted to the ICU. A database linkage between the Australian and New Zealand Intensive Care Society Adult Patient Database (ANZICS-APD) and the Victorian State Trauma Registry (VSTR) was utilized. The primary exposure variable was minimum acute physiology and chronic health evaluation (APACHE) III PaO2 in the first 24 h of ICU. We defined hypoxia as PaO2 < 60 mmHg, normoxia as 60-299 mmHg, and hyperoxia as ≥ 300 mmHg. The primary outcome was a Glasgow Outcome Scale-Extended (GOSE) < 5 at 6 months while secondary outcomes included 12 and 24 months GOSE and mortality at each of these timepoints. Additional sensitivity analyses were undertaken in the following subgroups: isolated head injury, patients with operative intervention, head injury severity, and PaO2 either subcategorized by increments of 60 mmHg or treated as a continuous variable. RESULTS: A total of 3699 patients met the inclusion criteria. The mean age was 42.8 years, 77.7% were male and the mean acute physiology and chronic health evaluation (APACHE) III score was 60.1 (26.3). 2842 patients experienced normoxia, and 783 hyperoxia. The primary outcome occurred in 1470 (47.1%) of patients overall with 1123 (47.1%) from the normoxia group and 312 (45.9%) from the hyperoxia group-odds ratio 0.99 (0.78-1.25). No significant differences in outcomes between groups at 6, 12, and 24 months were observed. Sensitivity analyses did not identify subgroups that were adversely affected by exposure to hyperoxia. CONCLUSIONS: No associations were observed between hyperoxia in ICU during the first 24 h and adverse neurological outcome at 6 months in ventilated TBI patients.

Obstetric admissions to intensive care units in Australia and New Zealand: a registry-based cohort study.

OBJECTIVE: Describe the epidemiology of obstetric patients admitted to an Intensive Care Unit (ICU). DESIGN: Registry-based cohort study. SETTING: One hundred and eighty-three ICUs in Australia and New Zealand. POPULATION: Women aged 15-49 years, admitted to ICU between 2008 and 2017, classified as pregnant, postpartum or with an obstetric-related diagnosis. METHODS: Data were extracted from the Australia and New Zealand Intensive Care Society (ANZICS) Adult Patient Database and national agencies. MAIN OUTCOME MEASURES: Incidence of ICU admission, cohort characteristics, maternal outcomes and changes over time. RESULTS: The cohort comprised 16 063 patients. The annual number of obstetric ICU admissions increased, whereas their proportion of total ICU admissions (1.3%) did not change (odds ratio 1.02, 95% CI 0.99-1.04, P = 0.14). There were 10 518 (65%) with an obstetric-related ICU diagnosis, and 5545 (35%) with a non-obstetric ICU diagnosis. Mean (SD) age was 31 (6.4) years, 1463 (9.1%) were Indigenous, 2305 (14%) were transferred from another hospital, and 3008 (19%) received mechanical ventilation. Median [IQR] length of stay in hospital was 5.2 [3.1-7.9] days, which included 1.1 [0.7-1.8] days in ICU. There were 108 (0.7%) maternal deaths, most (n = 97, 90%) having a non-obstetric diagnosis. There was no change in risk-adjusted length of stay or mortality over time. CONCLUSIONS: Obstetric patients account for a stable proportion of ICU admissions in Australia and New Zealand. These patients typically have a short length of ICU stay and low hospital mortality. TWEETABLE ABSTRACT: Obstetric patients in Australia/New Zealand ICUs have a short length of ICU stay and low mortality.

Central line-associated bloodstream infections in Australian ICUs: evaluating modifiable and non-modifiable risks in Victorian healthcare facilities.

Central line-associated bloodstream infections (CLABSIs) in intensive care units (ICUs) result in poor clinical outcomes and increased costs. Although frequently regarded as preventable, infection risk may be influenced by non-modifiable factors. The objectives of this study were to evaluate organisational factors associated with CLABSI in Victorian ICUs to determine the nature and relative contribution of modifiable and non-modifiable risk factors. Data captured by the Australian and New Zealand Intensive Care Society regarding ICU-admitted patients and resources were linked to CLABSI surveillance data collated by the Victorian Healthcare Associated Infection Surveillance System between 1 January 2010 and 31 December 2013. Accepted CLABSI surveillance methods were applied and hospital/patient characteristics were classified as 'modifiable' and 'non-modifiable', enabling longitudinal Poisson regression modelling of CLABSI risk. In total, 26 ICUs were studied. Annual CLABSI rates were 1·72, 1·37, 1·00 and 0·93/1000 CVC days for 2010-2013. Of non-modifiable factors, the number of non-invasively ventilated patients standardised to total ICU bed days was found to be independently associated with infection (RR 1·07; 95% CI 1·01-1·13; P = 0·030). Modelling of modifiable risk factors demonstrated the existence of a policy for mandatory ultrasound guidance for central venous catheter (CVC) localisation (RR 0·51; 95% CI 0·37-0·70; P < 0·001) and increased number of sessional specialist full-time equivalents (RR 0·52; 95% CI 0·29-0·93; P = 0·027) to be independently associated with protection against infection. Modifiable factors associated with reduced CLABSI risk include ultrasound guidance for CVC localisation and increased availability of sessional medical specialists.

Age and other perioperative risk factors for postoperative systemic inflammatory response syndrome after cardiac surgery.

BACKGROUND: The inflammatory response to surgery varies considerably between individual patients. Age might be a substantial factor in this variability. Our objective was to examine the association of patient age and other potential risk factors with the occurrence of a postoperative systemic inflammatory response syndrome, during the first 24 h after cardiac surgery. METHODS: This was a retrospective cohort study, using linked data from the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) Database and the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database. Data from patients who underwent coronary artery bypass grafting and/or valve surgery were used. The association between age and postoperative SIRS was analysed using Poisson regression, and corrected for other risk factors. Restricted cubic splines were used to determine relevant age categories. Results are expressed as risk ratios (RR) with 95% confidence intervals (CI). RESULTS: Data from 28 513 patients were used. In both univariable and multivariable models, increased patient age was strongly associated with reduced postoperative SIRS prevalence. Using 73-83 yr as the reference category, the RRs (95% CI) for the age categories were 1.38 (1.28-1.49) for ≤43 yr, 1.15 (1.09-1.20) for 44-63 yr, 1.05 (1.00-1.09) for 64-72 yr, and 1.03 (0.94-1.12) for >83 yr, respectively. The predictive value for postoperative SIRS of the final model, however, was moderate (c-statistic: 0.61). CONCLUSIONS: We have demonstrated that advanced patient age is associated with a decreased risk of postoperative SIRS among cardiac surgery patients, where patients aged over 72 yr had the lowest risk.

The association between peri-operative acute risk change (ARC) and long-term survival after cardiac surgery.

Acute risk change has been described as the difference in calculated mortality risk between the pre-operative and postoperative periods of cardiac surgery. We aimed to assess whether this was associated with long-term survival after cardiac surgery. We retrospectively analysed 22,570 cardiac surgical patients, with minimum and maximum follow-up of 1.0 and 6.7 years. Acute risk change was calculated as the arithmetic difference between pre- and postoperative mortality risk. 'Rising risk' represented an increase in risk from pre- to postoperative phase. The primary outcome was one-year mortality. Secondary outcomes included mortality at 3 and 5 years and time to death. Univariable and multivariable analyses were undertaken to examine the relationship between acute risk change and outcomes. Rising risk was associated with higher mortality (5.6% vs. 3.5%, p < 0.001). After adjusting for baseline risk, rising risk was independently associated with increased 1-year mortality (OR 2.6, 95%CI 2.2-3.0, p < 0.001). The association of rising risk with long-term survival was greatest in patients with highest baseline risk. Cox regression confirmed rising risk was associated with shorter time to death (HR 1.86, 1.68-2.05, p < 0.001). Acute risk change may represent peri-operative clinical events in combination with unmeasured patient risk and noise. Measuring risk change could potentially identify patterns of events that may be amenable to investigation and intervention. Further work with case review, and risk scoring with shared variables, may identify mechanisms, including the interaction between miscalibration of risk and true differences in peri-operative care.

Associations of hospital characteristics with nosocomial pneumonia after cardiac surgery can impact on standardized infection rates.

To identify hospital-level factors associated with post-cardiac surgical pneumonia for assessing their impact on standardized infection rates (SIRs), we studied 43 691 patients in a cardiac surgery registry (2001-2011) in 16 hospitals. In a logistic regression model for pneumonia following cardiac surgery, associations with hospital characteristics were quantified with adjustment for patient characteristics while allowing for clustering of patients by hospital. Pneumonia rates varied from 0·7% to 12·4% across hospitals. Seventy percent of variability in the pneumonia rate was attributable to differences in hospitals in their long-term rates with the remainder attributable to within-hospital differences in rates over time. After adjusting for patient characteristics, the pneumonia rate was found to be higher in hospitals with more registered nurses (RNs)/100 intensive-care unit (ICU) admissions [adjusted odds ratio (aOR) 1·2, P = 0·006] and more RNs/available ICU beds (aOR 1·4, P < 0·001). Other hospital characteristics had no significant association with pneumonia. SIRs calculated on the basis of patient characteristics alone differed substantially from the same rates calculated on the basis of patient characteristics and the hospital characteristic of RNs/100 ICU admissions. Since SIRs using patient case-mix information are important for comparing rates between hospitals, the additional allowance for hospital characteristics can impact significantly on how hospitals compare.

Acute risk change (ARC) identifies outlier institutions in perioperative cardiac surgical care when the standardized mortality ratio cannot.

BACKGROUND: With improvements in short-term mortality after cardiac surgery, the sensitivity of the standardized mortality ratio (SMR) as a performance-monitoring tool has declined. We assessed acute risk change (ARC) as a new and potentially more sensitive metric to differentiate overall cardiac surgical unit performance. METHODS: Retrospective analysis of the Australian and New Zealand Society of Cardiac and Thoracic Surgeons database and Australian and New Zealand Intensive Care Society Adult Patient Database was performed. The 16 656 patients who underwent coronary artery bypass grafting or cardiac valve procedures during a 4 yr period were included. The ARC was generated using the change between preoperative and postoperative probability of death. Outlier institutions were those with higher (outside 99.8% confidence intervals) ARC or SMR on annual and 4 yr funnel plots. Outliers were grouped and compared with non-outliers for baseline characteristics, intraoperative events, and postoperative morbidity. RESULTS: No outliers were identified using SMR. Two outliers were identified using ARC. Outliers had higher rates of new renal failure (5.7 vs 4.5%, P=0.017), stroke (1.6 vs 0.9%, P=0.001), reoperation (9 vs 6.0%, P<0.001), and prolonged ventilation (15.3 vs 9.5%, P<0.001). Outliers transfused more blood products (P<0.001) and had longer cardiopulmonary bypass times (P<0.001) and less senior surgeons operating (P<0.001). CONCLUSIONS: Acute risk change was able to discriminate between units where SMR could not. Outliers had more adverse events. Acute risk change can be calculated before mortality outcome and identifies outliers with lower patient numbers. This may allow early recognition and investigation of outlier units.

Deteriorating patients managed with end-of-life care following Medical Emergency Team calls.

AIM: To describe the characteristics of patients whose end-of-life care was initiated in response to a Medical Emergency Team (MET) call and to develop a predictive score to aid prospective identification of these patients. METHODS: Retrospective cohort study of all MET calls in a tertiary teaching hospital between April 2010 and March 2011. All inpatients attended by the hospital MET. The main outcome measures were patient demographics, admission features and comorbidities in active and palliative patients, timing, frequency, physiology, and interventions in active and palliative MET calls. RESULTS: One thousand, five hundred and sixty-seven MET calls were called for 1073 patients. Sixty (5.6%) patients had at least one MET call resulting in initiation of end-of-life care. Palliative MET call patients compared with active patients were older (76.4 vs 65.9 years; P < 0.0001), had a shorter hospital stay (7.5 vs 12 days; P = 0.0002), had increased in-hospital mortality (73.3% vs 13.5%; P < 0.001), had higher Charlson comorbidity scores (3.1 vs 2.1; P = 0.0002) and were more likely to receive multiple MET calls (1.95/patient vs 1.43/patient; P = 0.011). Patient physiological parameters were worse at palliative MET calls. Prior history of malignancy, hemiplegia and peripheral vascular disease, and increasing age were independently associated with initiation of end-of-life care and were used to derive a 13-point predictive score. Patients with a score of 7 or more had a 20% chance of having a palliative MET call. CONCLUSION: Prospective identification of patients requiring palliative care may be possible prior to MET involvement. This may allow more timely and appropriate end-of-life discussions.

Postoperative hypothermia and patient outcomes after major elective non-cardiac surgery.

Using a multicentre adult patient database from Australia and New Zealand, we obtained the lowest and highest temperature in the first 24 h after admission to the intensive care unit after elective non-cardiac surgery. Hypothermia was defined as core temperature < 36 °C; transient hypothermia as a temperature < 36 °C that was corrected within 24 h, and persistent hypothermia as hypothermia not corrected within 24 h. We studied 50,689 patients. Hypothermia occurred in 23,165 (46%) patients, was transient in 22,810 (45%), and was persistent in 608 (1.2%) patients. On multivariate analysis, neither transient (OR = 1.07, 95% CI 0.96-1.20) nor persistent (OR = 1.50. 95% CI 0.96-2.33) hypothermia was independently associated with increased hospital mortality.

Postoperative hypothermia and patient outcomes after elective cardiac surgery.

Hypothermia after elective cardiac surgery is an important physiological abnormality and is associated with increased morbidity and mortality. The Australian and New Zealand intensive care adult patient database was studied to obtain the lowest and highest temperature in the first 24 h after surgery. Hypothermia was defined as core temperature < 36 °C; transient hypothermia as temperature < 36 °C that was corrected within 24 h; and persistent hypothermia as hypothermia that was not corrected within 24 h. Hypothermia occurred in 28,587 out of a total of 43,158 consecutive patients (66%) and was persistent in 111 (0.3%). Transient hypothermia was not independently associated with increased hospital mortality (OR = 0.9, 95% CI 0.8-1.1), whereas persistent hypothermia was associated with markedly increased risk of death (OR = 6.3, 95% CI = 3.3-12.0). Hypothermia is common in postoperative cardiac surgery patients during the first 24 h after ICU admission but, if transient, is not independently associated with an increased risk of death.

Outcomes for head and neck cancer patients admitted to intensive care in Australia and New Zealand between 2000 and 2016.

OBJECTIVE: To report intensive care unit admission outcomes for head and neck cancer patients. METHODS: A retrospective, observational cohort analysis of all Australian and New Zealander head and neck cancer patient intensive care unit admissions from January 2000 to June 2016, including data from 192 intensive care units. RESULTS: There were 10 721 head and neck cancer patients, with a median age of 64 years (71.6 per cent male). Of admissions, 76.4 per cent were in public hospitals, 96.9 per cent were post-operative and 43.6 per cent required mechanical ventilation. Annual head and neck cancer admissions increased from 2000 to 2015 (from 348 to 1132 patients), but the overall proportion of intensive care unit admissions remained constant. In-hospital mortality was 2.7 per cent, and intensive care unit mortality was 0.7 per cent. The in-hospital mortality risk decreased three-fold (p < 0.001). CONCLUSION: Head and neck cancer patients had low mortality in the intensive care unit and in hospital. Risk of dying decreased despite more intensive care unit admissions. This is the first large-scale cohort study quantifying intensive care unit utilisation by head and neck cancer patients. It informs future work investigating alternatives to the intensive care unit for these patients.

Comparison of Intensive Care and Trauma-specific Scoring Systems in Critically Ill Patients.

INTRODUCTION: Amongst critically ill trauma patients admitted to ICU and still alive and in ICU after 24 hours, it is unclear which trauma scoring system offers the best performance in predicting in-hospital mortality. METHODS: The Australia and New Zealand Intensive Care Society Adult Patient Database and Victorian State Trauma Registry were linked using a unique patient identification number. Six scoring systems were evaluated: the Australian and New Zealand Risk of Death (ANZROD), Acute Physiology and Chronic Health Evaluation III (APACHE III) score and associated APACHE III Risk of Death (ROD), Trauma and Injury Severity Score (TRISS), Injury Severity Score (ISS), New Injury Severity Score (NISS) and the Revised Trauma Score (RTS). Patients who were admitted to ICU for longer than 24 hours were analysed. Performance of each scoring system was assessed primarily by examining the area under the receiver operating characteristic curve (AUROC) and in addition using standardised mortality ratios, Brier score and Hosmer-Lemeshow C statistics where appropriate. Subgroup assessments were made for patients aged 65 years and older, patients between 18 and 40 years of age, major trauma centre and head injury. RESULTS: Overall, 5,237 major trauma patients who were still alive and in ICU after 24 hours were studied from 25 ICUs in Victoria, Australia between July 2008 and January 2018. Hospital mortality was 10.7%. ANZROD (AUROC 0.91; 95% CI 0.90-0.92), APACHE III ROD (AUROC 0.88; 95% CI 0.87-0.90), and APACHE III (AUROC 0.88; 95% CI 0.87-0.89) were the best performing tools for predicting hospital mortality. TRISS had acceptable overall performance (AUROC 0.78; 95% CI 0.76-0.80) while ISS (AUROC 0.61; 95% CI 0.59-0.64), NISS (AUROC 0.68; 95% CI 0.65-0.70) and RTS (AUROC 0.69; 95% CI 0.67-0.72) performed poorly. The performance of each scoring system was highest in younger adults and poorest in older adults. CONCLUSION: In ICU patients admitted with a trauma diagnosis and still alive and in ICU after 24 hours, ANZROD and APACHE III had a superior performance when compared with traditional trauma-specific scoring systems in predicting hospital mortality. This was observed both overall and in each of the subgroup analyses. The anatomical scoring systems all performed poorly in the ICU population of Victoria, Australia.

What can paper-based clinical information systems tell us about the design of computerized clinical information systems (CIS) in the ICU?

BACKGROUND: Screen designs in computerized clinical information systems (CIS) have been modeled on their paper predecessors. However, limited understanding about how paper forms support clinical work means that we risk repeating old mistakes and creating new opportunities for error and inefficiency as illustrated by problems associated with computerized provider order entry systems. PURPOSE: This study was designed to elucidate principles underlying a successful ICU paper-based CIS. The research was guided by two exploratory hypotheses: (1) paper-based artefacts (charts, notes, equipment, order forms) are used differently by nurses, doctors and other healthcare professionals in different (formal and informal) conversation contexts and (2) different artefacts support different decision processes that are distributed across role-based conversations. METHOD: All conversations undertaken at the bedsides of five patients were recorded with any supporting artefacts for five days per patient. Data was coded according to conversational role-holders, clinical decision process, conversational context and artefacts. 2133 data points were analyzed using Poisson logistic regression analyses. RESULTS: Results show significant interactions between artefacts used during different professional conversations in different contexts (chi(2)((df=16))=55.8, p<0.0001). The interaction between artefacts used during different professional conversations for different clinical decision processes was not statistically significant although all two-way interactions were statistically significant. CONCLUSIONS: Paper-based CIS have evolved to support complex interdisciplinary decision processes. The translation of two design principles - support interdisciplinary perspectives and integrate decision processes - from paper to computerized CIS may minimize the risks associated with computerization.

Does anaesthetic management affect early outcomes after lung transplant? An exploratory analysis.

BACKGROUND: Primary graft dysfunction (PGD) is a predominant cause of early morbidity and mortality after lung transplantation. Although substantial work has been done to understand risk factors for PGD in terms of donor, recipient, and surgical factors, little is understood regarding the potential role of anaesthetic management variables in its development. METHODS: We conducted a retrospective exploratory analysis of 107 consecutive lung transplants to determine if anaesthesia factors were associated with early graft function quantified by Pa(O(2))/Fi(O(2)). Multivariate regression techniques were used to explore the association between anaesthetic management variables and Pa(O(2))/Fi(O(2)) ratio 12 h after operation. The relationship between these variables and both time to tracheal extubation and intensive care unit (ICU) length of stay was further examined using the Cox proportional hazards. RESULTS: On multivariate analysis, increasing volume of intraoperative colloid, comprising predominantly Gelofusine (succinylated gelatin), was independently associated with a lower Pa(O(2))/Fi(O(2)) 12 h post-transplantation [beta coefficient -42 mm Hg, 95% confidence interval (CI) -7 to -77 mm Hg, P=0.02] and reduced rate of extubation [hazard ratio (HR) 0.65, 95% CI 0.49-0.84, P=0.001]. There was a trend for intraoperative colloid to be associated with a reduced rate of ICU discharge (HR 0.79, 95% CI 0.31-1.02, P=0.07). CONCLUSIONS: We observed an inverse relationship between volume of intraoperative colloid and early lung allograft function. The association persists, despite detailed sensitivity analyses and adjustment for potential confounding variables. Further studies are required to confirm these findings and explore potential mechanisms through which these associations may act.

Early lung transplantation success utilizing controlled donation after cardiac death donors.

Donation-after cardiac death (DCD) donor organs have potential to significantly alleviate the shortage of transplantable lungs. However, only limited data so far describes DCD lung transplantation (LTx) techniques and results. This study aims to describe the Alfred Hospital's early and intermediate outcomes following DCD donor LTx. Following careful experimentation and consultation DCD guidelines were created to utilize Maastricht category III lung donors from either the ICU or operating room(OR), with a warm ischemic time(WIT) of <60 min. Between May 2006 and December 2007, 22 referred DCD donors led to 11 attempted retrievals after withdrawal, resulting in 8 actual bilateral LTx (2 donors did not arrest in prescribed period and 1 donor had nonacceptable lungs). ICU WIT = 38.4 min (range 20-54, OR WIT = 12.7 min (11-15), p < 0.05. Post-LTx, 1 pulmonary hypertensive patient required ECMO for PGD3. The mean group pO2/FiO2 ratio at 24 hours was 307.7 (240-507) with an ICU stay of 9.5 days (2-21) and ward stay of 21.5 days (11-76). All 8 survive at a mean of 311 days (10-573) with good performance status and lung function. In conclusion, the use of Maastricht category III lungs for human LTx is associated with acceptable early clinical outcomes.

The use of a simple three-level bronchoscopic assessment of inhalation injury to predict in-hospital mortality and duration of mechanical ventilation in patients with burns.

Major burn centres in Australia use bronchoscopy to assess severity of inhalation injuries despite limited evidence as to how best to classify severity of inhalational injury or its relationship to patient outcomes. All patients with burns who were admitted to the intensive care unit (ICU) at The Alfred Hospital between February 2010 and July 2014 and underwent bronchoscopy to assess inhalational injury, were reviewed. Age, total body surface area burnt, severity of illness indices and mechanisms of injury were extracted from medical histories and local ICU and burns registries. Inhalational injury was classified based on the Abbreviated Injury Score and then grouped into three categories (none/mild, moderate, or severe injury). Univariable and multivariable analyses were undertaken to examine the relationship between inhalational injury and outcomes (in-hospital mortality and duration of mechanical ventilation). One hundred and twenty-eight patients were classified as having none/mild inhalational injury, 81 moderate, and 13 severe inhalation injury. Mortality in each group was 2.3% (3/128), 7.4% (6/81) and 30.7% (4/13) respectively. Median (interquartile range) duration of mechanical ventilation in each group was 26 (11-82) hours, 84 (32-232) hours and 94 (21-146) hours respectively. After adjusting for age, total body surface area burnt and severity of illness, only the severe inhalation injury group was independently associated with increased mortality (odds ratio 20.4 [95% confidence intervals {CI} 1.74 to 239.4], P=0.016). Moderate inhalation injury was independently associated with increased duration of ventilation (odds ratio 2.25 [95% CI 1.53 to 3.31], P <0.001), but not increased mortality. This study suggests that stratification of bronchoscopically-assessed inhalational injury into three categories can provide useful prognostic information about duration of ventilation and mortality. Larger multicentre prospective studies are required to validate these findings.

Predicting medical emergency team calls, cardiac arrest calls and re-admission after intensive care discharge: creation of a tool to identify at-risk patients.

We aimed to develop a predictive model for intensive care unit (ICU)-discharged patients at risk of post-ICU deterioration. We performed a retrospective, single-centre cohort observational study by linking the hospital admission, patient pathology, ICU, and medical emergency team (MET) databases. All patients discharged from the Alfred Hospital ICU to wards between July 2012 and June 2014 were included. The primary outcome was a composite endpoint of any MET call, cardiac arrest call or ICU re-admission. Multivariable logistic regression analysis was used to identify predictors of outcome and develop a risk-stratification model. Four thousand, six hundred and thirty-two patients were included in the study. Of these, 878 (19%) patients had a MET call, 51 (1.1%) patients had cardiac arrest calls, 304 (6.5%) were re-admitted to ICU during the same hospital stay, and 964 (21%) had MET calls, cardiac arrest calls or ICU re-admission. A discriminatory predictive model was developed (area under the receiver operating characteristic curve 0.72 [95% confidence intervals {CI} 0.70 to 0.73]) which identified the following factors: increasing age (odds ratio [OR] 1.012 [95% CI 1.007 to 1.017] P <0.001), ICU admission with subarachnoid haemorrhage (OR 2.26 [95% CI 1.22 to 4.16] P=0.009), admission to ICU from a ward (OR 1.67 [95% CI 1.31 to 2.13] P <0.001), Acute Physiology and Chronic Health Evaluation (APACHE) III score without the age component (OR 1.005 [95% CI 1.001 to 1.010] P=0.025), tracheostomy on ICU discharge (OR 4.32 [95% CI 2.9 to 6.42] P <0.001) and discharge to cardiothoracic (OR 2.43 [95%CI 1.49 to 3.96] P <0.001) or oncology wards (OR 2.27 [95% CI 1.05 to 4.89] P=0.036). Over the two-year period, 361 patients were identified as having a greater than 50% chance of having post-ICU deterioration. Factors are identifiable to predict patients at risk of post-ICU deterioration. This knowledge could be used to guide patient follow-up after ICU discharge, optimise healthcare resources, and improve patient outcomes and service delivery.

Assessing contemporary intensive care unit outcome: development and validation of the Australian and New Zealand Risk of Death admission model.

The Australian and New Zealand Risk of Death (ANZROD) model currently used for benchmarking intensive care units (ICUs) in Australia and New Zealand utilises physiological data collected up to 24 hours after ICU admission to estimate the risk of hospital mortality. This study aimed to develop the Australian and New Zealand Risk of Death admission (ANZROD0) model to predict hospital mortality using data available at presentation to ICU and compare its performance with the ANZROD in Australian and New Zealand hospitals. Data pertaining to all ICU admissions between 1 January 2006 and 31 December 2015 were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Hospital mortality was modelled using logistic regression with development (two-thirds) and validation (one-third) datasets. All predictor variables available at ICU admission were considered for inclusion in the ANZROD0 model. Model performance was assessed using Brier score, standardised mortality ratio and area under the receiver operating characteristic curve. The relationship between ANZROD0 and ANZROD predicted risk of death was assessed using linear regression. After standard exclusions, 1,097,416 patients were available for model development and validation. Observed mortality was 9.5%. Model performance measures (Brier score, standardised mortality ratio and area under the receiver operating characteristic curve) for the ANZROD0 and ANZROD in the validation dataset were 0.069, 1.0 and 0.853; 0.057, 1.0 and 0.909, respectively. There was a strong positive correlation between the mortality predictions with an overall R2 of 0.73. We found that the ANZROD0 model had acceptable calibration and discrimination. Predictions from the models had high correlations in all major diagnostic groups, with the exception of cardiac surgery and possibly trauma and sepsis.