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.

Ambient intelligence-based monitoring of staff and patient activity in the intensive care unit.

BACKGROUND: Caregiver workload in the ICU setting is difficult to numerically quantify. Ambient Intelligence utilises computer vision-guided neural networks to continuously monitor multiple datapoints in video feeds, has become increasingly efficient at automatically tracking various aspects of human movement. OBJECTIVES: To assess the feasibility of using Ambient Intelligence to track and quantify allpatient and caregiver activity within a bedspace over the course of an ICU admission and also to establish patient specific factors, and environmental factors such as time ofday, that might contribute to an increased workload in ICU workers. METHODS: 5000 images were manually annotated and then used to train You Only LookOnce (YOLOv4), an open-source computer vision algorithm. Comparison of patientmotion and caregiver activity was then performed between these patients. RESULTS: The algorithm was deployed on 14 patients comprising 1762800 framesof new, untrained data. There was a strong correlation between the number ofcaregivers in the room and the standardized movement of the patient (p < 0.0001) withmore caregivers associated with more movement. There was a significant difference incaregiver activity throughout the day (p < 0.05), HDU vs. ICU status (p < 0.05), delirious vs. non delirious patients (p < 0.05), and intubated vs. not intubated patients(p < 0.05). Caregiver activity was lowest between 0400 and 0800 (average .71 ± .026caregivers per hour) with statistically significant differences in activity compared to 0800-2400 (p < 0.05). Caregiver activity was highest between 1200 and 1600 (1.02 ± .031 caregivers per hour) with a statistically significant difference in activity comparedto activity from 1600 to 0800 (p < 0.05). The three most dominant predictors of workeractivity were patient motion (Standardized Dominance 78.6%), Mechanical Ventilation(Standardized Dominance 7.9%) and Delirium (Standardized Dominance 6.2%). CONCLUSION: Ambient Intelligence could potentially be used to derive a single standardized metricthat could be applied to patients to illustrate their overall workload. This could be usedto predict workflow demands for better staff deployment, monitoring of caregiver workload, and potentially as a tool to predict burnout.

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.

Admissions of Children and Adolescents With Deliberate Self-harm to Intensive Care During the SARS-CoV-2 Outbreak in Australia.

Importance: Identification of potential indirect outcomes associated with the COVID-19 pandemic in the pediatric population may be essential for understanding the challenges of the current global public health crisis for children and adolescents. Objective: To investigate whether the SARS-CoV-2 outbreak and subsequent effective public health measures in Australia were associated with an increase in admissions to intensive care units (ICUs) of children and adolescents with deliberate self-harm (DSH). Design, Setting, and Participants: This national, multicenter cohort study was conducted using the Australian data subset of the binational Australian and New Zealand Paediatric Intensive Care registry, a collaborative containing more than 200 000 medical records with continuous contributions from all 8 Australian specialist, university-affiliated pediatric ICUs, along with 1 combined neonatal-pediatric ICU and 14 general (adult) ICUs in Australia. The study period encompassed 6.5 years from January 1, 2015, to June 30, 2021. Patients aged 12 to 17 years were included. Data were analyzed from December 2021 through February 2022. Exposures: Any of the following admission diagnoses: ingestion of a drug, ingestion of a nondrug, hanging or strangulation, or self-injury. Main Outcomes and Measures: The primary outcome measure was the temporal trend for national incidence of DSH ICU admissions per 1 million children and adolescents aged 12 to 17 years in Australia. Results: A total of 813 children and adolescents aged 12 to 17 years admitted to ICUs with DSH were identified among 64 145 patients aged 0 to 17 years in the Australian subset of the registry during the study period. Median (IQR) age was 15.1 (14.3-15.8) years; there were 550 (67.7%) female patients, 261 (32.2%) male patients, and 2 (0.2%) patients with indeterminate sex. At the onset of the pandemic, monthly incidence of DSH ICU admissions per million children and adolescents increased from 7.2 admissions in March 2020 to a peak of 11.4 admissions by August 2020, constituting a significant break in the temporal trend (odds ratio of DSH ICU admissions on or after vs before March 2020, 4.84; 95% CI, 1.09 to 21.53; P = .04). This occurred while the rate of all-cause admissions to pediatric ICUs of children and adolescents of all ages (ie, ages 0-17 years) per 1 million children and adolescents decreased from a long-term monthly median (IQR) of 150.9 (138.1-159.8) admissions to 91.7 admissions in April 2020. Conclusions and Relevance: This cohort study found that the coronavirus pandemic in Australia was associated with a significant increase in admissions of children and adolescents to intensive care with DSH.

Long-term outcomes of hospital survivors following an ICU stay: A multi-centre retrospective cohort study.

BACKGROUND: The focus of much Intensive Care research has been on short-term survival, which has demonstrated clear improvements over time. Less work has investigated long-term survival, and its correlates. This study describes long-term survival and identifies factors associated with time to death, in patients who initially survived an Intensive Care admission in Victoria, Australia. METHODS: We conducted a retrospective cohort study of adult patients discharged alive from hospital following admission to all Intensive Care Units (ICUs) in the state of Victoria, Australia between July 2007 and June 2018. Using the Victorian Death Registry, we determined survival of patients beyond hospital discharge. Comparisons between age matched cohorts of the general population were made. Cox regression was employed to investigate factors associated with long-term survival. RESULTS: A total of 130,775 patients from 23 ICUs were included (median follow-up 3.6 years post-discharge). At 1-year post-discharge, survival was 90% compared to the age-matched cohort of 98%. All sub-groups had worse long-term survival than their age-matched general population cohort, apart from elderly patients admitted following cardiac surgery who had better or equal survival. Multiple demographic, socio-economic, diagnostic, acute and chronic illness factors were associated with long-term survival. CONCLUSIONS: Australian patients admitted to ICU who survive to discharge have worse long-term survival than the general population, except for the elderly admitted to ICU following cardiac surgery. These findings may assist during goal-of-care discussions with patients during an ICU admission.

Micronutrient intake from enteral nutrition in critically ill adult patients: A retrospective observational study.

OBJECTIVE: The objective of this study was to determine the intake of micronutrients including vitamins B12, D, C, and A; folate; thiamine; iron; zinc; and selenium that are delivered from enteral nutrition (EN) during routine clinical practice in critically ill adults, expressed as a percentage of the Australia and New Zealand nutrient reference values. METHODS: This single-center retrospective observational study was conducted in Melbourne, Australia during the first 7 d of intensive care unit admission. Mechanically ventilated patients prescribed exclusive EN were considered for inclusion. The primary and secondary outcomes were micronutrient intake expressed as a percentage of the recommended dietary intake (daily intake intended to meet the needs of 97% to 98% of a healthy population) and the upper level of intake (highest daily intake unlikely to pose adverse health effects), respectively. Data are presented as mean (SD) or median [interquartile range]. RESULTS: In total, 57 patients were included (62 (16) y, 67% male). EN was delivered for 5 [4-6] d, with 47% (20) energy adequacy achieved. EN delivery met the recommended dietary intake for vitamin B12, vitamin C, thiamine, and iron and did not meet the recommended dietary intake for vitamin D, vitamin A, folate, zinc, and selenium. No micronutrients exceeded the upper level of intake. CONCLUSION: EN delivery met the recommended intake for four micronutrients, did not meet the recommended intake for five micronutrients, and did not exceed the upper level of intake for any micronutrient when approximately 50% energy adequacy was achieved types.

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.

Assessment of a novel marker of ICU strain, the ICU Activity Index, during the COVID-19 pandemic in Victoria, Australia.

Objectives: To validate a real-time Intensive Care Unit (ICU) Activity Index as a marker of ICU strain from daily data available from the Critical Health Resource Information System (CHRIS), and to investigate the association between this Index and the need to transfer critically ill patients during the coronavirus disease 2019 (COVID-19) pandemic in Victoria, Australia. Design: Retrospective observational cohort study. Setting: All 45 hospitals with an ICU in Victoria, Australia. Participants: Patients in all Victorian ICUs and all critically ill patients transferred between Victorian hospitals from 27 June to 6 September 2020. Main outcome measure: Acute interhospital transfer of one or more critically ill patients per day from one site to an ICU in another hospital. Results: 150 patients were transported over 61 days from 29 hospitals (64%). ICU Activity Index scores were higher on days when critical care transfers occurred (median, 1.0 [IQR, 0.4-1.7] v 0.6 [IQR, 0.3-1.2]; P < 0.001). Transfers were more common on days of higher ICU occupancy, higher numbers of ventilated or COVID-19 patients, and when more critical care staff were unavailable. The highest ICU Activity Index scores were observed at hospitals in north-western Melbourne, where the COVID-19 disease burden was greatest. After adjusting for confounding factors, including occupancy and lack of available ICU staff, a rising ICU Activity Index score was associated with an increased risk of a critical care transfer (odds ratio, 4.10; 95% CI, 2.34-7.18; P < 0.001). Conclusions: The ICU Activity Index appeared to be a valid marker of ICU strain during the COVID-19 pandemic. It may be useful as a real-time clinical indicator of ICU activity and predict the need for redistribution of critical ill patients.

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.

Reliability of comorbidity scores derived from administrative data in the tertiary hospital intensive care setting: a cross-sectional study.

BACKGROUND: Hospital reporting systems commonly use administrative data to calculate comorbidity scores in order to provide risk-adjustment to outcome indicators. OBJECTIVE: We aimed to elucidate the level of agreement between administrative coding data and medical chart review for extraction of comorbidities included in the Charlson Comorbidity Index (CCI) and Elixhauser Index (EI) for patients admitted to the intensive care unit of a university-affiliated hospital. METHOD: We conducted an examination of a random cross-section of 100 patient episodes over 12 months (July 2012 to June 2013) for the 19 CCI and 30 EI comorbidities reported in administrative data and the manual medical record system. CCI and EI comorbidities were collected in order to ascertain the difference in mean indices, detect any systematic bias, and ascertain inter-rater agreement. RESULTS: We found reasonable inter-rater agreement (kappa (κ) coefficient ≥0.4) for cardiorespiratory and oncological comorbidities, but little agreement (κ<0.4) for other comorbidities. Comorbidity indices derived from administrative data were significantly lower than from chart review: -0.81 (95% CI - 1.29 to - 0.33; p=0.001) for CCI, and -2.57 (95% CI -4.46 to -0.68; p=0.008) for EI. CONCLUSION: While cardiorespiratory and oncological comorbidities were reliably coded in administrative data, most other comorbidities were under-reported and an unreliable source for estimation of CCI or EI in intensive care patients. Further examination of a large multicentre population is required to confirm our findings.

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.