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Lung transplantation is limited by a lack of suitable lung donors. In Australia, the national donation organisation (DonateLife) has taken a major role in optimising organ donor identification. However, the potential outside the DonateLife network hospitals remains uncertain. We aimed to create a prediction model for lung donation within the DonateLife network and estimate the untapped lung donors outside of the DonateLife network. We reviewed all deaths in the state of Victoria's intensive care units using a prospectively collected population-based intensive care unit database linked to organ donation records. A logistic regression model derived using patient-level data was developed to characterise the lung donors within DonateLife network hospitals. Consequently, we estimated the expected number of lung donors in Victorian hospitals outside the DonateLife network and compared the actual number. Between 2014 and 2018, 291 lung donations occurred from 8043 intensive care unit deaths in DonateLife hospitals, while only three lung donations occurred from 1373 ICU deaths in non-DonateLife hospitals. Age, sex, postoperative admission, sepsis, neurological disease, trauma, chronic respiratory disease, lung oxygenation and serum creatinine were factors independently associated with lung donation. A highly discriminatory prediction model with area under the receiver operator characteristic curve of 0.91 was developed and accurately estimated the number of lung donors. Applying the model to non-DonateLife hospital data predicted only an additional five lung donors. This prediction model revealed few additional lung donor opportunities outside the DonateLife network, and the necessity of alternative and novel strategies for lung donation. A donor prediction model could provide a useful benchmarking tool to explore organ donation potential across different jurisdictions, hospitals and transplanting centres.

Background: Laboratory and clinical studies have suggested that hyperoxia early after resuscitation from cardiac arrest may increase neurological injury and worsen outcome. Previous clinical studies have been small or have not included relevant prehospital data. We aimed to determine in a larger cohort of patients whether hyperoxia in the intensive care unit in patients admitted after out-of-hospital cardiac arrest (OHCA) was associated with increased mortality rate after correction for prehospital variables., Methods: Data from the Victorian Ambulance Cardiac Arrest Registry (VACAR) of patients transported to hospital after resuscitation from OHCA and an initial cardiac rhythm of ventricular fibrillation between January 2007 and December 2011 were linked to the Australian and New Zealand Intensive Care Society Adult Patient Database (ANZICS-APD). Patients were allocated into three groups (hypoxia [PaO2<60mmHg], normoxia [PaO2,60-299mmHg] or hyperoxia [PaO2≥300mmHg]) according to their most abnormal PaO2 level in the first 24 hours of ICU stay. The relationship between PaO2 and hospital mortality was investigated using multivariate logistic regression analysis to adjust for confounding prehospital and ICU factors., Results: There were 957 patients identified on the VACAR database who met inclusion criteria. Of these, 584 (61%) were matched to the ANZICS-APD and had hospital mortality and oxygen data available. The unadjusted hospital mortality was 51% in the hypoxia patients, 41% in the normoxia patients and 47% in the hyperoxia patients (P=0.28). After adjustment for cardiopulmonary resuscitation by a bystander, patient age and cardiac arrest duration, hyperoxia in the ICU was not associated with increased hospital mortality (OR, 1.2; 95% CI, 0.51-2.82; P=0.83)., Conclusions: Hyperoxia within the first 24 hours was not associated with increased hospital mortality in patients admitted to ICU following out-of-hospital ventricular fibrillation cardiac arrest.

Objectives: To describe the design, development and implementation of an organ and tissue donation after cardiac death (DCD) program, evaluate its success and assess its impact on tissue and organ availability and the number of donors after brain death., Design, Participants and Setting: Prospective collection of patient characteristics and outcomes for actual and potential donors from 2000 to 2010, thus including the 5 years after the implementation of a DCD program at a major Australian tertiary hospital in 2006., Main Outcome Measures: The number and type of donors before and after implementation of the DCD program, and subsequent numbers of solid organ and tissue donations., Results: The DCD program was associated with an increase in overall donor numbers. There were 80 donors (20 DCD and 60 donation after brain death [DBrD]) after 2006, compared with 51 DBrD donors in the previous 5 years. Four of the DBrD donors were patients who were initially considered for DCD. DCD accounted for eight of the total 19 donors in 2009 and seven of the total 23 donors in 2010. There were 62 solid organ and 35 tissue and cornea transplants as a result of the DCD program., Conclusions: Successful implementation of a DCD program is possible and has led to an increase in overall donor numbers and organs transplanted without any reduction in DBrD donors. The widespread implementation of DCD across Australia may help reduce the shortfall of organs for transplantation.

Unlabelled: There is interest in evaluating the quality of critical care by auditing patient outcomes after hospital discharge. Risk adjustment using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation (APACHE III) scores, derived from clinical databases is commonly performed for in-hospital mortality outcome measures. However, these clinical databases do not routinely track patient outcomes after hospital discharge. Linkage of clinical databases to administrative data sets that maintain records on patient survival after discharge can allow for the measurement of survival outcomes of critical care patients after hospital discharge while using validated risk adjustment methods., Objective: The aim of this study was to compare the ability of 4 methods of risk adjustment to predict survival of critically ill patients at 180 days after hospital discharge: one using only variables from an administrative data set, one using only variables from a clinical database, a model using a full range of administrative and clinical variables, and a model using administrative variables plus APACHE III scores., Design: This was a population-based cohort study., Patients: The study sample consisted of adult (>15 years of age) residents of Victoria, Australia, admitted to a public hospital intensive care unit between 1 January 2001 and 31 December 2006 (n = 47,312 linked cases). Logistic regression analyses were used to develop the models., Results: The administrative-only model was the poorest predictor of mortality at 180 days after hospital discharge (C = 0.73). The clinical model had substantially better predictive capabilities (C = 0.82), whereas the full-linked model achieved similar performance (C = 0.83). Adding APACHE III scores to the administrative model also had reasonable predictive capabilities (C = 0.83)., Conclusions: The addition of APACHE III scores to administrative data substantially improved model performance to the level of the clinical model. Although linking data systems requires some investment, having the ability to evaluate case ascertainment and accurately risk adjust outcomes of intensive care patients after discharge will add valuable insights into clinical audit and decision-making processes., (Copyright © 2012 Elsevier Inc. All rights reserved.)

There has been considerable evolution in the pre-, peri- and postoperative management of patients with severe lung disease undergoing LTx. Compared with where we started at the Alfred Hospital in 1990, in 2008 we now recognize that the majority of donor lungs that are offered for LTx (including DCD lungs) are useable, patients with a wide range of ages and disease processes are suitable to be considered for LTx and modern surgical, anesthetic and ICU management should result in a 90% one-year survival rate. It is likely that the procedural mix in LTX servicing will remain little changed in the years to come, with BLTx being the pre-eminent service modality for the majority of end-stage lung disease patients. SLTx will remain a viable procedure almost exclusively for the IPF recipient, with HLTx a necessity for the congenital heart disease patient, for whom all other medical and surgical options have been exhausted. Notwithstanding theseconsiderable achievements, including the factthat one-third of patients now survive more than10 years, it is also apparent that BOS and recurrent infections remain a problem limiting the overall success of LTx. Understanding more about the interactions between the immunosuppressive regimen, infective agents (particularly viruses) and the recipients responses to all of the abovehold the keys to improving these late outcomes.