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|Title:||Estimating the Number of Organ Donors in Australian Hospitals-Implications for Monitoring Organ Donation Practices.|
|Authors:||Pilcher, David V;Gladkis, Laura;Arcia, Byron;Bailey, Michael J;Cook, David;Cass, Yael;Opdam, Helen Ingrid|
|Affiliation:||2 Department of Epidemiology and Preventive Medicine, Australian and New Zealand Intensive Care - Research Centre, School of Public Health and Preventive Medicine, Monash University, VIC, Australia|
6 Department of Intensive Care Medicine, The Austin Hospital, Heidelberg, VIC, Australia
3 DonateLife, The Australian Organ and Tissue Authority, ACT, Australia
1 Department of Intensive Care, The Alfred Hospital, Commercial Road, Prahran, VIC, Australia
5 School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
4 Department of Intensive Care, Princess Alexandra Hospital, Brisbane, QLD, Australia
|Citation:||Transplantation; 99(10): 2203-9|
|Abstract:||The Australian DonateLife Audit captures information on all deaths which occur in emergency departments, intensive care units and in those recently discharged from intensive care unit. This information provides the opportunity to estimate the number of donors expected, given present consent rates and contemporary donation practices. This may then allow benchmarking of performance between hospitals and jurisdictions. Our aim was to develop a method to estimate the number of donors using data from the DonateLife Audit on the basis of baseline patient characteristics alone.All intubated patient deaths at contributing hospitals were analyzed. Univariate comparisons of donors to nondonors were performed. A logistic regression model was developed to estimate expected donor numbers from data collected between July 2012 and December 2013. This was validated using data from January to April 2014.Between July 2012 and April 2014, 6861 intubated patient deaths at 68 hospitals were listed on the DonateLife Audit of whom 553 (8.1%) were organ donors. Factors independently associated with organ donation included age, brain death, neurological diagnoses, chest x-ray findings, PaO2/FiO2, creatinine, alanine transaminase, cancer, cardiac arrest, chronic heart disease, and peripheral vascular disease. A highly discriminatory (area under the receiver operatory characteristic, 0.940 [95% confidence interval, 0.924-0.957]) and well-calibrated prediction model was developed which accurately estimated donor numbers. Three hospitals appeared to have higher numbers of actual donors than expected.It is possible to estimate the expected number of organ donors. This may assist benchmarking of donation outcomes and interpretation of changes in donation rates over time.This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially.|
|Internal ID Number:||25919766|
|Appears in Collections:||Journal articles|
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