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dc.contributorOkahara, S.en_US
dc.contributorSnell, G.en_US
dc.contributorLevvey, B.en_US
dc.contributorMcDonald, M.en_US
dc.contributorD'Costa, R.en_US
dc.contributorOpdam, H.en_US
dc.contributorPilcher, D.en_US
dc.descriptionIncludes data from BHS, WHCGen_US
dc.description.abstractLung 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.en_US
dc.description.provenanceSubmitted by Gemma Siemensma ( on 2022-12-05T03:45:18Z No. of bitstreams: 0en
dc.description.provenanceApproved for entry into archive by Gemma Siemensma ( on 2022-12-16T02:07:32Z (GMT) No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2022-12-16T02:07:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2022en
dc.titleA prediction model to determine the untapped lung donor pool outside of the DonateLife network in Victoria.en_US
dc.typeJournal Articleen_US
dc.bibliographicCitation.titleAnaesthesia and Intensive Careen_US
dc.subject.healththesaurusORGAN DONATIONen_US
dc.subject.healththesaurusLUNG TRANSPLANTATIONen_US
dc.subject.healththesaurusTHORACIC ORGAN DONATIONen_US
Appears in Collections:Research Output

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