Evidence map›Paper›PMID 25919766›Full record

ArticleTransplantation2015

Estimating the Number of Organ Donors in Australian Hospitals--Implications for Monitoring Organ Donation Practices.

David Pilcher, Laura Gladkis, Byron Arcia, Michael Bailey, David Cook, Yael Cass, Helen Opdam

Abstract readMulticenter Study
In one paragraph

Article in Transplantation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

David Pilcher1 Department of Intensive Care, The Alfred Hospital, Commercial Road, Prahran, VIC, Australia. 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. 3 DonateLife, The Australian Organ and Tissue Authority, ACT, Australia. 4 Department of Intensive Care, Princess Alexandra Hospital, Brisbane, QLD, Australia. 5 School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia. 6 Department of Intensive Care Medicine, The Austin Hospital, Heidelberg, VIC, Australia.
Laura Gladkis
Byron Arcia
Michael Bailey
David Cook
Yael Cass
Helen Opdam

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsAll 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.

resultsBetween 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.

conclusionsIt 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.

Indexed as

AdolescentAdultAgedAged, 80 and overAlgorithmsAustraliaBenchmarkingCalibrationChildChild, PreschoolCritical CareHospitalsHumansInfantIntensive Care UnitsMiddle Aged

Identifiers

PMID25919766
PMCPMC4617283

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.