A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 197

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 317
Function: require_once

Predicting Expected Organ Donor Numbers in Australian Hospitals Outside of the Donate-Life Network Using the ANZICS Adult Patient Database. | LitMetric

Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background: The majority of organ donations in Australia occur in the DonateLife Network of hospitals, but limited monitoring at other sites may allow donation opportunities to be missed. Our aim was to estimate expected donor numbers using routinely collected data from the Australian and New Zealand Intensive Care Society Adult Patient Database and determine whether unrecognized potential donors might exist in non-DonateLife hospitals.

Methods: All deaths at 150 Australian intensive care units (ICUs) contributing to the Australian and New Zealand Intensive Care Society Adult Patient Database were analyzed between January 2010 and December 2015. Donor numbers were extracted from the Australian and New Zealand Organ Donor registry. A univariate linear regression model was developed to estimate expected donor numbers in DonateLife hospitals, then applied to non-DonateLife hospitals.

Results: Of 33 614 deaths at 71 DonateLife hospitals, 6835 (20%) met criteria as "ICU deaths potentially suitable to be donors," and 1992 (6%) were actual donors. There was a consistent relationship between these groups (R = 0.626, P < 0.001) allowing the development of a prediction model which adequately estimated expected donors. Of 8077 deaths in 79 non-DonateLife ICUs, 452 (6%) met criteria as potentially suitable donors. Applying the prediction model developed in DonateLife hospitals, the estimated expected donors in non-DonateLife hospitals was 130. However, there were only 75 actual donors.

Conclusions: It is possible to estimate the expected number of Australian organ donors using routinely collected registry data. These findings suggest that there may be a small but significant pool of underutilized potential donors in non-DonateLife hospitals. This may provide an opportunity to increase donation rates.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072376PMC
http://dx.doi.org/10.1097/TP.0000000000002111DOI Listing

Publication Analysis

Top Keywords

donor numbers
16
adult patient
12
patient database
12
estimate expected
12
australian zealand
12
intensive care
12
donatelife hospitals
12
organ donor
8
expected donor
8
routinely collected
8

Similar Publications