Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: A fracture neck of femur is the leading cause of injury-related mortality in the elderly population. The 30-day mortality figure is a well utilised marker of clinical outcome following a fracture neck of femur. Current studies fail to analyse all patient demographic, biochemical and comorbid parameters associated with increased 30-day mortality. We aimed to assess medical risk factors for mortality, which are easily identifiable on admission for patients presenting with a fractured neck of femur.
Methods: A retrospective review of a prospectively populated database was undertaken to identify all consecutive patients with a fracture neck of femur between October 2008 and March 2011. All factors related to the patient, injury and surgery were identified. The primary outcome of interest was 30-day mortality. Univariate and subsequent multivariate analyses using a backward stepwise likelihood ratio Cox regression model were performed in order to establish all parameters that significantly increased the risk of death.
Results: A total of 1,356 patients were included in the study. The 30-day mortality was 8.7%. The most common causes of death included pneumonia, sepsis and acute myocardial infarction. Multiple regression analysis revealed male gender, increasing age, admission source other than the patient's own home, admission haemoglobin of less than 10 g/dL, a history of myocardial infarction, concomitant chest infection during admission, increasing Charlson comorbidity score and liver disease to be significant predictors of mortality.
Conclusions: This study has elucidated risk factors for mortality using clinical and biochemical information which are easily gathered at the point of hospitalization. These results allow for identification of vulnerable patients who may benefit from a prioritisation of resources.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5334018 | PMC |
http://dx.doi.org/10.4055/cios.2017.9.1.10 | DOI Listing |
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