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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Objective: To examine the risk factors and direct medical costs associated with early (≤30 days) versus late (31-180 days) unplanned readmissions among patients with type 2 diabetes in Singapore.
Methods: Risk factors and associated costs among diabetes patients were investigated using electronic medical records from a local tertiary care hospital from 2010 to 2012. Multivariable logistic regression was used to identify risk factors associated with early and late unplanned readmissions while a generalized linear model was used to estimate the direct medical cost. Sensitivity analysis was also performed.
Results: A total of 1729 diabetes patients had unplanned readmissions within 180 days of an index discharge. Length of index stay (a marker of acute illness burden) was one of the risk factors associated with early unplanned readmission while patient behavior-related factors, like diabetes-related medication adherence, were associated with late unplanned readmission. Adjusted mean cost of index admission was higher among patients with unplanned readmission. Sensitivity analysis yielded similar results.
Conclusions: Existing routinely captured data can be used to develop prediction models that flag high risk patients during their index admission, potentially helping to support clinical decisions and prevent such readmissions.
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Source |
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http://dx.doi.org/10.1080/03007995.2018.1431617 | DOI Listing |
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