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/aims: Hemodialysis (HD) patients are hospitalized more frequently than patients with other chronic diseases, averaging 11.5 hospital days/patient/year. Hospital costs attributable to renal failure in the US exceed $2 billion per year. The present healthcare climate continues to force dialysis providers to focus on these issues in order to optimize patient care while limiting cost.
Methods: We used a novel method for analyzing hospitalization risk, a multiple-event Cox proportional hazards model, to identify factors that influenced hospitalization in a HD unit population over a two-year period. This model allows individual patients to contribute multiple failure events to the model while controlling for the serial dependency of events.
Results: 178 HD patients were retrospectively examined. There were 381 hospitalizations during the study period, averaging out to 1.9 hospitalizations and 10.5 hospital days/patient-year. Substance abuse and diabetes conveyed the largest risks for hospitalization (diabetes RR: 2.09; substance abuse RR: 2.24) in the study cohort, exposing the necessity for examining practice patterns and behavioral interventions as means for improving HD patient care.
Conclusion: Despite the small numbers of patients in this single-center HD population, the model achieved adequate statistical power. Therefore, it has the potential to serve as a continuous quality improvement (CQI) tool in particular HD patient sub-groups, or in individual HD units.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1159/000013521 | DOI Listing |
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