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: Video-EEG monitoring in the epilepsy monitoring unit (EMU) is a limited clinical resource. Knowledge of the predicting factors for length of stay (LOS) in the EMU may allow providers to more efficiently utilize EMU bed space.
Methods: The records for all consecutive admissions to the EMU at the University of Colorado Hospital between December 1, 2010 and May 31, 2011 (n = 142) were retrospectively reviewed.
Results: Univariate analyses focusing on variables known prior to admission showed that EMU LOS (in hours) was not significantly correlated with patient age, number of event types, or number of antiepileptic drugs at admission. Patients who were admitted to the EMU for event characterization had statistically significantly shorter average LOS than patients who had been admitted as a part of a presurgical evaluation. Patients who reported < = 1 seizure per week had a statistically significantly higher average LOS than patients who reported >= 1 seizure per day. These variables were also significantly predictive of total LOS (p < 0.0001 and p = 0.03, respectively) in multivariate analysis.
Significance: Pre-admission clinical variables may predict EMU LOS. These factors could be used at the administrative level for maximum EMU resource utilization.
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