Using Surrogate Markers of Injury Severity in Predicting Nosocomial Bloodstream Infections: A Methodological Perspective

2.50
Hdl Handle:
http://hdl.handle.net/10755/148937
Type:
Presentation
Title:
Using Surrogate Markers of Injury Severity in Predicting Nosocomial Bloodstream Infections: A Methodological Perspective
Abstract:
Using Surrogate Markers of Injury Severity in Predicting Nosocomial Bloodstream Infections: A Methodological Perspective
Conference Sponsor:Sigma Theta Tau International
Conference Year:2005
Author:El-Masri, Maher M., RN, PhD
P.I. Institution Name:University of Windsor
Title:Assistant Professor
Co-Authors:Susan M. Fox-Wasylyshyn, RN, MScN
Background: Health and financial consequences of nosocomial bloodstream infections (BSI) constitute a significant burden to the health care system. Therefore, it is important that predictors of these infections be properly investigated. Injury severity indices are numerical scores that have been utilized to predict nosocomial BSI in critically ill patients. However, surrogate markers of injury severity (SMIS) may be more meaningful than the commonly used numerical scores with respect to control and prevention of nosocomial BSI. Purpose: The purpose of this study was to demonstrate the clinical superiority of SMIS over injury severity indices in predicting nosocomial BSI. Method: A prospective non-experimental cohort design was conducted on 361 critically ill trauma patients. Univariate analysis was performed to identify the unadjusted predictors of nosocomial BSI. Three logistic regression models were then examined for their theoretical validity and statistical parsimony. The first model included Injury Severity Score (ISS), five other independent predictors, and excluded SMIS. The second model included all study variables. The third model excluded ISS. Results: Linear regression analysis identified blood transfusion, central venous catheters, and presence of chest tube(s) as SMIS. Logistic regression analysis suggested that ISS was an independent predictor of nosocomial BSI only when SMIS were excluded from the model. The model that included the SMIS and excluded ISS explained the highest variance in nosocomial BSI (Cox and Snell R2 = 0.31; Nagelkerke R2 = 0.54) and had the best positive predictive value (76.7%). Implications: ISS simply permits prediction of the risk of developing nosocomial BSI. However, knowledge of SMIS can provide clinicians with specific targets over which they can intervene in effort to minimize the risk of nosocomial BSI. Hence, SMIS can serve not only as a prediction tool, but can also enhance control and prevention strategies.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleUsing Surrogate Markers of Injury Severity in Predicting Nosocomial Bloodstream Infections: A Methodological Perspectiveen_GB
dc.identifier.urihttp://hdl.handle.net/10755/148937-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Using Surrogate Markers of Injury Severity in Predicting Nosocomial Bloodstream Infections: A Methodological Perspective</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2005</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">El-Masri, Maher M., RN, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Windsor</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Assistant Professor</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">melmasri@uwindsor.ca</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Susan M. Fox-Wasylyshyn, RN, MScN</td></tr><tr><td colspan="2" class="item-abstract">Background: Health and financial consequences of nosocomial bloodstream infections (BSI) constitute a significant burden to the health care system. Therefore, it is important that predictors of these infections be properly investigated. Injury severity indices are numerical scores that have been utilized to predict nosocomial BSI in critically ill patients. However, surrogate markers of injury severity (SMIS) may be more meaningful than the commonly used numerical scores with respect to control and prevention of nosocomial BSI. Purpose: The purpose of this study was to demonstrate the clinical superiority of SMIS over injury severity indices in predicting nosocomial BSI. Method: A prospective non-experimental cohort design was conducted on 361 critically ill trauma patients. Univariate analysis was performed to identify the unadjusted predictors of nosocomial BSI. Three logistic regression models were then examined for their theoretical validity and statistical parsimony. The first model included Injury Severity Score (ISS), five other independent predictors, and excluded SMIS. The second model included all study variables. The third model excluded ISS. Results: Linear regression analysis identified blood transfusion, central venous catheters, and presence of chest tube(s) as SMIS. Logistic regression analysis suggested that ISS was an independent predictor of nosocomial BSI only when SMIS were excluded from the model. The model that included the SMIS and excluded ISS explained the highest variance in nosocomial BSI (Cox and Snell R2 = 0.31; Nagelkerke R2 = 0.54) and had the best positive predictive value (76.7%). Implications: ISS simply permits prediction of the risk of developing nosocomial BSI. However, knowledge of SMIS can provide clinicians with specific targets over which they can intervene in effort to minimize the risk of nosocomial BSI. Hence, SMIS can serve not only as a prediction tool, but can also enhance control and prevention strategies.</td></tr></table>en_GB
dc.date.available2011-10-26T09:53:13Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T09:53:13Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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