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Prediction Model to Identify Patients With Staphylococcus aureus Bacteremia at Risk for Methicillin Resistance

Published online by Cambridge University Press:  02 January 2015

Thomas P. Lodise Jr. ,
Peggy S. McKinnon  and
Michael Rybak
Thomas P. Lodise Jr.
Affiliation:
Anti-Infective Research Laboratory, Detroit Receiving Hospital, Wayne State University, Detroit, Michigan
Peggy S. McKinnon*
Affiliation:
Anti-Infective Research Laboratory, Detroit Receiving Hospital, Wayne State University, Detroit, Michigan
Michael Rybak
Affiliation:
Anti-Infective Research Laboratory, Detroit Receiving Hospital, Wayne State University, Detroit, Michigan
*
Anti-Infective Research Laboratory and Department of Pharmacy Services, Detroit Receiving Hospital, Wayne State University, 4201 St. Antoine Blvd, 1-B UHC, Detroit, MI 48201
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Abstract

Objectives:

To identify institution-specific risk factors for MRSA bacteremia and develop an objective mechanism to estimate the probability of methicillin resistance in a given patient with Staphylococcus aureus bacteremia (SAB).

Design:

A cohort study was performed to identify institution-specific risk factors for MRSA. Logistic regression was used to model the likelihood of MRSA A stepwise approach was employed to derive a parsimonious model. The MRSA prediction tool was developed from the final model.

Setting:

A 279-bed, level 1 trauma center.

Patients:

Between January 1, 1999, and June 30, 2001, 494 patients with clinically significant episodes of SAB were identified.

Results:

The MRSA rate was 45.5%. Of 18 characteristics included in the logistic regression, the only independent features for MRSA were prior antibiotic exposure (OR, 9.2; CI95, 4.8 to 17.9), hospital onset (OR, 3.0; CI95, 1.9 to 4.9), history of hospitalization (OR, 2.5; CI95, 1.5 to 3.8), and presence of decubitus ulcers (OR, 2.5; CI95, 1.2 to 4.9). The prediction tool was derived from the final model, which was shown to accurately reflect the actual MRSA distribution in the cohort.

Conclusion:

Through multivariate modeling techniques, we were able to identify the most important determinants of MRSA at our institution and develop a tool to predict the probability of methicillin resistance in a patient with SAB. This knowledge can be used to guide empiric antibiotic selection. In the era of antibiotic resistance, such tools are essential to prevent indiscriminate antibiotic use and preserve the longevity of current antimicrobials.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 24 , Issue 9 , September 2003 , pp. 655 - 661
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2003

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