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Electronic Prediction Rules for Methicillin-Resistant Staphylococcus aureus Colonization

Published online by Cambridge University Press:  02 January 2015

Ari Robicsek ,
Jennifer L. Beaumont ,
Marc-Oliver Wright ,
Richard B. Thomson Jr ,
Karen L. Kaul  and
Lance R. Peterson
Ari Robicsek*
Affiliation:
University of Chicago Pritzker School of Medicine, Chicago, Illinois NorthShore University Health System, Departments of Medicine, Chicago, Illinois Medical Informatics, Chicago, Illinois Infection Control, Chicago, Illinois Center for Clinical Research Informatics, Chicago, Illinois
Jennifer L. Beaumont
Affiliation:
Northwestern University Feinberg School of Medicine, Chicago, Illinois
Marc-Oliver Wright
Affiliation:
Infection Control, Chicago, Illinois
Richard B. Thomson Jr
Affiliation:
University of Chicago Pritzker School of Medicine, Chicago, Illinois Pathology and Laboratory Medicine, Chicago, Illinois
Karen L. Kaul
Affiliation:
University of Chicago Pritzker School of Medicine, Chicago, Illinois Pathology and Laboratory Medicine, Chicago, Illinois
Lance R. Peterson
Affiliation:
University of Chicago Pritzker School of Medicine, Chicago, Illinois Pathology and Laboratory Medicine, Chicago, Illinois Infection Control, Chicago, Illinois
*
NorthShore University HealthSystem, 2650 Ridge Avenue, Burch 124, Evanston, IL 60201 (arobicsek@northshore.org)
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Abstract

Background.

Considerable hospital resources are dedicated to minimizing the number of methicillin-resistant Staphylococcus aureus (MRSA) infections. One tool that is commonly used to achieve this goal is surveillance for MRSA colonization. This process is costly, and false-positive test results lead to isolation of individuals who do not carry MRSA. The performance of this technique would improve if patients who are at high risk of colonization could be readily targeted.

Methods.

Five MRSA colonization prediction rules of varying complexity were derived in a population of 23,314 patients who were consecutively admitted to a US hospital and tested for colonization. Rules incorporated only prospectively collected, structured electronic data found in a patient's record within 1 day of hospital admission. These rules were tested in a validation cohort of 26,650 patients who were admitted to 2 other hospitals.

Results.

The prevalence of MRSA at hospital admission was 2.2% and 4.0% in the derivation and validation cohorts, respectively. Multivariable modeling identified predictors of MRSA colonization among demographic, admission-related, pharmacologic, laboratory, physiologic, and historical variables. Five prediction rules varied in their performance, but each could be used to identify the 30% of patients who accounted for greater than 60% of all cases of MRSA colonization and approximately 70% of all MRSA-associated patient-days. Most rules could also identify the 20% of patients with a greater than 8% chance of colonization and the 40% of patients among whom colonization prevalence was 2% or less.

Conclusions.

We report electronic prediction rules that can fully automate triage of patients for MRSA-related hospital admission testing and that offer significant improvements on previously reported rules. The efficiencies introduced may result in savings to infection control programs with little sacrifice in effectiveness.

Information

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 1 , January 2011 , pp. 9 - 19
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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