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Development and Validation of a Simple and Easy-to-Employ Electronic Algorithm for Identifying Clinical Methicillin-Resistant Staphylococcus aureus Infection

Published online by Cambridge University Press:  10 May 2016

Westyn Branch-Elliman ,
Judith Strymish  and
Kalpana Gupta
Westyn Branch-Elliman*
Affiliation:
Boston Veterans Affairs Healthcare System, Boston, Massachusetts Divisions of Infectious Diseases and Infection Control, Beth Israel Deaconess Medical Center, Boston, Massachusetts Harvard University Medical School, Boston, Massachusetts
Judith Strymish
Affiliation:
Boston Veterans Affairs Healthcare System, Boston, Massachusetts Harvard University Medical School, Boston, Massachusetts Patient Safety Center of Inquiry on Measurement to Advance Patient Safety, Veterans Affairs National Healthcare System, Boston, Massachusetts
Kalpana Gupta
Affiliation:
Boston Veterans Affairs Healthcare System, Boston, Massachusetts Boston University School of Medicine, Boston, Massachusetts Patient Safety Center of Inquiry on Measurement to Advance Patient Safety, Veterans Affairs National Healthcare System, Boston, Massachusetts
*
330 Brookline Avenue, Boston, MA 02215 (wb-e@fsm.northwestern.edu).
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Abstract

Background.

With growing demands to track and publicly report and compare infection rates, efforts to utilize automated surveillance systems are increasing. We developed and validated a simple algorithm for identifying patients with clinical methicillin-resistant Staphylococcus aureus (MRSA) infection using microbiologic and antimicrobial variables. We also estimated resource savings.

Methods.

Patients who had a culture positive for MRSA at any of 5 acute care Veterans Affairs hospitals were eligible. Clinical infection was defined on the basis of manual chart review. The electronic algorithm defined clinical MRSA infection as a positive non-sterile-site culture with receipt of MRSA-active antibiotics during the 5 days prior to or after the culture.

Results.

In total, 246 unique non-sterile-site cultures were included, of which 168 represented infection. The sensitivity (43.4%–95.8%) and specificity (34.6%–84.6%) of the electronic algorithm varied depending on the combination of antimicrobials included. On multivariable analysis, predictors of algorithm failure were outpatient status (odds ratio, 0.23 [95% confidence interval, 0.10–0.56]) and respiratory culture (odds ratio, 0.29 [95% confidence interval, 0.13–0.65]). The median cost was $2.43 per chart given 4.6 minutes of review time per chart.

Conclusions.

Our simple electronic algorithm for detecting clinical MRSA infections has excellent sensitivity and good specificity. Implementation of this electronic system may streamline and standardize surveillance and reporting efforts.

Infect Control Hosp Epidemiol 2014;35(6):692–698

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 35 , Issue 6 , 01 June 2014 , pp. 692 - 698
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved.

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Footnotes

Presented in part: ID Week 2013, San Francisco, California (abstract).

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