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

  • Westyn Branch-Elliman (a1) (a2) (a3), Judith Strymish (a1) (a3) (a4) and Kalpana Gupta (a1) (a5) (a4)



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.


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.


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.


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


Corresponding author

330 Brookline Avenue, Boston, MA 02215 (


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



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1. Grota, PG, Stone, PW, Jordan, S, Pogorzelska, M, Larson, E. Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction. Am J Infect Control 2010;38(7):509514.
2. Greene, L, Cain, T, Khoury, R, Krystofiak, S, Patrick, M, Streed, S. APIC Position Paper: The Importance of Surveillance Technologies in the Prevention of Healthcare-Associated Infections (HAIs). 2009. Accessed April 2, 2013.
3. Tracy, LA, Furuno, JP, Harris, AD, Singer, M, Langenberg, P, Roghmann, MC. Staphylococcus aureus infections in US veterans, Maryland, USA, 1999–2008. Emerg Infect Dis 2011;17(3):441448.
4. Schweizer, ML, Eber, MR, Laxminarayan, R, et al. Validity of ICD-9-CM coding for identifying incident methicillin-resistant Staphylococcus aureus (MRSA) infections: is MRSA infection coded as a chronic disease? Infect Control Hosp Epidemiol 2011;32(2):148154.
5. Schaefer, MK, Ellingson, K, Conover, C, et al. Evaluation of International Classification of Diseases, Ninth Revision, Clinical Modification codes for reporting methicillin-resistant Staphylococcus aureus infections at a hospital in Illinois. Infect Control Hosp Epidemiol 2010;31(5):463468.
6. Jones, M, DuVall, SL, Spuhl, J, Samore, MH, Nielson, C, Rubin, M. Identification of methicillin-resistant Staphylococcus aureus within the nation’s Veterans Affairs medical centers using natural language processing. BMC Med Inform Decis Mak 2012;12:34.
7. Chalfine, A, Cauet, D, Lin, WC, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol 2006;27(8):794801.
8. Wright, MO, Perencevich, EN, Novak, C, Hebden, JN, Standiford, HC, Harris, AD. Preliminary assessment of an automated surveillance system for infection control. Infect Control Hosp Epidemiol 2004;25(4):325332.
9. Furuno, JP, Schweizer, ML, McGregor, JC, Perencevich, EN. Economics of infection control surveillance technology: cost-effective or just cost? Am J Infect Control 2008;36(suppl 3):S12S17.
10. Leal, J, Laupland, KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69(3):220229.
11. Spolaore, P, Pellizzer, G, Fedeli, U, et al. Linkage of microbiology reports and hospital discharge diagnoses for surveillance of surgical site infections. J Hosp Infect 2005;60(4):317320.
12. Bolon, MK, Hooper, D, Stevenson, KB, et al. Improved surveillance for surgical site infections after orthopedic implantation procedures: extending applications for automated data. Clin Infect Dis 2009;48(9):12231229.
13. Yokoe, DS, Noskin, GA, Cunnigham, SM, et al. Enhanced identification of postoperative infections among inpatients. Emerg Infect Dis 2004;10(11):19241930.
14. Gupta, K, Martinello, RA, Young, M, Strymish, J, Cho, K, Lawler, E. MRSA nasal carriage patterns and the subsequent risk of conversion between patterns, infection, and death. PLoS ONE 2013;8(1):e53674.
15. Center for Drug Evaluation and Research, US Food and Drug Administration. Guidance for Industry Acute Bacterial Skin and Skin Structure Infections: Developing Drugs for Treatment. Federal Register. 2010. Accessed April 3, 2013.
16. File, TM Jr, Low, DE, Eckburg, PB, et al. Integrated analysis of FOCUS 1 and FOCUS 2: randomized, doubled-blinded, multicenter phase 3 trials of the efficacy and safety of ceftaroline fosamil versus ceftriaxone in patients with community-acquired pneumonia. Clin Infect Dis 2010;51(12):13951405.
17. Rubin, RH, Shapiro, ED, Andriole, VT, Davis, RJ, Stamm, WE; Infectious Diseases Society of America, Food and Drug Administration. Evaluation of new anti-infective drugs for the treatment of urinary tract infection. Clin Infect Dis 1992;15(suppl 1):S216S227.
18. Hidron, AI, Edwards, JR, Patel, J, et al. NHSN annual update: antimicrobial-resistant pathogens associated with healthcare-associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007. Infect Control Hosp Epidemiol 2008;29(11):9961011.
19. Bureau of Labor Statistics, US Department of Labor. Occupational Outlook Handbook. 2012-3; 2012-13 edition. Accessed April 2, 2013.
20. Kock, R, Becker, K, Cookson, B, et al. Methicillin-resistant Staphylococcus aureus (MRSA): burden of disease and control challenges in Europe. Euro Surveill 2010;15(41):19688.
21. Gupta, K, Macintyre, A, Vanasse, G, Dembry, LM. Trends in prescribing beta-lactam antibiotics for treatment of community-associated methicillin-resistant Staphylococcus aureus infections. J Clin Microbiol 2007;45(12):39303934.
22. Gastmeier, P, Brauer, H, Hauer, T, Schumacher, M, Daschner, F, Ruden, H. How many nosocomial infections are missed if identification is restricted to patients with either microbiology reports or antibiotic administration? Infect Control Hosp Epidemiol 1999;20(2):124127.


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