Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-09T07:32:53.321Z Has data issue: false hasContentIssue false
  • English
  • Français

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 ([email protected]).
Get access Rights & Permissions [Opens in a new window]

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.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

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

References

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):509–514.CrossRefGoogle ScholarPubMed
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. http://www.apic.org/Resource_/TinyMceFileManager/Advocacy-PDFs/Surveillance_Technologies_position_paper_2009-5_29_09.pdf. Accessed April 2, 2013.Google Scholar
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):441–448.Google Scholar
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):148–154.Google Scholar
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):463–468.Google Scholar
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.Google Scholar
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):794–801.Google Scholar
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):325–332.Google Scholar
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):S12–S17.Google Scholar
10. Leal, J, Laupland, KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69(3):220–229.CrossRefGoogle ScholarPubMed
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):317–320.Google Scholar
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):1223–1229.CrossRefGoogle ScholarPubMed
13. Yokoe, DS, Noskin, GA, Cunnigham, SM, et al. Enhanced identification of postoperative infections among inpatients. Emerg Infect Dis 2004;10(11):1924–1930.Google Scholar
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.Google Scholar
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. http://www.fda.gov/downloads/Drugs/../Guidances/ucm071185.pdf. Accessed April 3, 2013.Google Scholar
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):1395–1405.CrossRefGoogle ScholarPubMed
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):S216–S227.Google Scholar
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):996–1011.Google Scholar
19. Bureau of Labor Statistics, US Department of Labor. Occupational Outlook Handbook. 2012-3; 2012-13 edition. http://www.bls.gov/ooh/healthcare/registered-nurses.htm. Accessed April 2, 2013.Google Scholar
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.Google Scholar
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):3930–3934.Google Scholar
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):124–127.Google Scholar