Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T05:55:12.507Z Has data issue: false hasContentIssue false

Accuracy of Electronic Surveillance of Catheter-Associated Urinary Tract Infection at an Academic Medical Center

Published online by Cambridge University Press:  10 May 2016

H. L. Wald*
Affiliation:
University of Colorado School of Medicine, Aurora, Colorado
B. Bandle
Affiliation:
University of Colorado School of Medicine, Aurora, Colorado
A. Richard
Affiliation:
University of Colorado School of Medicine, Aurora, Colorado
S. Min
Affiliation:
University of Colorado School of Medicine, Aurora, Colorado
*
Campus Box F480, 13199 West Montview Boulevard, Suite 400, Aurora, CO ([email protected]).

Abstract

Objective.

To develop and validate a methodology for electronic surveillance of catheter-associated urinary tract infections (CAUTIs).

Design.

Diagnostic accuracy study.

Setting.

A 425-bed university hospital.

Subjects.

A total of 1,695 unique inpatient encounters from November 2009 through November 2010 with a high clinical suspicion of CAUTI.

Methods.

An algorithm was developed to identify incident CAUTIs from electronic health records (EHRs) on the basis of the Centers for Disease Control and Prevention (CDC) surveillance definition. CAUTIs identified by electronic surveillance were compared with the reference standard of manual surveillance by infection preventionists. To determine diagnostic accuracy, we created 2 × 2 tables, one unadjusted and one adjusted for misclassification using chart review and case adjudication. Unadjusted and adjusted test statistics (percent agreement, sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], and κ) were calculated.

Results.

Electronic surveillance identified 64 CAUTIs compared with manual surveillance, which identified 19 CAUTIs for 97% agreement, 79% sensitivity, 97% sensitivity, 23% PPV, 100% NPV, and κ of .33. Compared with the reference standard adjusted for misclassification, which identified 55 CAUTIs, electronic surveillance had 98% agreement, 80% sensitivity, 99% specificity, 69% PPV, 99% NPV, and κ of .71.

Conclusion.

The electronic surveillance methodology had a high NPV and a low PPV compared with the reference standard, indicating a role of the electronic algorithm in screening data sets to exclude cases. However, the PPV markedly improved compared with the reference standard adjusted for misclassification, suggesting a future role in surveillance with improvements in EHRs.

Infect Control Hosp Epidemiol 2014;35(6):685–691

Type
Original Articles
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.)

References

1. Klevens, RM, Edwards, JR, Richards, CL Jr, et al. Estimating health care–associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122(2):160166.Google Scholar
2. Foxman, B. Epidemiology of urinary tract infections: incidence, morbidity, and economic costs. Am J Med 2002;113 (1):5s13s.Google Scholar
3. Saint, S. Clinical and economic consequences of nosocomial catheter-related bacteriuria. Am J Infect Control 2000;28(1):6875.Google Scholar
4. Tambyah, PA, Knasinski, V, Maki, DG. The direct costs of nosocomial catheter-associated urinary tract infection in the era of managed care. Infect Control Hosp Epidemiol 2002;23(1):2731.Google Scholar
5. Gould, CV, Umscheid, CA, Agarwal, RK, et al. Guideline for Prevention of Catheter-Associated Urinary Tract Infections 2009. Healthcare Infection Control Practices Advisory Committee (HICPAC), 2009. http://www.cdc.gov/hicpac/pdf/CAUTI/CAUTIguideline2009final.pdf. Accessed April 2, 2014.Google Scholar
6. Wald, HL, Kramer, AM. Nonpayment for harms resulting from medical care: catheter-associated urinary tract infections. JAMA 2007;289(23):27822784.Google Scholar
7. National Healthcare Safety Network. Catheter-Associated Urinary Tract Infection (CAUTI) Event. 2012. http://www.cdc.gov/nhsn/pdfs/pscManual/7pscCauticurrent.pdf. Accessed September 24, 2013.Google Scholar
8. Mayer, J, Greene, T, Howell, J, et al; CDC Prevention Epicenters Program. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55(3):364370.CrossRefGoogle ScholarPubMed
9. Lin, My, Hota, B, Kahn, YM, et al; CDC Prevention Epicenter Program. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:20352041.Google Scholar
10. Burns, AC, Peterson, NJ, Garza, A, et al. Accuracy of a urinary catheter surveillance protocol. Am J Infect Control 2012;(40):5558.Google Scholar
11. Stevenson, KB, Khan, Y, Dickman, J, et al. Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care–associated infections. Am J Infect Control 2008;36(3):155164.Google Scholar
12. Hota, B, Lin, M, Doherty, J, et al; CDC Prevention Epicenter Program. Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection. J Am Med Inform Assoc 2010;17(1):4248.Google Scholar
13. Wald, HL, Kramer, AM. Feasibility of audit and feedback to reduce postoperative urinary catheter duration. J Hosp Med 2011;6(4):183189.Google Scholar
14. Harris, P, Taylor, R, Thielke, R, Payne, J, Gonzalez, N, Conde, JG. Research electronic data capture (REDCap): a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42(2):377381.Google Scholar
15. Choudhuri, JA, Pergamit, RF, Chan, JD, et al. An electronic catheter–associated urinary tract infection surveillance tool. Infect Control Hosp Epidemiol 2011;32(8):757762.Google Scholar
16. Hospitals’ use of infection surveillance software growing fast. FierceHealthIT website. http://www.fiercehealthit.com/story/hospitals-use-infection-surveillance-software-growing-fast/2011-06-23. 2011. Accessed August 4, 2013.Google Scholar
17. 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
18. Wald, H, Richard, A, Bandle, B, et al. Building capacity in HAI prevention research: NICHE and the STOP CAUTI Workgroup. AHRQ Adv (forthcoming).Google Scholar