Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T03:51:28.328Z Has data issue: false hasContentIssue false

A Multicenter Longitudinal Study of Hospital-Onset Bacteremia: Time for a New Quality Outcome Measure?

Published online by Cambridge University Press:  23 October 2015

Clare Rock*
Affiliation:
Department of Medicine, Division of Infectious Diseases, Johns Hopkins University, Baltimore, Maryland
Kerri A. Thom
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Anthony D. Harris
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Shanahan Li
Affiliation:
Department of Biostatistics, Indiana University Fairbanks School of Public Health, Indianapolis, Indiana
Daniel Morgan
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Aaron M. Milstone
Affiliation:
Department of Pediatrics, Division of Pediatric Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland
Brian Caffo
Affiliation:
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
Manjari Joshi
Affiliation:
Division of Infectious Diseases, University of Maryland Medical Center, School of Medicine, Baltimore, Maryland
Surbhi Leekha
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
*
Address correspondence to Clare Rock, MD, MS, Department of Medicine, Division of Infectious Diseases, Johns Hopkins University, 600 North Wolfe Street/Osler 425 Baltimore, MD 21287-5425 (Clare.Rock@jhmi.edu).

Abstract

BACKGROUND

Central-line–associated bloodstream infection (CLABSI) rate is an important quality measure, but it suffers from subjectivity and interrater variability, and decreasing national CLABSI rates may compromise its power to discriminate between hospitals. This study evaluates hospital-onset bacteremia (HOB, ie, any positive blood culture obtained 48 hours post admission) as a healthcare-associated infection–related outcome measure by assessing the association between HOB and CLABSI rates and comparing the power of each to discriminate quality among intensive care units (ICUs).

METHODS

In this multicenter study, ICUs provided monthly CLABSI and HOB rates for 2012 and 2013. A Poisson regression model was used to assess the association between these 2 rates. We compared the power of each measure to discriminate between ICUs using standardized infection ratios (SIRs) with 95% confidence intervals (CIs). A measure was defined as having greater power to discriminate if more of the SIRs (with surrounding CIs) were different from 1.

RESULTS

In 80 ICUs from 16 hospitals in the United States and Canada, a total of 663 CLABSIs, 475,420 central line days, 11,280 HOBs, and 966,757 patient days were reported. An absolute change in HOB of 1 per 1,000 patient days was associated with a 2.5% change in CLABSI rate (P<.001). Among the 80 ICUs, 20 (25%) had a CLABSI SIR and 60 (75%) had an HOB SIR that was different from 1 (P<.001).

CONCLUSION

Change in HOB rate is strongly associated with change in CLABSI rate and has greater power to discriminate between ICU performances. Consideration should be given to using HOB to replace CLABSI as an outcome measure in infection prevention quality assessments.

Infect. Control Hosp. Epidemiol. 2016;37(2):143–148

Type
Original Articles
Copyright
© 2015 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

*

Author’s name has been corrected since original publication. An erratum notice detailing this change was also published (DOI 10.1017/ice.2015.314).

References

REFERENCES

1. Rajaram, R, Barnard, C, Bilimoria, KY. Concerns about using the patient safety indicator-90 composite in pay-for-performance programs. JAMA 2015;313:897898.Google Scholar
2. Hospital Compare Quality of Care. Medicare website. http://www.medicare.gov/hospitalcompare/search.html. Accessed July 2, 2015.Google Scholar
3. Sexton, DJ, Chen, LF, Moehring, R, Thacker, PA, Anderson, DJ. Casablanca redux: we are shocked that public reporting of rates of central line-associated bloodstream infections are inaccurate. Infect Control Hosp Epidemiol 2012;33:932935.Google Scholar
4. Lin, MY, Hota, B, Khan, YM, et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304:20352041.Google Scholar
5. Stone, PW, Dick, A, Pogorzelska, M, Horan, TC, Furuya, EY, Larson, E. Staffing and structure of infection prevention and control programs. Am J Infect Control 2009;37:351357.Google Scholar
6. Niedner, MF. 2008 National Association of Children’s Hospitals and Related Institutions Pediatric Intensive Care Unit Patient Care FOCUS Group. The harder you look, the more you find: Catheter-associated bloodstream infection surveillance variability. Am J Infect Control 2010;38:585595.Google Scholar
7. Mayer, J, Greene, T, Howell, J, Ying, J, Rubin, MA, Trick, WE, Samore, MH, CDC Prevention Epicenters Program. Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55:364370.Google Scholar
8. Drees, M, Pineles, L, Harris, AD, Morgan, DJ. Variation in definitions and isolation procedures for multidrug-resistant Gram-negative bacteria: a survey of the Society for Healthcare Epidemiology of America Research Network. Infect Control Hosp Epidemiol 2014;35:362366.CrossRefGoogle Scholar
9. Morgan, DJ, Meddings, J, Saint, S, et al. Does nonpayment for hospital-acquired catheter-associated urinary tract infections lead to overtesting and increased antimicrobial prescribing? Clin Infect Dis 2012;55:923929.CrossRefGoogle ScholarPubMed
10. National Health Safety Network. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/. Accessed July 2, 2015.Google Scholar
11. Hadfield, JD. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 2010;33:122.CrossRefGoogle Scholar
12. R Core team 2014. R: A language and environment for statistical computing. R Project website. http://www.R-project.org. Accessed July 2, 2015.Google Scholar
13. Taylor, WJ, Redden, D, Dalbeth, N, et al. Application of the OMERACT filter to measures of core outcome domains in recent clinical studies of acute gout. J Rheumatol 2014;41:574580.Google Scholar
14. Leekha, S, Li, S, Thom, KA, et al. Comparison of total hospital-acquired bloodstream infections to central line-associated bloodstream infections and implications for outcome measures in infection control. Infect Control Hosp Epidemiol 2013;34:984986.Google Scholar
15. Greenland, S. On sample-size and power calculations for studies using confidence intervals. Am J Epidemiol 1988;128:231237.Google Scholar
16. Gordon, I. Sample size estimation in occupational mortality studies with use of confidence interval theory. Am J Epidemiol 1987:125158125162.Google ScholarPubMed
17. Hospital-Acquired Conditions. Centers for Medicare and Medicaid Services website. http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/HospitalAcqCond/Hospital-Acquired_Conditions.html. Published 2014. Accessed July 2, 2015.Google Scholar
18. Vallés, J, León, C, Alvarez-Lerma, F. Nosocomial bacteremia in critically ill patients: a multicenter study evaluating epidemiology and prognosis. Spanish Collaborative Group for Infections in Intensive Care Units of Sociedad Espanola de Medicina Intensiva y Unidades Coronarias (SEMIUC). Clin Infect Dis 1997;24:387395.Google Scholar
19. Haut, ER, Pronovost, PJ. Surveillance bias in outcomes reporting. JAMA 2011;305:24622463.CrossRefGoogle ScholarPubMed
20. Yokoe, DS, Anderson, DJ, Berenholtz, SM, et al. A compendium of strategies to prevent healthcare-associated infections in acute care hospitals: 2014 updates. Infect Control Hosp Epidemiol 2014;35:S21S31.Google Scholar
Supplementary material: PDF

Rock supplementary material

Online Appendix

Download Rock supplementary material(PDF)
PDF 79.7 KB