Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T22:33:10.398Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparison of Total Hospital-Acquired Bloodstream Infections to Central Line–Associated Bloodstream Infections and Implications for Outcome Measures in Infection Control

Published online by Cambridge University Press:  02 January 2015

Surbhi Leekha ,
Shanshan Li ,
Kerri A. Thom ,
Michael Anne Preas ,
Brian S. Caffo ,
Daniel J. Morgan  and
Anthony D. Harris
Surbhi Leekha*
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Shanshan Li
Affiliation:
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
Kerri A. Thom
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
Michael Anne Preas
Affiliation:
Department of Infection Prevention, University of Maryland Medical Center, Baltimore, Maryland
Brian S. Caffo
Affiliation:
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
Daniel J. Morgan
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland Veterans Affairs Maryland Healthcare System, Baltimore, Maryland
Anthony D. Harris
Affiliation:
Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland Veterans Affairs Maryland Healthcare System, Baltimore, Maryland
*
University of Maryland School of Medicine, 110 South Paca Street, 6th Floor, Baltimore, MD 21201 ([email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The validity of the central line-associated bloodstream infection (CLABSI) measure is compromised by subjectivity. We observed significant decreases in both CLABSIs and total hospital-acquired bloodstream infections (BSIs) following a CLABSI prevention intervention in adult intensive care units. Total hospital-acquired BSIs could be explored as an adjunct, objective CLABSI measure.

Type
Concise Communication
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 9 , September 2013 , pp. 984 - 986
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

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.Horan, TC, Andrus, M, Dudeck, MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control 2008;36(5):309–332.Google Scholar
2.Mayer, J, Greene, T, Howell, J, et al.Agreement in classifying bloodstream infections among multiple reviewers conducting surveillance. Clin Infect Dis 2012;55(3):364–370.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(9):932–935.CrossRefGoogle ScholarPubMed
4.Lin, MY, Hota, B, Khan, YM, et al.Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. JAMA 2010;304(18):2035–2041.CrossRefGoogle ScholarPubMed
5.Deyo, RA, Cherkin, DC, Ciol, MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45(6):613–619.Google Scholar
6.Fetter, RB, Shin, Y, Freeman, JL, Averiii, RF, Thompson, JD. Case mix definition by diagnosis-related groups. Med Care 1980; 18(suppl 2):1–53.Google Scholar
7. Clinical Data Base/Resource Manager. University HealthSystem Consortium website. https://www.uhc.edu/11536.htm. Accessed December 12, 2012.Google Scholar
8.McCullagh, P, Neider, JA. Generalized Linear Models. Vol 37. 2nd ed. London: Chapman & Hall/CRC, 1989.Google Scholar
9. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2011. http://www.R-project.org/. Accessed April 18, 2013.Google Scholar
10.Hota, B, Lin, M, Doherty, JA, et al.Formulation of a model for automating infection surveillance: algorithmic detection of central-line associated bloodstream infection. J Am Med Inform Assoc 2010;17(1):42–48.CrossRefGoogle Scholar