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Automated Surveillance for Central Line–Associated Bloodstream Infection in Intensive Care Units

Published online by Cambridge University Press:  02 January 2015

Keith F. Woeltje ,
Anne M. Butler ,
Ashleigh J. Goris ,
Nhial T. Tutlam ,
Joshua A. Doherty ,
M. Brandon Westover ,
Vicky Ferris  and
Thomas C. Bailey
Keith F. Woeltje*
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, Missouri
Anne M. Butler
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, Missouri
Ashleigh J. Goris
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, Missouri
Nhial T. Tutlam
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, Missouri
Joshua A. Doherty
Affiliation:
BJC Healthcare, St. Louis, Missouri
M. Brandon Westover
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, Missouri
Vicky Ferris
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, Missouri
Thomas C. Bailey
Affiliation:
School of Medicine, Washington University in St. Louis, St. Louis, Missouri BJC Healthcare, St. Louis, Missouri
*
School of Medicine, Washington University in St. Louis, Box 8051, 660 S. Euclid, St. Louis, MO 63110 ([email protected])
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Abstract

Objective.

To develop and evaluate computer algorithms with high negative predictive values that augment traditional surveillance for central line–associated bloodstream infection (CLABSI).

Setting.

Barnes-Jewish Hospital, a 1,250-bed tertiary care academic hospital in Saint Louis, Missouri.

Methods.

We evaluated all adult patients in intensive care units who had blood samples collected during the period from July 1, 2005, to June 30,2006, that were positive for a recognized pathogen on culture. Each isolate recovered from culture was evaluated using the definitions for nosocomial CLABSI provided by the National Healthcare Safety Network of the Centers for Disease Control and Prevention. Using manual surveillance by infection prevention specialists as the gold standard, we assessed the ability of various combinations of dichotomous rules to determine whether an isolate was associated with a CLABSI. Sensitivity, specificity, and predictive values were calculated.

Results.

Infection prevention specialists identified 67 cases of CLABSI associated with 771 isolates recovered from blood samples. The algorithms excluded approximately 40%-62% of the isolates from consideration as possible causes of CLABSI. The simplest algorithm, with 2 dichotomous rules (ie, the collection of blood samples more than 48 hours after admission and the presence of a central venous catheter within 48 hours before collection of blood samples), had the highest negative predictive value (99.4%) and the lowest specificity (44.2%) for CLABSI. Augmentation of this algorithm with rules for common skin contaminants confirmed by another positive blood culture result yielded in a negative predictive value of 99.2% and a specificity of 68.0%.

Conclusions.

An automated approach to surveillance for CLABSI that is characterized by a high negative predictive value can accurately identify and exclude positive culture results not representing CLABSI from further manual surveillance.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 29 , Issue 9 , September 2008 , pp. 842 - 846
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2008

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References

1.Dimick, JB, Pelz, RK, Consunji, R, Swoboda, SM, Hendrix, CW, Lipsett, PA. Increased resource use associated with catheter-related bloodstream infection in the surgical intensive care unit. Arch Surg 2001;136:229–234.Google Scholar
2.Warren, DK, Quadir, WW, Hollenbeak, CS, Elward, AM, Cox, MJ, Fraser, VJ. Attributable cost of catheter-associated bloodstream infections among intensive care patients in a nonteaching hospital. Crit Care Med 2006;34:2084–2089.Google Scholar
3.Blot, SI, Depuydt, P, Annemans, L, et al.Clinical and economic outcomes in critically ill patients with nosocomial catheter-related bloodstream infections. Clin Infect Dis 2005;41:1591–1598.Google Scholar
4.O'Grady, NP, Alexander, M, Dellinger, EP, et al.Guidelines for the prevention of intravascular catheter-related infections. Centers for Disease Control and Prevention. MMWR Recomm Rep 2002;51(RR-10):1–29.Google Scholar
5.Centers for Disease Control and Prevention. National Center for Infectious Diseases. The National Healthcare Safety Network (NHSN) Manual. Department of Health and Human Services Publication 5-24-0007. Available at: http://www.cdc.gov/ncidod/dhqp/pdf/nhsn/NHSN_Manual_PatientSafetyProtocol_CURRENT.pdf. Accessed October 31, 2008.Google Scholar
6.Edwards, JR, Peterson, KD, Andrus, ML, et al.National Healthcare Safety Network (NHSN) Report, data summary for 2006, issued June 2007. Am J Infect Control 2007;35:290–301.Google Scholar
7.Bellini, C, Petignat, C, Francioli, P, et al.Comparison of automated strategies for surveillance of nosocomial bacteremia. Infect Control Hosp Epidemiol 2007;28:1030–1035.Google Scholar
8.Graham, PL III, San Gabriel, P, Lutwick, S, Haas, J, Saiman, L. Validation of a multicenter computer-based surveillance system for hospital-acquired bloodstream infections in neonatal intensive care departments. Am J Infect Control 2004;32:232–234.CrossRefGoogle ScholarPubMed
9.Trick, WE, Zagorski, BM, Tokars, JI, et al.Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10:1612–1620.Google Scholar
10.Yokoe, DS, Anderson, J, Chambers, R, et al.Simplified surveillance for nosocomial bloodstream infections. Infect Control Hosp Epidemiol 1998;19:657–660.Google Scholar
11.Evans, RS, Larsen, RA, Burke, JP, et al.Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:1007–1011.Google Scholar
12.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;20:124–127.Google Scholar
13.Piatt, R, Yokoe, DS, Sands, KE; CDC Eastern Massachusetts Prevention Epicenter Investigators. Automated methods for surveillance of surgical site infections. Emerg Infect Dis 2001;7:212–216.Google Scholar
14.Pokorny, L, Rovira, A, Martin-Baranera, M, Gimeno, C, Alonso-Tarres, C, Vilarasau, J. Automatic detection of patients with nosocomial infection by a computer-based surveillance system: a validation study in a general hospital. Infect Control Hosp Epidemiol 2006;27:500–503.Google Scholar