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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 (kwoeltje@im.wustl.edu)
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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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