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Comparison of Automated Strategies for Surveillance of Nosocomial Bacteremia

Published online by Cambridge University Press:  02 January 2015

Cristina Bellini ,
Christiane Petignat ,
Patrick Francioli ,
Aline Wenger ,
Jacques Bille ,
Adriana Klopotov ,
Yannick Vallet ,
René Patthey  and
Giorgio Zanetti
Cristina Bellini*
Affiliation:
Service of Hospital Preventive Medecine, University Hospital, Lausanne, Switzerland
Christiane Petignat
Affiliation:
Service of Hospital Preventive Medecine, University Hospital, Lausanne, Switzerland
Patrick Francioli
Affiliation:
Service of Hospital Preventive Medecine, University Hospital, Lausanne, Switzerland
Aline Wenger
Affiliation:
Microbiological Institute, University Hospital, Lausanne, Switzerland
Jacques Bille
Affiliation:
Microbiological Institute, University Hospital, Lausanne, Switzerland
Adriana Klopotov
Affiliation:
Informatics Institute, University Hospital, Lausanne, Switzerland
Yannick Vallet
Affiliation:
Informatics Institute, University Hospital, Lausanne, Switzerland
René Patthey
Affiliation:
Informatics Institute, University Hospital, Lausanne, Switzerland
Giorgio Zanetti
Affiliation:
Service of Hospital Preventive Medecine, University Hospital, Lausanne, Switzerland
*
Service de Médecine Préventive Hospitalière—CHUV, Rue du Bugnon 46, 1011 Lausanne, Switzerland ([email protected])
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Abstract

Objective.

Surveillance of nosocomial bloodstream infection (BSI) is recommended, but time-consuming. We explored strategies for automated surveillance.

Methods.

Cohort study. We prospectively processed microbiological and administrative patient data with computerized algorithms to identify contaminated blood cultures, community-acquired BSI, and hospital-acquired BSI and used algorithms to classify the latter on the basis of whether it was a catheter-associated infection. We compared the automatic classification with an assessment (71% prospective) of clinical data.

Setting.

An 850-bed university hospital.

Participants.

All adult patients admitted to general surgery, internal medicine, a medical intensive care unit, or a surgical intensive care unit over 3 years.

Results.

The results of the automated surveillance were 95% concordant with those of classical surveillance based on the assessment of clinical data in distinguishing contamination, community-acquired BSI, and hospital-acquired BSI in a random sample of 100 cases of bacteremia. The two methods were 74% concordant in classifying 351 consecutive episodes of nosocomial BSI with respect to whether the BSI was catheter-associated. Prolonged episodes of BSI, mostly fungemia, that were counted multiple times and incorrect classification of BSI clinically imputable to catheter infection accounted for 81% of the misclassifications in automated surveillance. By counting episodes of fungemia only once per hospital stay and by considering all cases of coagulase-negative staphylococcal BSI to be catheter-related, we improved concordance with clinical assessment to 82%. With these adjustments, automated surveillance for detection of catheter-related BSI had a sensitivity of 78% and a specificity of 93%; for detection of other types of nosocomial BSI, the sensitivity was 98% and the specificity was 69%.

Conclusion.

Automated strategies are convenient alternatives to manual surveillance of nosocomial BSI.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 28 , Issue 9 , September 2007 , pp. 1030 - 1035
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2007

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