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Development of a Novel Electronic Surveillance System for Monitoring of Bloodstream Infections

Published online by Cambridge University Press:  02 January 2015

Jenine Leal ,
Daniel B. Gregson ,
Terry Ross ,
Ward W. Flemons ,
Deirdre L. Church  and
Kevin B. Laupland
Jenine Leal
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada
Daniel B. Gregson
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada
Terry Ross
Affiliation:
Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada Centre for Antimicrobial Resistance, University of Calgary, Alberta Health Services and Calgary Laboratory Services, Calgary, Alberta, Canada
Ward W. Flemons
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada
Deirdre L. Church
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada Division of Microbiology, Calgary Laboratory Services, Calgary, Alberta, Canada
Kevin B. Laupland*
Affiliation:
Department of Medicine, University of Calgary, Calgary, Alberta, Canada Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, Alberta, Canada Centre for Antimicrobial Resistance, University of Calgary, Alberta Health Services and Calgary Laboratory Services, Calgary, Alberta, Canada
*
Alberta Health Services, Foothills Medical Centre, Room 719, North Tower, 1403-29th Street NW, Calgary, AB T2N 2T9, Canada (Kevin.laupland@albertahealthservices.ca)
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Extract

Background.

Electronic surveillance systems (ESSs) that utilize existing information in databases are more efficient than conventional infection surveillance methods.

Objective.

To develop an ESS for monitoring bloodstream infections (BSIs) and assess whether data obtained from the ESS were in agreement with data obtained by traditional manual medical-record review.

Methods.

An ESS was developed by linking data from regional laboratory and hospital administrative databases. Definitions for excluding BSI episodes representing contamination and duplicate episodes were developed and applied. Infections were classified as nosocomial infections, healthcare-associated community-onset infections, or community-acquired infections. For a random sample of episodes, data in the ESS were compared with data obtained by independent medical chart review.

Results.

From the records of the 306 patients whose infections were selected for comparative review, the ESS identified 323 episodes of BSI, of which 107 (33%) were classified as healthcare-associated community-onset infections, 108 (33%) were classified as community-acquired infections, 107 (33%) were classified as nosocomial infections, and 1 (0.3%) could not be classified. In comparison, 310 episodes were identified by use of medical chart review, of which 116 (37%) were classified as healthcare-associated community-onset infections, 95 (31%) as community-acquired infections, and 99 (32%) as nosocomial infections. For 302 episodes of BSI, there was concordance between the findings of the ESS and those of traditional manual chart review. Of the additional 21 discordant episodes that were identified by use of the ESS, 17 (81%) were classified as representing isolation of skin contaminants, by use of chart review. Of the additional 8 discordant episodes further identified by use of chart review, most were classified as repeat or polymicrobial episodes of disease. There was an overall 85% agreement between the findings of the ESS and those of chart review (K = 0.78; standard error, K = 0.04) for classification according to location of acquisition.

Conclusion.

Our novel ESS allows episodes of BSI to be identified and classified with a high degree of accuracy. This system requires validation in other cohorts and settings.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 31 , Issue 7 , July 2010 , pp. 740 - 747
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2010

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