Cited by 41Bacterial Identification and Susceptibility TestingClinical Laboratory Practices and Quality ControlUrinary Tract Infections Management

Evaluation of the WASPLab Segregation Software To Automatically Analyze Urine Cultures Using Routine Blood and MacConkey Agars

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Faron, Matthew L., Buchan, Blake W., Relich, Ryan F., Clark, James, Ledeboer, Nathan A.·2020·Journal of Clinical Microbiology

Abstract

Automation of the clinical microbiology laboratory has become more prominent as laboratories face higher specimen volumes and understaffing and are becoming more consolidated. One recent advancement is the use of digital image analysis to rapidly distinguish between chromogenic growth for screening bacterial cultures. In this study, colony segregation software developed by Copan (Brescia, Italy) was evaluated to distinguish between significant growth and no growth of urine cultures plated onto standard blood and MacConkey agars. Specimens from 3 sites were processed on a WASP instrument (Copan) and incubated on the WASPLab platform (Copan), and plates were imaged at 0 and 24 hours postinoculation. Images were read by technologists following validated laboratory protocols (VLPs), and results were recorded in the laboratory information systems (LIS). Image analysis performed colony counts on the 24-hour images, and results were compared with the VLP. A total of 12,931 urine cultures were tested and analyzed with an overall sensitivity and specificity of 99.8% and 72.0%, respectively. After secondary review, 91.1% of manual-positive/automation-negative specimens were due to expert rules that reported the plate as contaminated or growing only normal flora and not due to threshold counts. Nine specimens were found to be manual-positive/automation-negative; a secondary review demonstrated that the results of 8 of these specimens were due to growth of microcolonies that were program…

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Crossref · 10.1128/jcm.01683-19

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  • Prior studies have shown the accuracy of these algorithms across multiple specimen types and media.

    “Prior studies have shown the accuracy of these algorithms across multiple specimen types and media (2–6).”

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Authors
Faron, M. L., Buchan, B. W., Relich, R. F., Clark, J., & Ledeboer, N. A.
Affiliations
  • Medical College of Wisconsin (US)
  • Wisconsin Diagnostic Laboratories, Milwaukee, Wisconsin, USA
  • Indiana University Health (US)
  • Alverno Clinical Laboratories, Hammond, Indiana, USA
Venue
Journal of Clinical Microbiology
Published
2020-03-25
Volume
58
Issue
4
ISSN
  • 0095-1137 (ISSN-L)
  • 1098-660X
OpenAlex
W3000278843
Cited by
41
References
2
Citations by year
2026: 11 · 2025: 3 · 2024: 5 · 2023: 7 · 2022: 4 · 2021: 5 · 2020: 6
FWCI
3.64
Type
journal article
Language
en
Open access
Locations
  • Journal of Clinical Microbiology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Bacterial Identification and Susceptibility Testing
  • Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
  • Health Sciences › Medicine › Epidemiology › Urinary Tract Infections Management
Keywords
urine culture, automated colony counting, digital image analysis, blood agar, MacConkey agar, false-negative results, microcolonies, laboratory automation
MeSH terms
Bacteriological Techniques, Culture Media, Humans, Italy, Software, Urine, Automation, Laboratory

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