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Related Experiment Videos

Agreement between visual and automated UniScept API readings.

C M O'Hara1, D L Rhoden, P B Smith

  • 1Nosocomial Infections Laboratory Branch, Centers for Disease Control, Atlanta, Georgia 30333.

Journal of Clinical Microbiology
|March 1, 1990
PubMed
Summary

Automated readings of the UniScept API system show high agreement with visual readings for bacterial identification and antimicrobial susceptibility testing. This automated method is an acceptable alternative for clinical microbiology labs.

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Area of Science:

  • Clinical Microbiology
  • Diagnostic Technology
  • Bacterial Identification and Susceptibility Testing

Background:

  • Accurate bacterial identification and antimicrobial susceptibility testing are crucial for effective patient treatment.
  • Manual visual readings of diagnostic panels can be subjective and time-consuming.
  • Automated systems aim to improve efficiency and consistency in laboratory diagnostics.

Purpose of the Study:

  • To evaluate the agreement between visual and automated readings of the UniScept API system.
  • To assess the accuracy of automated readings for both bacterial identification and antimicrobial susceptibility panels.
  • To determine the clinical acceptability of automated readings in a microbiology setting.

Main Methods:

  • Biochemical responses of 340 bacterial cultures were read visually and automatically using the UniScept API 20E system.

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  • Antimicrobial susceptibility results for 470 bacterial cultures were compared between visual and automated readings using the UniScept MIC system with 17 antimicrobial agents.
  • Discrepancy rates, including very major discrepancies, were analyzed for both identification and susceptibility panels.

    • Main Results:

    • Automated and visual readings demonstrated 99.3% agreement for biochemical tests, with indole and citrate showing the most disagreements.
    • For antimicrobial susceptibility, agreement within +/- 1 dilution was 94.1% for enteric fermenters and 91.7% for other cultures.
    • Very major discrepancies occurred in 0.95% of automated susceptibility readings, which is within acceptable clinical limits.

    Conclusions:

    • Automated reading of the UniScept API 20E system's biochemical panels is a reliable alternative to visual reading.
    • The automated reading of antimicrobial susceptibility results is acceptable, despite a small percentage of very major discrepancies, particularly in glucose nonfermenters.
    • The UniScept API system's automated readings offer a viable option for clinical microbiology laboratories seeking efficiency and consistency.