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Related Experiment Videos

[Quality control for antimicrobial susceptibility test using correlation between MIC results].

Tamio Ueno1, Kazufumi Hiramatsu, Tadao Nakano

  • 1Department of Laboratory Medicine, Oita Medical University Hospital, Hasama, Oita, 879-5593.

Rinsho Byori. the Japanese Journal of Clinical Pathology
|August 22, 2002
PubMed
Summary

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A new computer program analyzes antimicrobial susceptibility test results to detect accidental errors. This method, using correlations between antimicrobial agents, enhances daily quality control in clinical laboratories.

Area of Science:

  • Clinical microbiology
  • Medical laboratory science
  • Diagnostic testing

Context:

  • The National Committee for Clinical Laboratory Standards (NCCLS) method is standard for daily antimicrobial susceptibility testing (AST) quality control.
  • However, the NCCLS method primarily detects systematic errors, missing accidental errors in AST.
  • Accidental errors can compromise the accuracy of AST results, impacting patient treatment.

Purpose:

  • To develop and validate a novel computer-based method for detecting accidental errors in AST.
  • To utilize the correlation between minimum inhibitory concentration (MIC) results of different antimicrobial agents for error detection.
  • To improve the reliability of daily AST quality control procedures.

Summary:

  • A computer program was developed using correlations between MIC results of antimicrobial agents (correlation coefficient >= 0.7) from 98 bacterial species.

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  • 127 combinations for 13 species were selected and validated using 666 strains, identifying 47 unexpected results.
  • The method confirmed errors in 12 combinations from 3 strains, demonstrating its applicability for AST quality control.

    • Impact:

    • This correlation-based method offers a valuable tool for enhancing the accuracy of daily AST quality control.
    • It provides a complementary approach to existing methods for detecting accidental errors in laboratory testing.
    • Improved AST accuracy can lead to more effective antimicrobial therapy and better patient outcomes.