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Analysis of antimicrobial susceptibility results using microcomputers.

K W Chan1, J Ling, K L Ling

  • 1Department of Pathology, University of Hong Kong.

Journal of Clinical Microbiology
|January 1, 1988
PubMed
Summary

A new computer program analyzes antimicrobial susceptibility testing results for numerous organisms and drugs. It provides detailed statistical outputs and MIC distributions, aiding in antimicrobial resistance analysis.

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Area of Science:

  • Microbiology
  • Computational Biology
  • Pharmacology

Background:

  • Accurate analysis of antimicrobial susceptibility testing (AST) is crucial for guiding clinical treatment and monitoring antimicrobial resistance.
  • Manual analysis of large AST datasets is time-consuming and prone to errors.
  • Efficient computational tools are needed to process and interpret complex AST data.

Purpose of the Study:

  • To develop and describe a computer program for analyzing antimicrobial susceptibility results.
  • To provide comprehensive statistical data and graphical representations of Minimum Inhibitory Concentrations (MICs).
  • To facilitate the interpretation of large-scale antimicrobial testing data.

Main Methods:

  • Development of a computer program utilizing dBASE III command language on an IBM-PC-compatible microcomputer.

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  • Implementation of algorithms to process antimicrobial susceptibility data for multiple organisms and agents.
  • Generation of detailed outputs including MIC distributions, percentages, MIC50, MIC90, MIC ranges, and geometric means.
  • Inclusion of histogram plots for visualizing MIC distributions.

    • Main Results:

    • The program efficiently analyzes large volumes of antimicrobial susceptibility data.
    • Outputs include quantitative data (counts, percentages) and key MIC parameters (MIC50, MIC90, geometric means).
    • Graphical representation of MIC distributions aids in data visualization and interpretation.
    • A compiled version ensures rapid execution independent of the dBASE III package.

    Conclusions:

    • The developed computer program offers a robust and efficient solution for analyzing antimicrobial susceptibility data.
    • It provides valuable insights into antimicrobial activity and resistance patterns.
    • This tool can significantly aid researchers and clinicians in managing antimicrobial therapies and surveillance.