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      Evaluation of a computerized antimicrobial susceptibility system with bacteria isolated from animals.

      Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc
      Animals, Anti-Bacterial Agents, pharmacology, Bacteria, drug effects, isolation & purification, Humans, Microbial Sensitivity Tests, methods, Software

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          Abstract

          The BIOMIC is a computerized system used to calculate the minimal inhibitory concentration (MIC) of an antimicrobial agent from a zone of inhibition generated by a disk diffusion test. This system was developed using bacterial pathogens of human origin. This study investigated the use of the BIOMIC system for determining MICs for bacterial pathogens from animals. The MICs generated by the BIOMIC system were compared with the MICs generated using a broth microdilution testing method. A total of 663 drug-organism combinations was tested. These combinations included 3 species of gram-positive bacteria, 5 species of gram-negative bacteria, and the antimicrobial agents ampicillin, gentamicin, cephalothin, ciprofloxacin, enrofloxacin, trimethoprim/sulfamethoxazole, tetracycline, and erythromycin. Overall, the MICs generated by the BIOMIC system correlated with the broth microdilution MICs for 72% of the total drug-organism combinations tested. The Pseudomonas aeruginosa strains tested showed the highest agreement between the 2 systems, with 100% for all antibacterial agents tested, whereas Pasteurella haemolytica, Pasteurella multocida, and enterococci showed the least agreement (76%, 57%, and 47%, respectively). Among these organisms, trimethoprim-sulfa showed the least agreement (31%) and ciprofloxacin showed the greatest (91%). These results indicate that the BIOMIC system could be a useful tool in veterinary medicine for producing quantitative antimicrobial susceptibility results. However, it is currently unreliable for some drug-bacteria combinations. This discrepancy possibly could be corrected by modification of the software using data points generated by a large-scale study.

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