Effect of atypical antibiotic resistance on microorganism identification by pattern recognition.
We classified microorganisms from the clinical laboratory by using information provided by the Gram stain and antibiotic sensitivity profiles obtained with the Bauer-Kirby technique. Approximately 4,000 microorganisms, routinely identified and tested for antibiotic sensitivities in a large hospital microbiology laboratory, were used as a data set for several pattern recognition classification methods: K--nearest-neighbor analysis, statistical isolinear multicomponent analysis, Bayesian inference, and linear discriminant analysis. K--nearest-neighbor analysis yielded the highest prospective classification accuracy for gram-negative organisms, 90%. When those organisms displaying an atypical antibiotic resistance pattern were excluded from the data, the gram-negative classification accuracy improved to 95%. These results are inferior to currently accepted biochemical identification methods. Microorganisms with atypical antibiotic resistance patterns are likely to be misidentified and are common enough (17% of our isolates) to limit the feasibility of routine identification of microorganisms from their antibiotic sensitivities.
D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D001419:Bacteria / Q000145:classification* / Q000187:drug effects; D001424:Bacterial Infections / Q000382:microbiology*; D004352:Drug Resistance, Microbial; D006801:Humans; D010363:Pattern Recognition, Automated
D000900:Anti-Bacterial Agents