Effect of atypical antibiotic resistance on microorganism identification by pattern recognition.

AUTOR(ES)

Boyd, J C

RESUMO

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.

Documentos Relacionados

• Classification of cultured mammalian cells by shape analysis and pattern recognition.
• Pattern recognition analysis of in vivo enzyme-substrate fluorescence velocities in microorganism detection and identification.
• Wavelet-based techniques for adaptive feature extraction and pattern recognition.
• Effect of Surfactants on Antibiotic Resistance
• Effect of Culture Conditions on Microorganism Identification by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry