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Microbiological tests to predict treatment outcome in experimental device-related infections due to Staphylococcus aureus
Abstract
Treatment outcome of experimental device-related infections cannot be predicted by the results of standard susceptibility tests such as MIC. Microorganisms involved in such infections have a slow growth rate and adhere to surfaces. Therefore, laboratory tests were developed taking into account these properties and compared with the treatment outcome in an animal model. Vancomycin, teicoplanin, ciprofloxacin and fleroxacin were tested alone, or in combination with rifampicin for their ability to cure experimental device-related infections in guinea pigs due to Staphylococcus aureus ATCC 29213. Rifampicin alone or in combination was significantly more effective than the other four drugs (P < 0.001). Combined treatment with rifampicin had a higher cure rate than rifampicin alone. Treatment success was not predicted by an antibiotic trough level exceeding the MIC at site of infection. In contrast, drug efficacy was predicted if the stationary-phase MBC was in the sensitive range, and if glass-adherent S. aureus was killed by low drug concentrations.
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Cited by
Rex, J. H., Pfaller, M. A., Galgiani, J. N., Bartlett, M. S., Espinel-Ingroff, A., Ghannoum, M. A., Lancaster, M., Odds, F. C., Rinaldi, M. G., Walsh, T. J., et al. (1997) · Clinical Infectious Diseases · doi:10.1093/clinids/24.2.235 · Cited by 855
Metadata
- Venue
- Journal of Antimicrobial Chemotherapy
- Publisher
- Oxford University Press (OUP)
- Published
- 1994
- Volume
- 33
- Issue
- 5
- Pages
- 959-967
- ISSN
- 0305-7453 (ISSN-L)
- 1460-2091
- OpenAlex
- W87013843
- Cited by
- 192
- References
- 0
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- Microbiological tests to predict treatment outcome in experimental device-related infections due to Staphylococcus aureus. (1994). Journal of Antimicrobial Chemotherapy. https://doi.org/10.1093/jac/33.5.959
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.