The author declares no conflicts of interest.
Interpreting culture and susceptibility data in critical care: perks and pitfalls
Article first published online: 8 FEB 2010
© Veterinary Emergency and Critical Care Society 2010
Journal of Veterinary Emergency and Critical Care
Volume 20, Issue 1, pages 110–131, February 2010
How to Cite
Boothe, D. M. (2010), Interpreting culture and susceptibility data in critical care: perks and pitfalls. Journal of Veterinary Emergency and Critical Care, 20: 110–131. doi: 10.1111/j.1476-4431.2009.00509.x
- Issue published online: 8 FEB 2010
- Article first published online: 8 FEB 2010
- antimicrobial resistance;
- design of dosing regimen
Problem – The need for immediate, effective antimicrobial therapy in the critical care patient must be tempered by approaches which simultaneously minimize emergence of antimicrobial resistance. Ideally, therapy will successfully resolve clinical signs of infection, while eradicating infecting pathogens such that the risk of resistance is avoided. Increasing limitations associated with empirical antimicrobial choices direct the need for culture and susceptibility data as a basis of therapy. Even so, such in vitro data should be utilized within its limitations.
Objectives – To demonstrate the attributes and limitations of patient and population culture and susceptibility (pharmacodynamic) data in the selection of antimicrobial drugs and to demonstrate the design of individualized dosing regimens based on integration of pharmacodynamic (PD) and pharmacokinetic (PK) data.
Diagnosis – Limitations in culture and susceptibility testing begin with sample collection and continue through drug selection and dose design. Among the challenges in interpretation is discrimination between pathogens and commensals. Properly collected samples are critical for generation of data relevant to the patient's infection. Data are presented as minimum inhibitory concentrations (MICs). The MIC facilitate selection of the most appropriate drug, particularly when considered in the context of antimicrobial concentrations achieved in the patient at a chosen dose. Integration of MIC data with key PK data yields the Cmax:MIC important to efficacy of concentration-dependent drugs and T>MIC, which guides use of time-dependent drugs. These indices are then used to design dosing regimens that are more likely to kill all infecting pathogens. In the absence of patient MIC data, population data (eg, MIC90) may serve as a reasonable surrogate.
Conclusions – Properly collected, performed, and interpreted culture and susceptibility data are increasingly important in the selection of and design of dosing regimens for antimicrobial drugs. Integration of PK and PD data as modified by host and microbial factors supports a hit hard, exit fast approach to therapy that will facilitate efficacy while minimizing resistance.