Utilizing Monte Carlo Simulations to Optimize Institutional Empiric Antipseudomonal Therapy
Abstract
:1. Introduction
2. Methods
2.1. Data Collection
2.2. Model Construction
3. Results
Breakpoint a (mcg/mL) | MIC Range (mcg/mL) | MIC50 (mcg/mL) | MIC90 (mcg/mL) | % Susceptible | |
---|---|---|---|---|---|
Aztreonam | 8 | ≤2–≥32 | 8 | 32 | 68 |
Cefepime | 8 | ≤1–≥32 | 4 | 16 | 81 |
Meropenem | 2 | ≤1–≥16 | 1 | 8 | 74 |
Piperacillin | 16 | ≤2–≥128 | 8 | 128 | 75 |
Antimicrobial Agent | Clearance (L/h) | Volume of Distribution (L) | Half Life (h) | Protein Binding (%) |
---|---|---|---|---|
Aztreonam [16,17] | 5.45 ± 1.24 | 13.7 ± 4.94 | 1.69 ± 0.43 | 56 |
Cefepime [18,19] | 8.58 ± 1.5 | 18.4 ± 3.8 | 2.32 ± 0.39 | 20 |
Meropenem [15,20] | 11.28 ± 1.86 | 12.5 ± 1.5 | 0.98 | 2 |
Piperacillin [21,22] | 11.07 ± 2.59 | 11.2 ± 2.1 | 0.7 ± 0.11 | 30 |
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Tennant, S.J.; Burgess, D.R.; Rybak, J.M.; Martin, C.A.; Burgess, D.S. Utilizing Monte Carlo Simulations to Optimize Institutional Empiric Antipseudomonal Therapy. Antibiotics 2015, 4, 643-652. https://doi.org/10.3390/antibiotics4040643
Tennant SJ, Burgess DR, Rybak JM, Martin CA, Burgess DS. Utilizing Monte Carlo Simulations to Optimize Institutional Empiric Antipseudomonal Therapy. Antibiotics. 2015; 4(4):643-652. https://doi.org/10.3390/antibiotics4040643
Chicago/Turabian StyleTennant, Sarah J., Donna R. Burgess, Jeffrey M. Rybak, Craig A. Martin, and David S. Burgess. 2015. "Utilizing Monte Carlo Simulations to Optimize Institutional Empiric Antipseudomonal Therapy" Antibiotics 4, no. 4: 643-652. https://doi.org/10.3390/antibiotics4040643