Time Above All Else: Pharmacodynamic Analysis of β-Lactams in Critically Ill Patients

Katie B. Landmesser, Justin A. Clark, David S. Burgess

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


β-Lactams are the most commonly used antibiotics in intensive care units (ICUs). As critically ill patients often experience pharmacokinetic aberrations, and rates of antimicrobial resistance vary between hospital settings, reliance on tertiary sources or package labeling to guide empiric dosing often results in suboptimal β-lactam exposure. The primary objective was to identify β-lactam regimens capable of achieving ≥90% cumulative fraction of response (CFR) against 7 Gram-negative pathogens within 4 ICUs at our institution. Unit-specific minimal inhibitory concentration (MIC) distribution data was used in combination with published pharmacokinetic parameters in critically ill patients to perform Monte Carlo simulations. The percentage of time for which the unbound concentration of antibiotic remained above the MIC (%ƒT > MIC) was used as the pharmacodynamic target: 70%ƒT >MIC for cefepime, 40%ƒT > MIC for meropenem, and 50%ƒT > MIC for piperacillin/tazobactam. Regimens were modeled to determine the likelihood of achieving ≥90% CFR. Overall, intermittently dosed cefepime, meropenem, and piperacillin/tazobactam failed to achieve ≥90% CFR for every organism. Cefepime 2 g intermittent bolus every 8 hours failed to achieve ≥90% CFR for Klebsiella pneumoniae or Enterobacter cloacae despite susceptibility rates exceeding 90%. Piperacillin/tazobactam 4.5 g prolonged infusion (PI) every 6 hours achieved <85% CFR for Pseudomonas aeruginosa and <50% CFR for Acinetobacter baumannii in every ICU. Meropenem 2 g PI every 8 hours and meropenem 2 g PI every 6 hours were the only regimens capable of achieving ≥90% CFR for P aeruginosa in all units. Use of Monte Carlo simulations, with incorporation of local MIC distribution data, provides a mechanism to effectively predict optimal agent and dose selection within specific hospital systems, thereby enhancing pharmacokinetic/pharmacodynamic optimization and improving clinical efficacy.

Original languageEnglish
Pages (from-to)479-485
Number of pages7
JournalJournal of Clinical Pharmacology
Issue number4
StatePublished - Apr 2022

Bibliographical note

Publisher Copyright:
© 2021, The American College of Clinical Pharmacology


  • Gram-negative resistance
  • Monte Carlo simulations
  • pharmacodynamics
  • pharmacokinetics

ASJC Scopus subject areas

  • Pharmacology
  • Pharmacology (medical)


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