Burns, Trauma and Critical Care Research Centre, The University of Queensland, Brisbane, Australia.
Curr Pharm Biotechnol. 2011 Dec;12(12):2070-9. doi: 10.2174/138920111798808329.
Antibiotic prescription for critically ill patients is a complicated process because of the pharmacokinetic differences of this patient population with non-critically ill patients and the lack of robust informative studies. This article seeks to review the available literature describing dosing requirements for optimized treatment of critically ill patients and to discuss a framework to rationally address complex cases by outlining the suggested processes for optimal achievement of pharmacokinetic/ pharmacodynamic targets. A variety of papers exist describing the effect of pathophysiology on antibiotic kinetics. In the critically ill patient, dysfunction of almost any organ system can result in significant changes to drug volume of distribution and clearance. Dysfunction of the cardiovascular and renal systems in particular is problematic and can lead to potentially sub-therapeutic antibiotic concentrations in blood and in interstitial fluid. In response to altered pharmacokinetics, dose regimens that adhere to the pharmacodynamics of the antibiotic are essential. In the absence of validated dosing algorithms, therapeutic drug monitoring data and susceptibility data of the infecting pathogen should be inputted into a Bayesian software program that include population pharmacokinetic models to calculate dosing regimens that are personalized for the critically ill patient.
危重症患者的抗生素处方是一个复杂的过程，因为这类患者人群与非危重症患者的药代动力学存在差异，而且缺乏强有力的信息研究。本文旨在回顾现有文献，描述为优化治疗危重症患者所需的剂量要求，并通过概述实现药代动力学/药效学目标的建议流程，讨论合理解决复杂病例的框架。有大量描述病理生理学对抗生素动力学影响的文献。在危重症患者中，几乎任何器官系统的功能障碍都可能导致药物分布容积和清除率发生显著变化。心血管和肾脏系统的功能障碍尤其成问题，可能导致血液和间质液中的抗生素浓度潜在低于治疗水平。为了应对药代动力学的改变，必须遵循抗生素药效学的剂量方案。在没有经过验证的剂量算法的情况下，应将治疗药物监测数据和感染病原体的药敏数据输入到贝叶斯软件程序中，该程序包括群体药代动力学模型，以计算针对危重症患者的个体化剂量方案。
