Laboratory of Medical Biochemistry and Clinical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
J Antimicrob Chemother. 2016 Sep;71(9):2502-8. doi: 10.1093/jac/dkw150. Epub 2016 May 4.
In the field of antimicrobial chemotherapy, readers are increasingly confronted with population pharmacokinetic models and the ensuing simulation results with the purpose to improve the efficiency of currently used therapeutic regimens. One such type of analysis is Monte Carlo (MC) simulations in support of dose selection. At the moment, results of these MC simulations consist of predictions for the typical individual/population only. The uncertainty associated with the parameters, from which the simulations are derived, is completely ignored. Here, we highlight the importance of and the need to include parameter uncertainty in PTA simulations.
Using MC simulation with parameter uncertainty, we estimated CIs around PTA curves. The added benefit of this approach was illustrated using, on the one hand, a population pharmacokinetic model developed in-house for a β-lactam antibiotic and, on the other hand, results from a previously published PTA analysis.
Our examples illustrate that proper clinical decision-making requires more than the typical PTA curve. Therefore, authors should be encouraged to provide an estimate of the uncertainty along with their simulations and to take this into account when interpreting the results. We feel that CIs around PTA curves provide this information in a comprehensive manner without requiring advanced knowledge on the underlying modelling approaches from the reader.
We believe that this approach should be advocated by all stakeholders in antibiotic stewardship programmes to safeguard the quality of clinical decision-making in the future.
在抗菌药物化疗领域，读者越来越多地面对群体药代动力学模型和随之而来的模拟结果，旨在提高当前治疗方案的效率。其中一种分析类型是支持剂量选择的蒙特卡罗（MC）模拟。目前，这些 MC 模拟的结果仅包含对典型个体/群体的预测。完全忽略了模拟所依据的参数的不确定性。在这里，我们强调了在预测治疗窗口（PTA）模拟中包含参数不确定性的重要性和必要性。
使用具有参数不确定性的 MC 模拟，我们估计了 PTA 曲线周围的置信区间（CI）。一方面，我们使用内部开发的β-内酰胺类抗生素群体药代动力学模型，另一方面使用先前发表的 PTA 分析结果，说明了这种方法的附加好处。
我们的例子表明，正确的临床决策需要的不仅仅是典型的 PTA 曲线。因此，作者应该被鼓励提供模拟结果的不确定性估计，并在解释结果时考虑到这一点。我们认为，PTA 曲线周围的 CI 以全面的方式提供了这方面的信息，而不需要读者具备底层建模方法的相关知识。
我们相信，所有参与抗生素管理计划的利益相关者都应该倡导这种方法，以确保未来临床决策的质量。
