Background and Objective

Piperacillin is a broad-spectrum β-lactam antibiotic commonly prescribed in intensive care units. Many piperacillin population pharmacokinetic models have been published, but few underwent an external evaluation. External evaluation is an important process to determine a model’s capability of being generalized to other hospitals. We aimed to assess the predictive performance of these models with an external validation dataset.

Methods

Six models were evaluated with a dataset consisting of 30 critically ill patients (35 samples) receiving piperacillin by continuous infusion. Models were subject to prediction-based (bias and imprecision) and simulation-based evaluations. When a model had an acceptable evaluation, it was used for dosing simulations to evaluate the probability of target attainment.

Results

Bias and imprecision ranged from − 35.7 to 295% and from 22.7 to 295%, respectively. The models of Klastrup et al. and of Udy et al. were acceptable according to our criteria and were used for dosing simulations. Simulations showed that a loading dose of 4 g followed by a maintenance dose of 16 g/24 h of piperacillin infused continuously was necessary to remain above a pharmacokinetic-pharmacodynamic target set as a minimal inhibitory concentration of 16 mg/L in 90% of patients, for a median patient with a creatinine clearance of 76 mL/min.

Conclusions

Despite the considerable variation in the predictive performance of the models with the external validation dataset, this study was able to validate two of these models and led to the elaboration of a dosing nomogram for piperacillin by continuous infusion that can be used by clinicians in intensive care units.

中文翻译：

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使用经过验证的群体药代动力学模型为危重成年患者提供持续输注哌拉西林的剂量建议

背景和目的

哌拉西林是重症监护室常用的广谱 β-内酰胺抗生素。许多哌拉西林群体药代动力学模型已发表，但很少经过外部评估。外部评价是确定模型能否推广到其他医院的重要过程。我们的目的是使用外部验证数据集评估这些模型的预测性能。

方法

使用由 30 名接受连续输注哌拉西林的重症患者（35 个样本）组成的数据集对 6 个模型进行了评估。模型接受基于预测（偏差和不精确）和基于模拟的评估。当模型获得可接受的评估时，将其用于剂量模拟以评估达到目标的概率。

结果

偏差和不精确度分别为 - 35.7% 至 295% 和 22.7% 至 295%。Klastrup 等人的模型。和乌迪等人。根据我们的标准是可以接受的，并用于剂量模拟。模拟显示，需要先持续输注 4 g 的负荷剂量，然后持续输注 16 g/24 小时的哌拉西林维持剂量，才能在 90% 的患者中保持高于设定的药代动力学-药效学目标（即 16 mg/L 的最小抑制浓度） ，对于肌酐清除率中位数为 76 mL/min 的患者。

结论

尽管模型的预测性能与外部验证数据集存在很大差异，但本研究能够验证其中两个模型，并详细阐述了连续输注哌拉西林的剂量列线图，可供临床医生在重症监护中使用单位。