哌拉西林(PIP)/他唑巴坦是一种常用的抗生素;然而,过量或不足可能导致毒性,治疗失败,以及耐药性的发展。对24个已发表的PIP模型的外部评估表明,基于模型的精确给药(MIPD)可以提高目标实现。采用各种候选模型,本研究旨在评估不同MIPD方法的预测性能,比较(I)单模型方法,(ii)模型选择算法(MSA)和(iii)模型平均算法(MAA)。Precision,准确性和预期目标达成,考虑每个患者的初始(B1)或初始和次要(B2)治疗药物监测(TDM)样本,在多中心数据集中进行评估(561例患者,11个德国中心,3654个TDM样本)。结果表明,在B1中使用MAA的预测性能略有优势,无论候选模型如何,与MSA和最佳单一型号(MAA,MSA,单一模型:不准确±3%,±10%,±8%;不精确:<25%,<31%,<28%;预期目标实现>77%,>71%,>73%)。包含第二个TDM样本显着提高了所有MIPD方法的精度和目标实现,特别是在MSA和大多数单一模型的背景下。当在24小时内整合TDM样品时,预期的目标达到最大化(高达>90%)。总之,MAA通过降低为特定患者选择不适当模型的风险来简化MIPD。因此,使用MAA的PIP的MIPD涉及在危重患者中抗生素暴露的进一步优化,通过提高只有一个样本可用于贝叶斯预测的预测性能,安全,和临床实践中的可用性。
Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA). Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicenter dataset (561 patients, 11 German centers, 3654 TDM-samples). The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, single models: inaccuracy ±3%, ±10%, ±8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 hours. In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimization of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice.