多模型方法的预测性能,用于在重症患者中以模型为基础的精确剂量定量皮佩拉西林
Lea Marie Schatz1, Sebastian Greppmair1, Alexandra K Kunzelmann1
1Department of Anaesthesiology, LMU University Hospital, LMU Munich, Munich, Germany.
模型平均算法 (MAA) 改善了对piperacillin/tazobactam的模型信息精确剂量 (MIPD),提高了对抗生素暴露的优化. 在24小时内整合第二个TDM样本可以最大限度地实现目标,特别是在重症患者中.
科学领域:
- 药理学 药理学是指药理学的学科.
- 临床药房 临床药房
- 制药指标 (Pharmacometrics) 是一个指标.
背景情况:
- 皮佩拉西林/塔扎巴克坦的剂量需要优化,以防止毒性和耐药性.
- 基于模型的精确剂量 (MIPD) 显示出改善抗生素目标实现的前景.
- 之前的评估表明,MIPD可以提高治疗结果.
研究的目的:
- 为了比较不同MIPD方法的预测性能:单模型,模型选择算法 (MSA) 和模型平均算法 (MAA).
- 评估一个 (B1) 与两个 (B2) 治疗药物监测 (TDM) 样本对MIPD准确性和精度的影响.
- 评估MIPD在多中心环境中的piperacillin (PIP) 剂量策略.
主要方法:
- 使用了561名患者和3654个TDM样本的多中心数据集.
- 预测性能的评估是基于不准确性,不准确性和预期的目标实现.
- 三种MIPD方法 (单一模型,MSA,MAA) 使用候选PIP模型进行了比较.
主要成果:
- 在一个TDM样本 (B1) 中,MAA在MSA和单个模型中表现出优异的预测性能 (B1).
- 不准确性:MAA (±3%) <单个模型 (±8%)
77%) >单个模型 (>73%) >MSA (>71%). - 第二个TDM样本显著提高了所有方法的精度和目标实现,在24小时内集成时最大的实现 (>90%).
结论:
- 通过减少选择次优模型的风险,MAA简化了MIPD.
- 使用MAA的PIP的MIPD优化了重症患者对抗生素的暴露.
- MAA提高了MIPD的预测性能,安全性和可用性,特别是在单一的TDM样本.
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