以子组为基础的模型选择,以改善康明度的预测
Hanna Kadri Laas1,2, Tuuli Metsvaht1,3,4, Kadri Tamme2,5
1Department of Microbiology, University of Tartu, Tartu, Estonia.
一个新的模型选择工具 (MST) 提高了万科米辛剂量准确性,并简化了模型选择以实现基于模型的精确剂量 (MIPD). 这提高了治疗药物监测和患者的治疗结果.
科学领域:
- 药理动力学和药理动力学
- 临床药房 临床药房
- 计算生物学 计算生物学
背景情况:
- 个性化的万科米辛剂量对于疗效和安全至关重要.
- 基于模型的精确剂量 (MIPD) 是首选的,但在模型选择和初始剂量确定方面面临挑战.
- 准确的万科米辛度预测对于优化患者治疗至关重要.
研究的目的:
- 开发和评估一种用于范胺剂量的模型选择工具 (MST).
- 评估MST提高度预测精度和减少偏差的能力,与普遍表现最佳模型 (UBM) 相比.
- 确定最佳数量的先前度,以准确预测万科米辛水平.
主要方法:
- 对成年ICU患者的万科米辛治疗数据的回顾性分析.
- 使用遗传算法开发一种MST.
- 使用培训和验证数据集,将MST性能与UBM进行比较.
- 基于以前的万科米辛度的预测准确性的评估.
主要成果:
- 与UBM相比,MST的精度提高了,在训练 (22.8%与26.0%) 和验证 (28.4%与30.2%) 数据集中,平均绝对百分比预测误差 (PAPE) 较低.
- 统统测量显示偏差较低,预测误差 (PPE) 的平均百分比为5.8% (培训) 和-2.8% (验证).
- 使用两次先前测量度预测第三个万科米辛度获得了最高的准确性 (平均PAPE培训17.0%,验证18.9%).
结论:
- 开发的MST可以提高从初始剂量开始的万科米辛剂量准确性.
- MST简化了模型选择过程,促进了MIPD的更广泛采用.
- 改进的万科米辛剂量策略可以带来更好的患者结果和减少毒性.
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