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Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.
Pharmaceutical Research ( IF 3.7 ) Pub Date : 2020-08-23 , DOI: 10.1007/s11095-020-02908-7
Tingjie Guo 1, 2, 3 , Reinier M van Hest 2 , Laura B Zwep 3, 4 , Luca F Roggeveen 1 , Lucas M Fleuren 1 , Rob J Bosman 5 , Peter H J van der Voort 5 , Armand R J Girbes 1 , Ron A A Mathot 2 , Paul W G Elbers 1 , Johan G C van Hasselt 3
Affiliation  

Purpose

Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for model-based TDM.

Methods

We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days.

Results

The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were − 7.7%, −4.5% and − 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition, regardless of MAP method, including historical TDM data further in the past will increase prediction errors.

Conclusions

The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance.


中文翻译:

优化重症监护患者万古霉素浓度贝叶斯预测的预测性能。

目的

贝叶斯预测对于基于重症监护 (ICU) 患者万古霉素治疗药物监测 (TDM) 数据的基于模型的剂量优化至关重要。我们的目的是使用基于模型的 TDM 的最大后验 (MAP) 估计来评估贝叶斯预测的性能。

方法

我们使用万古霉素 TDM 数据集(n  = 408 名患者)。我们将基于 MAP 的标准贝叶斯预测与两种替代方法进行了比较:(i)通过多次迭代处理数据的自适应 MAP,以及(ii)对数据的似然贡献进行加权的加权 MAP。我们评估了七种场景的百分比误差 (PE),包括前一天至 7 天的历史 TDM 数据。

结果

标准 MAP、自适应 MAP 和加权 MAP 方法的所有场景的中位 PE 平均值分别为 - 7.7%、-4.5% 和 - 6.7%。自适应 MAP 还显示 PE 的四分位数范围最窄。此外,无论采用哪种MAP方法,进一步包含过去的历史TDM数据都会增加预测误差。

结论

所提出的自适应 MAP 方法在预测性能方面优于标准 MAP,并且可以考虑用于改进基于模型的剂量优化。包含超过一天(标准 MAP 和加权 MAP)或两天(自适应 MAP)的历史数据会降低预测性能。
更新日期:2020-08-23
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