开发参数和非参数模型,实现模型指导下的精确用药:肥胖症患者使用万古霉素的质量改进工作。

IF 2.8 4区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Therapeutic Drug Monitoring Pub Date : 2024-05-10 DOI:10.1097/FTD.0000000000001214
Maria-Stephanie A Hughes, Jasmine H Hughes, Jeffrey Endicott, Meagan Langton, John W Ahern, Ron J Keizer
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引用次数: 0

摘要

背景:目前已提出参数和非参数方法来支持模型信息精确配药(MIPD)。然而,哪种方法能产生更好的模型仍不确定。通过使用开源软件,我们使用万古霉素在具有挑战性的 3 类肥胖亚群中的药代动力学对这两种模型开发统计方法进行了比较:将 2021 年 11 月 1 日至 2023 年 2 月 14 日期间佛蒙特大学医疗中心使用万古霉素的患者输入 MIPD 软件。纳入标准为体重指数(BMI)至少为 40 kg/m2,万古霉素水平为 1 级或 1 级以上。使用 nlmixr2/NONMEM 创建了参数模型,使用度量创建了非参数模型。然后,使用归一化均方根误差(nRMSE)评估先验预测和后验预测的精确度,使用平均百分比误差(MPE)评估预测的偏差。在模拟 MIPD 的情况下,使用外部验证数据集对参数模型进行了评估:共有 83 名患者参与了模型开发,中位年龄为 56.6 岁(范围:24-89 岁),中位体重指数为 46.3 kg/m2(范围:40-70.3 kg/m2)。参数模型和非参数模型均为二室模型,肌酐清除率和去脂质量分别作为清除率和容积参数的协变量。参数模型与非参数模型的先验 MPE 和 nRMSE 分别为 -6.3% 对 2.69% 和 27.2% 对 30.7%。后验 MPE 和 RMSE 分别为 0.16% 和 0.84%,以及 13.8% 和 13.1%。在外部验证数据集(n = 576 名患者)上,参数模型与之前发表的模型相匹配或表现更优:结论:在建立万古霉素3类肥胖模型时,参数法和非参数法在模型结构和预测误差方面的差异很小。然而,参数模型的表现优于其他几个模型,这表明针对特定机构的模型可以改善药代动力学管理。
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Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity.

Background: Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity.

Methods: Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using metrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset.

Results: In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m2 (range: 40-70.3 kg/m2). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to c clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients).

Conclusions: Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

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来源期刊
Therapeutic Drug Monitoring
Therapeutic Drug Monitoring 医学-毒理学
CiteScore
5.00
自引率
8.00%
发文量
213
审稿时长
4-8 weeks
期刊介绍: Therapeutic Drug Monitoring is a peer-reviewed, multidisciplinary journal directed to an audience of pharmacologists, clinical chemists, laboratorians, pharmacists, drug researchers and toxicologists. It fosters the exchange of knowledge among the various disciplines–clinical pharmacology, pathology, toxicology, analytical chemistry–that share a common interest in Therapeutic Drug Monitoring. The journal presents studies detailing the various factors that affect the rate and extent drugs are absorbed, metabolized, and excreted. Regular features include review articles on specific classes of drugs, original articles, case reports, technical notes, and continuing education articles.
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