Establishment of a new initial dose plan for vancomycin using the generalized linear mixed model.

Q1 Mathematics
Yasuyuki Kourogi, Kenji Ogata, Norito Takamura, Jin Tokunaga, Nao Setoguchi, Mitsuhiro Kai, Emi Tanaka, Susumu Chiyotanda
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引用次数: 4

Abstract

Background: When administering vancomycin hydrochloride (VCM), the initial dose is adjusted to ensure that the steady-state trough value (Css-trough) remains within the effective concentration range. However, the Css-trough (population mean method predicted value [PMMPV]) calculated using the population mean method (PMM) often deviate from the effective concentration range. In this study, we used the generalized linear mixed model (GLMM) for initial dose planning to create a model that accurately predicts Css-trough, and subsequently assessed its prediction accuracy.

Methods: The study included 46 subjects whose trough values were measured after receiving VCM. We calculated the Css-trough (Bayesian estimate predicted value [BEPV]) from the Bayesian estimates of trough values. Using the patients' medical data, we created models that predict the BEPV and selected the model with minimum information criterion (GLMM best model). We then calculated the Css-trough (GLMMPV) from the GLMM best model and compared the BEPV correlation with GLMMPV and with PMMPV.

Results: The GLMM best model was {[0.977 + (males: 0.029 or females: -0.081)] × PMMPV + 0.101 × BUN/adjusted SCr - 12.899 × SCr adjusted amount}. The coefficients of determination for BEPV/GLMMPV and BEPV/PMMPV were 0.623 and 0.513, respectively.

Conclusion: We demonstrated that the GLMM best model was more accurate in predicting the Css-trough than the PMM.

Abstract Image

Abstract Image

Abstract Image

应用广义线性混合模型建立万古霉素新的初始剂量计划。
背景:在给药盐酸万古霉素(VCM)时,需要调整初始剂量以确保稳定波谷值(css -波谷)保持在有效浓度范围内。然而,使用总体平均数法(PMM)计算的css -波谷(总体平均数法预测值[PMMPV])往往偏离有效浓度范围。在本研究中,我们使用广义线性混合模型(GLMM)进行初始剂量规划,建立了一个准确预测css -谷的模型,并随后评估了其预测精度。方法:对46例接受VCM治疗的患者进行波谷值测定。我们从槽值的贝叶斯估计中计算出css -波谷(贝叶斯估计预测值[BEPV])。利用患者的医疗数据,建立了BEPV预测模型,并选择了最小信息准则模型(GLMM最佳模型)。然后,我们根据GLMM最佳模型计算了css -槽(GLMMPV),并比较了BEPV与GLMMPV和PMMPV的相关性。结果:GLMM最佳模型为{[0.977 +(男:0.029或女:-0.081)]× PMMPV + 0.101 × BUN/调整SCr - 12.899 × SCr调整量}。BEPV/GLMMPV和BEPV/PMMPV的决定系数分别为0.623和0.513。结论:GLMM最佳模型比PMM更能准确预测css -谷。
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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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