Downes Kevin J, Sharova Anna, Malone Judith, Odom John Audrey R, Zuppa Athena F, Neely Michael N
Center for Clinical Pharmacology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Clinical Futures, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Ther Drug Monit. 2025 Jan 23;47(4):512-519. doi: 10.1097/FTD.0000000000001293.
Area-under-the-curve (AUC)-directed vancomycin therapy is recommended; however, AUC estimation in critically ill children is difficult owing to the need for multiple samples and lack of informative models.
The authors prospectively enrolled critically ill children receiving intravenous (IV) vancomycin for suspected infection and evaluated the accuracy of Bayesian estimation of AUC from a single, optimally timed sample. During the dosing interval, when clinical therapeutic drug monitoring was performed, an optimally timed sample was collected, which was determined for each subject using an established population pharmacokinetic model and the multiple model optimal function of Pmetrics, a nonparametric population pharmacokinetic modeling software. The model was embedded in InsightRx NOVA (InsightRx, Inc.) for individual Bayesian estimation of AUC using the optimal sample versus all available samples (optimally timed sample + clinical samples).
Eighteen children were included. The optimal sampling time to inform Bayesian estimation of vancomycin AUC was highly variable, with trough samples being optimally informative in 32% of children. Optimal samples were collected by clinical nurses within 15 minutes of the goal time in 14 of 18 participants (78%). Compared with all samples, Bayesian AUC estimation with optimal samples had a mean bias of 0.4% (±5.9%) and mean imprecision of 4.6% (±3.6%). Bias of optimal sampling was <10% for 17 of the 18 participants (94%). When estimating AUC using only a peak sample (≤2 hours after dose) or only a trough (≤30 minutes before next dose), bias was <10% for 78% and 86% of participants, respectively.
Optimal sampling supports accurate Bayesian estimation of vancomycin AUC from a single plasma sample in critically ill children.
推荐采用曲线下面积（AUC）指导万古霉素治疗；然而，由于需要采集多个样本且缺乏有效模型，在重症儿童中估算AUC较为困难。
作者前瞻性纳入因疑似感染接受静脉注射万古霉素的重症儿童，评估根据单次最佳采样时间样本进行贝叶斯法估算AUC的准确性。在给药间隔期间，当进行临床治疗药物监测时，采集一个最佳采样时间样本，使用已建立的群体药代动力学模型和非参数群体药代动力学建模软件Pmetrics的多模型优化功能为每个受试者确定该样本。该模型嵌入InsightRx NOVA（InsightRx公司）中，用于根据最佳样本与所有可用样本（最佳采样时间样本 + 临床样本）对AUC进行个体贝叶斯估算。
纳入18名儿童。用于指导贝叶斯法估算万古霉素AUC的最佳采样时间差异很大，32%的儿童中谷浓度样本提供的信息最丰富。18名参与者中有14名（78%）的临床护士在目标时间的15分钟内采集到了最佳样本。与所有样本相比，使用最佳样本进行贝叶斯AUC估算的平均偏差为0.4%（±5.9%），平均不精密度为4.6%（±3.6%）。18名参与者中有17名（94%）的最佳采样偏差<10%。仅使用峰浓度样本（给药后≤2小时）或仅使用谷浓度样本（下次给药前≤30分钟）估算AUC时，分别有78%和86%的参与者偏差<10%。
最佳采样有助于从重症儿童的单个血浆样本中准确地进行贝叶斯法估算万古霉素AUC。
