基于单室模型和仅谷浓度监测的贝叶斯浓度-时间曲线下面积的万古霉素群体药代动力学给药方案的性能。
Performance of Bayesian Area Under the Concentration-Time Curve-Based Pharmacokinetic Dosing Based on a One-Compartment Model and Trough-Only Monitoring for Vancomycin.
机构信息
Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, Arizona, USA.
Banner University Medical Center-Phoenix, Phoenix, Arizona, USA.
出版信息
Antimicrob Agents Chemother. 2023 Jun 15;67(6):e0017223. doi: 10.1128/aac.00172-23. Epub 2023 May 3.
A novel Bayesian method was developed to interpret serum vancomycin concentrations (SVCs) following the administration of one or more vancomycin doses with potential varying doses and intervals based on superposition principles. The method was evaluated using retrospective data from 442 subjects from three hospitals. The patients were required to receive vancomycin for more than 3 days, have stable renal function (fluctuation in serum creatinine of ≤0.3 mg/dL), and have at least 2 trough concentrations reported. Pharmacokinetic parameters were predicted using the first SVC, and the fitted parameters were then used to predict subsequent SVCs. Using only covariate-adjusted population prior estimates, the first two SVC prediction errors were 47.3 to 54.7% for the scaled mean absolute error (sMAE) and 62.1 to 67.8% for the scaled root mean squared error (sRMSE). "Scaled" refers to the division of the MAE or RMSE by the mean value. The Bayesian method had minimal errors for the first SVC (by design), and for the second SVC, the sMAE was 8.95%, and the sRMSE was 36.5%. The predictive performance of the Bayesian method did degrade with subsequent SVCs, which we attributed to time-dependent pharmacokinetics. The 24-h area under the concentration-time curve (AUC) was determined from simulated concentrations before and after the first SVC was reported. Prior to the first SVC, 170 (38.4%) patients had a 24-h AUC of <400 mg · h/L, 186 (42.1%) had a 24-h AUC within the target range, and 86 (19.5%) had a 24-h AUC of >600 mg · h/L. After the first SVC was reported, 322 (72.9%) had a 24-h AUC within the target range, 68 (15.4%) had low values, and 52 (11.8%) had high values based on the model simulation. Target attainments were 38% before the first SVC and 73% after the first SVC. The hospitals had no policies or procedures in place for targeting 24-h AUCs, although the trough target was typically 13 to 17 mg/L. Our data provide evidence of time-dependent pharmacokinetics, which will require regular therapeutic drug monitoring regardless of the method used to interpret SVCs.
一种新的贝叶斯方法被开发出来，用于解释单次或多次万古霉素给药后血清万古霉素浓度（SVC），这些剂量和间隔基于叠加原理，可能会有所不同。该方法使用来自 3 家医院的 442 名患者的回顾性数据进行了评估。患者需要接受万古霉素治疗超过 3 天，肾功能稳定（血清肌酐波动≤0.3mg/dL），至少报告 2 个谷浓度。使用第一个 SVC 预测药代动力学参数，然后使用拟合参数预测后续 SVC。仅使用协变量调整的群体先验估计，前两个 SVC 预测误差的比例平均绝对误差（sMAE）为 47.3%至 54.7%，比例均方根误差（sRMSE）为 62.1%至 67.8%。“比例”是指 MAE 或 RMSE 除以平均值。贝叶斯方法在前一个 SVC 上具有最小的误差（这是设计的），对于第二个 SVC，sMAE 为 8.95%，sRMSE 为 36.5%。贝叶斯方法的预测性能随着后续 SVC 而下降，我们将其归因于时间依赖性药代动力学。在报告第一个 SVC 之前和之后，从模拟浓度中确定 24 小时浓度-时间曲线下面积（AUC）。在第一个 SVC 之前，170 名（38.4%）患者的 24 小时 AUC ＜400mg·h/L，186 名（42.1%）患者的 24 小时 AUC 在目标范围内，86 名（19.5%）患者的 24 小时 AUC ＞600mg·h/L。在报告第一个 SVC 后，322 名（72.9%）患者的 24 小时 AUC 在目标范围内，68 名（15.4%）患者的 AUC 值较低，52 名（11.8%）患者的 AUC 值较高，这是基于模型模拟的。在第一个 SVC 之前，目标达成率为 38%，在第一个 SVC 之后，目标达成率为 73%。这些医院没有针对 24 小时 AUC 的目标设定政策或程序，尽管谷值目标通常为 13 至 17mg/L。我们的数据提供了时间依赖性药代动力学的证据，无论使用何种方法解释 SVC，都需要进行常规治疗药物监测。
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