Department of Laboratory Medicine, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan, Korea.
Department of Laboratory Medicine, Ewha Womans University College of Medicine, Seoul, Korea.
Ann Lab Med. 2023 Nov 1;43(6):554-564. doi: 10.3343/alm.2023.43.6.554. Epub 2023 Jun 30.
The revised U.S. consensus guidelines on vancomycin therapeutic drug monitoring (TDM) recommend obtaining trough and peak samples to estimate the area under the concentration-time curve (AUC) using the Bayesian approach; however, the benefit of such two-point measurements has not been demonstrated in a clinical setting. We evaluated Bayesian predictive performance with and without peak concentration data using clinical TDM data.
We retrospectively analyzed 54 adult patients without renal impairment who had two serial peak and trough concentration measurements in a ≤1-week interval. The concentration and AUC values were estimated and predicted using Bayesian software (MwPharm++; Mediware, Prague, Czech Republic). The median prediction error (MDPE) for bias and median absolute prediction error (MDAPE) for imprecision were calculated based on the estimated AUC and measured trough concentration.
AUC predictions using the trough concentration had an MDPE of -1.6% and an MDAPE of 12.4%, whereas those using both peak and trough concentrations had an MDPE of -6.2% and an MDAPE of 16.9%. Trough concentration predictions using the trough concentration had an MDPE of -8.7% and an MDAPE of 18.0%, whereas those using peak and trough concentrations had an MDPE of -13.2% and an MDAPE of 21.0%.
The usefulness of the peak concentration for predicting the AUC on the next occasion by Bayesian modeling was not demonstrated; therefore, the practical value of peak sampling for AUC-guided dosing can be questioned. As this study was conducted in a specific setting and generalization is limited, results should be interpreted cautiously.
修订后的美国万古霉素治疗药物监测（TDM）共识指南建议采用贝叶斯法获取谷值和峰值样本，以估算浓度-时间曲线下面积（AUC）；然而，这种两点测量方法在临床环境中的益处尚未得到证实。我们使用临床 TDM 数据评估了有无峰浓度数据的贝叶斯预测性能。
我们回顾性分析了 54 例无肾功能损害的成年患者，这些患者在≤1 周的时间内进行了两次连续的峰谷浓度测量。使用贝叶斯软件（MwPharm++；Mediware，捷克布拉格）估算和预测浓度和 AUC 值。基于估算的 AUC 和测量的谷浓度，计算偏倚的中位数预测误差（MDPE）和精度的中位数绝对预测误差（MDAPE）。
仅使用谷浓度进行 AUC 预测的 MDPE 为-1.6%，MDAPE 为 12.4%，而同时使用峰谷浓度进行 AUC 预测的 MDPE 为-6.2%，MDAPE 为 16.9%。仅使用谷浓度进行谷浓度预测的 MDPE 为-8.7%，MDAPE 为 18.0%，而同时使用峰谷浓度进行谷浓度预测的 MDPE 为-13.2%，MDAPE 为 21.0%。
贝叶斯建模中，峰浓度对预测下一次 AUC 的有用性未得到证实；因此，峰采样对 AUC 指导剂量的实际价值值得怀疑。由于本研究是在特定环境中进行的，推广受限，因此结果应谨慎解释。
