Obara Shinju, Imaizumi Tsuyoshi, Hakozaki Takahiro, Hosono Atsuyuki, Iseki Yuzo, Sanbe Norie, Murakawa Masahiro
Surgical Operation Department, Fukushima Medical University Hospital, 1 Hikarigaoka, Fukushima, Fukushima, 960-1295, Japan.
Department of Intensive Care Medicine, Fukushima Medical University Hospital, 1 Hikarigaoka, Fukushima, Fukushima, 960-1295, Japan.
J Anesth. 2018 Feb;32(1):33-40. doi: 10.1007/s00540-017-2424-1. Epub 2017 Nov 2.
Little information is available on the predictive ability of previously published pharmacokinetic models of dexmedetomidine in patients under spinal anesthesia. We evaluated nine published pharmacokinetic models that were constructed in different study settings.
Sixteen patients received dexmedetomidine infusions after spinal anesthesia according to the manufacturer's recommended regimen (6 µg/kg/h over 10 min followed by 0.2-0.7 µg/kg/h) or target-controlled infusion (initial target of 1.5 ng/ml using the Dyck model). Dexmedetomidine concentrations were measured and median performance error (MDPE), median absolute performance error (MDAPE), and wobble were calculated.
A total of 84 blood samples were analyzed. The pharmacokinetic model reported by Hannivoort et al. had the greatest ability to predict dexmedetomidine concentrations (MDPE 5.6%, MDAPE 18.1%, and wobble 6.2%).
Hannivoort et al.'s pharmacokinetic model, constructed with a dataset obtained from healthy volunteers, can predict dexmedetomidine concentrations best during continuous infusion under spinal anesthesia.
关于先前发表的右美托咪定药代动力学模型对脊髓麻醉患者的预测能力，目前可用信息较少。我们评估了在不同研究背景下构建的九个已发表的药代动力学模型。
16例患者在脊髓麻醉后根据制造商推荐方案（10分钟内6μg/kg/h，随后0.2 - 0.7μg/kg/h）或靶控输注（使用Dyck模型初始靶浓度为1.5ng/ml）接受右美托咪定输注。测量右美托咪定浓度，并计算中位性能误差（MDPE）、中位绝对性能误差（MDAPE）和摆动度。
共分析了84份血样。Hannivoort等人报告的药代动力学模型预测右美托咪定浓度的能力最强（MDPE 5.6%，MDAPE 18.1%，摆动度6.2%）。
Hannivoort等人的药代动力学模型是基于从健康志愿者获得的数据集构建的，在脊髓麻醉下持续输注期间能最佳地预测右美托咪定浓度。
