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建立吸入麻醉药吸入经验模型的数学方法。

Mathematical method to build an empirical model for inhaled anesthetic agent wash-in.

机构信息

Department of Anesthesiology, Intensive Care and Pain Therapy, Onze Lieve Vrouwziekenhuis, Aalst, Belgium.

出版信息

BMC Anesthesiol. 2011 Jun 24;11:13. doi: 10.1186/1471-2253-11-13.

Abstract

BACKGROUND

The wide range of fresh gas flow - vaporizer setting (FGF - FD) combinations used by different anesthesiologists during the wash-in period of inhaled anesthetics indicates that the selection of FGF and FD is based on habit and personal experience. An empirical model could rationalize FGF - FD selection during wash-in.

METHODS

During model derivation, 50 ASA PS I-II patients received desflurane in O2 with an ADU® anesthesia machine with a random combination of a fixed FGF - FD setting. The resulting course of the end-expired desflurane concentration (FA) was modeled with Excel Solver, with patient age, height, and weight as covariates; NONMEM was used to check for parsimony. The resulting equation was solved for FD, and prospectively tested by having the formula calculate FD to be used by the anesthesiologist after randomly selecting a FGF, a target FA (FAt), and a specified time interval (1 - 5 min) after turning on the vaporizer after which FAt had to be reached. The following targets were tested: desflurane FAt 3.5% after 3.5 min (n = 40), 5% after 5 min (n = 37), and 6% after 4.5 min (n = 37).

RESULTS

Solving the equation derived during model development for FD yields FD=-(e(-FGF*-0.23+FGF0.24)(e(FGF*-0.23)FAtHt0.1-e(FGF-0.23)FGF2.55+40.46-e(FGF*-0.23)40.46+e(FGF-0.23+Time/-4.08)40.46-e(Time/-4.08)40.46))/((-1+e(FGF0.24))(-1+e(Time/-4.08))*39.29). Only height (Ht) could be withheld as a significant covariate. Median performance error and median absolute performance error were -2.9 and 7.0% in the 3.5% after 3.5 min group, -3.4 and 11.4% in the 5% after 5 min group, and -16.2 and 16.2% in the 6% after 4.5 min groups, respectively.

CONCLUSIONS

An empirical model can be used to predict the FGF - FD combinations that attain a target end-expired anesthetic agent concentration with clinically acceptable accuracy within the first 5 min of the start of administration. The sequences are easily calculated in an Excel file and simple to use (one fixed FGF - FD setting), and will minimize agent consumption and reduce pollution by allowing to determine the lowest possible FGF that can be used. Different anesthesia machines will likely have different equations for different agents.

摘要

背景

不同麻醉医师在吸入麻醉药洗入期使用的新鲜气体流量-蒸发器设定(FGF-FD)组合范围很广,这表明 FGF 和 FD 的选择基于习惯和个人经验。经验模型可以使洗入期的 FGF-FD 选择合理化。

方法

在模型推导过程中,50 名 ASA PS I-II 患者在 O2 中接受地氟醚,使用 ADU®麻醉机,FD 设置为固定 FGF-FD 组合的随机组合。使用 Excel Solver 对终末呼气地氟醚浓度(FA)的变化过程进行建模,患者年龄、身高和体重作为协变量;使用 NONMEM 检查简约性。根据方程计算 FD,并通过让公式在随机选择 FGF、目标 FA(FAt)和打开蒸发器后指定的时间间隔(1-5 分钟)后计算 FD,前瞻性地测试该公式。测试了以下目标:3.5 分钟后达到 3.5%地氟醚 FAt(n=40)、5 分钟后达到 5%(n=37)和 4.5 分钟后达到 6%(n=37)。

结果

针对在模型开发期间推导的方程求解 FD 得出 FD=-(e(-FGF*-0.23+FGF0.24)(e(FGF*-0.23)FAtHt0.1-e(FGF-0.23)FGF2.55+40.46-e(FGF*-0.23)40.46+e(FGF-0.23+Time/-4.08)40.46-e(Time/-4.08)40.46))/((-1+e(FGF0.24))(-1+e(Time/-4.08))*39.29)。只有身高(Ht)可以作为显著的协变量保留。在 3.5 分钟后达到 3.5%的组中,中位数性能误差和中位数绝对性能误差分别为-2.9%和 7.0%,在 5 分钟后达到 5%的组中分别为-3.4%和 11.4%,在 4.5 分钟后达到 6%的组中分别为-16.2%和 16.2%。

结论

经验模型可用于预测在开始给药后前 5 分钟内达到目标终末麻醉剂浓度的 FGF-FD 组合,其临床准确性可接受。序列可以在 Excel 文件中轻松计算,使用简单(一个固定的 FGF-FD 设置),并将最大限度地减少药物消耗,减少污染,因为可以确定可以使用的最低可能的 FGF。不同的麻醉机会针对不同的药物有不同的方程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71bc/3224103/7a163f0b9c6f/1471-2253-11-13-1.jpg

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