Suppr超能文献

基于丙泊酚和芬太尼麻醉,预测靶控输注中丙泊酚苏醒效应室浓度的混杂因素。

Confounding factors to predict the awakening effect-site concentration of propofol in target-controlled infusion based on propofol and fentanyl anesthesia.

作者信息

Chan Shun-Ming, Lee Meei-Shyuan, Lu Chueng-He, Cherng Chen-Hwan, Huang Yuan-Shiou, Yeh Chun-Chang, Kuo Chan-Yang, Wu Zhi-Fu

机构信息

Department of Anesthesiology, Tri-Service General Hospital and National Defense Medical Center, Taipei, Taiwan, Republic of China; Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, Republic of China.

School of Public Health, National Defense Medical Center, Taipei, Taiwan, Republic of China.

出版信息

PLoS One. 2015 May 4;10(5):e0124343. doi: 10.1371/journal.pone.0124343. eCollection 2015.

Abstract

We conducted a large retrospective study to investigate the confounding factors that predict Ce ROC under propofol-based TIVA with TCI. We recorded sex, age, height, weight, Ce LOC, Ce ROC, total propofol and fentanyl consumption dose, and anesthetic time. Simple linear regression models were used to identify potential predictors of Ce ROC, and multiple linear regression models were used to identify the confounding predictors of Ce ROC. We found that Ce ROC correlated with age, sex, Ce LOC, and both total fentanyl and propofol consumption dose. The prediction formula was: Ce ROC = 0.87 - 0.06 × age + 0.18 × Ce LOC + 0.04 (if fentanyl consumption > 150 μg; if not, ignore this value) + 0.07 × (1 or 2, according to the total propofol consumption dose, 1 for a propofol amount 1000-2000 mg and 2 for a propofol amount > 2000 mg). We simplified the formula further as Ce ROC = 0.87 - 0.06 × age + 0.18 × Ce LOC. In conclusion, Ce ROC can be predicted under TCI with propofol- and fentanyl-based TIVA. The confounding factors that predicted propofol Ce ROC are age, sex, Ce LOC, and total consumption dose of propofol and fentanyl.

摘要

我们进行了一项大型回顾性研究,以调查在基于丙泊酚的靶控输注全凭静脉麻醉(TIVA)下预测脑电双频指数(Ce ROC)的混杂因素。我们记录了性别、年龄、身高、体重、脑电双频指数(Ce LOC)、脑电双频指数(Ce ROC)、丙泊酚和芬太尼的总消耗量以及麻醉时间。使用简单线性回归模型来识别脑电双频指数(Ce ROC)的潜在预测因素,并使用多元线性回归模型来识别脑电双频指数(Ce ROC)的混杂预测因素。我们发现脑电双频指数(Ce ROC)与年龄、性别、脑电双频指数(Ce LOC)以及芬太尼和丙泊酚的总消耗量相关。预测公式为:脑电双频指数(Ce ROC)=0.87 - 0.06×年龄 + 0.18×脑电双频指数(Ce LOC) + 0.04(如果芬太尼消耗量>150μg;如果不是,则忽略此值) + 0.07×(1或2,根据丙泊酚总消耗量,丙泊酚量为1000 - 2000mg时为1,丙泊酚量>2000mg时为2)。我们将公式进一步简化为脑电双频指数(Ce ROC)=0.87 - 0.06×年龄 + 0.18×脑电双频指数(Ce LOC)。总之,在基于丙泊酚和芬太尼的TIVA靶控输注下可以预测脑电双频指数(Ce ROC)。预测丙泊酚脑电双频指数(Ce ROC)的混杂因素是年龄、性别、脑电双频指数(Ce LOC)以及丙泊酚和芬太尼的总消耗量剂量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71f7/4418734/aef38c37ed1c/pone.0124343.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验