基于ERAS理念的术中麻醉管理在肺叶切除术中的应用
Gansu Medical Journal(2018)
Abstract
目的:探讨应用加快术后康复理念进行术中麻醉管理对行胸腔镜下肺叶切除术患者术后恢复的影响.方法:选择2016年10月至2017年10月在我院行择期胸腔镜下肺叶切除术患者60例,随机分为常规麻醉管理组(A组)和加快术后康复策略麻醉管理组(B组),各30例.对比两组患者术后苏醒情况、术后2、4、6、8、12、24、48h静息时NAS评分及咳嗽时NAS评分、镇痛情况、测定皮质醇及β-内啡肽水平.结果:与A组比较B组苏醒期躁动发生率和躁动评分明显降低(P<0.05),苏醒时间、拔管时间两组比较无统计学差异(P>0.05);B组术后2、4、6、8、12h的静息时和咳嗽时的数字等级评分(NAS评分)明显低于A组(P<0.05),24h及48h的NAS评分无统计学差异(P>0.05);与A组比较,B组术后48h皮肤瘙痒,恶心、呕吐及嗜睡的发生率明显降低(P<0.05),对疼痛控制的满意度两组比较差异无统计学意义(P>0.05),B组患者医疗服务满意度显著高于A组(P<0.05);B组在术后6、12、24h及48h的Cor和β-EP水平明显低于A组P<0.05),术前及术毕时比较差异无统计学意义(P>0.05).结论:应用加快术后康复理念进行术中麻醉管理,并采用0.5%罗哌卡因20ml椎旁阻滞+诱导前、缝皮前30min各给予凯纷50mg+术后常规PCIA的多模式镇痛方案,能有效缓解行胸腔镜下肺叶切除术患者术后苏醒期躁动症状,减轻麻醉后不良反应,促进麻醉后恢复,增加患者满意度.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined