扁桃体低温等离子切除术与传统剥离术的应用效果比较
Comparison of the Application Effect of Tonsil Low-temperature Plasma Resection with Tradi-tional Stripping
张玉龙 1陈伟东 1田雅鑫1
作者信息
- 1. 中国人民解放军联勤保障部队第九八一医院耳鼻喉科,河北承德 067000
- 折叠
摘要
目的 对比扁桃体低温等离子切除术与传统剥离术的价值.方法 方便选取2021年4月—2023年10月中国人民解放军联勤保障部队第九八一医院77例接受扁桃体摘除手术患者作为研究对象,以手术方式的不同分为两.研究组(n=38)行扁桃体低温等离子切除术,对照组(n=39)行传统剥离术.比较两组患者围术期指标、炎性因子、并发症.结果 术后,研究组围术期指标优于参照组,差异有统计学意义(P<0.05);术后,研究组炎性因子水平低于对照组,差异有统计学意义(P<0.05);研究组并发症发生率(0)低于参照组(15.38%),差异有统计学意义(χ2=4.379,P<0.05).结论 相比于传统剥离术,扁桃体低温等离子切除术的应用有助于优化围术期指标,减轻炎症反应,能够降低并发症发生风险.
Abstract
Objective To compare the value of tonsil low-temperature plasma resection with traditional stripping.Methods A total of 77 patients receiving tonsil removal surgery in 981 Hospital of the Chinese People's Liberation Army Joint Logistic Support Force from April 2021 to October 2023 were conveniently se-lected as the research objects and divided into two groups according to different surgical methods.The study group(n=38)underwent low temperature plasma tonsillectomy,and the reference group(n=39)underwent tradi-tional dissection.Perioperative indexes,inflammatory factors and complications were compared between the two groups.Results After operation,the perioperative indexes of the study group were better than those of the reference group,the difference was statistically significant(P<0.05).After operation,the level of inflammatory factors in the study group was lower than that in the reference group,and the difference was statistically signifi-cant(P<0.05).The complication rate(0)of the study group was lower than(15.38%)the reference group,and the difference was statistically significant(χ2=4.379,P<0.05).Conclusion The application of tonsil low-temperature plasma resection compared with traditional stripping in the clinic helps to optimize the periopera-tive indexes,reduces the inflammatory response,and is also able to reduce the risk of complications.
关键词
低温等离子切除术/传统剥离术/应用效果Key words
Low-temperature plasma resection/Traditional stripping/Application effect引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据