ISSN 2097-6046（网络）
ISSN 2096-7446（印刷）
CN 10-1655/R
主管：中国科学技术协会
主办：中华护理学会
中华急危重症护理杂志

中华急危重症护理杂志 >

2025 , Vol. 6 >Issue 6: 671 - 675

DOI: https://doi.org/10.3761/j.issn.2096-7446.2025.06.005

急危重症智慧护理创新与实践

人工气道阻力智慧预警系统的研发及应用研究

  • 王伟钟 ,
  • 周尧英 ,
  • 刘伟董
展开
  • 312000 绍兴市人民医院内科重症医学科（王伟钟），护理部（周尧英），外科重症医学科（刘伟董）
王伟钟：男，本科，副主任护师，副护士长，E-mail：906134210@qq.com
周尧英，E-mail：1275965033@qq.com

收稿日期: 2024-07-15

  网络出版日期: 2025-06-06

基金资助

2023年浙江省医药卫生科技计划(2023KY356)

收起

Development and application of intelligent warning system for artificial airway resistance

  • WANG Weizhong ,
  • ZHOU Yaoying ,
  • LIU Weidong
Expand
  • Department of Medical Intensive Care Unit，Shaoxing People’s Hospital，Shaoxing，312000，China

Received date: 2024-07-15

  Online published: 2025-06-06

Fold

摘要

目的 研发人工气道阻力智慧预警系统，早期发现机械通气患者的人工气道分泌物，探讨其在提高人工气道分泌物干预率、减少分泌物粘滞中的效果。 方法 2022年5月开始研发基于物联技术的人工气道阻力智慧预警系统，实现人工气道分泌物粘滞的评估-预警-质控全程闭环智慧管理。2023年3月—4月，在浙江省某三级甲等医院内科重症医学科、外科重症医学科中应用该系统。比较系统应用后（2023年3月—4月）与应用前（2022年3月—4月）机械通气患者的人工气道分泌物干预率、人工气道分泌物粘滞率。 结果 系统应用后，人工气道分泌物干预率由14.2%提升至25.8%，人工气道分泌物粘滞率由45.3%下降至24.5%，差异均有统计学意义（P<0.05）。结论 应用基于物联网技术的人工气道阻力智慧预警系统，可提高人工气道分泌物干预率，降低分泌物粘滞率。

关键词： 重症监护病房; 物联网; 人工气道; 分泌物; 气道阻力; 预警; 护理管理研究

本文引用格式

王伟钟 , 周尧英 , 刘伟董 . 人工气道阻力智慧预警系统的研发及应用研究[J]. 中华急危重症护理杂志, 2025 , 6(6) : 671 -675 . DOI: 10.3761/j.issn.2096-7446.2025.06.005

Abstract

Objective To develop artificial airway resistance intelligent warning system to detect the secretions early for patients with mechanical ventilation，and explore its effect in improving the intervention rate of secretions and reducing the viscosity of artificial airway secretions. Methods In May 2022，we started to develop an artificial airway resistance intelligent early warning system based on Internet of Things technology to achieve closed-loop intelligent management of artificial airway secretion viscosity. From March to April 2023，it was applied in the Department of Internal intensive Care Medicine and the Department of Surgical Intensive Care Medicine of a tertiary class A hospital in Zhejiang Province. The intervention rate of artificial airway secretion and the viscosity rate of artificial airway secretion in patients with mechanical ventilation after the application of the system（March to April 2023） and before the application（March to April 2022） were compared. Results After the system application，the intervention rate of artificial airway secretion increased from 14.2% to 25.8%，and the viscosity rate of artificial airway secretion decreased from 45.3% to 24.5%，with statistical significance（P<0.05）. Conclusion The application of intelligent early warning system of artificial airway resistance based on Internet of Things technology can improve the intervention rate of artificial airway secretion and reduce the viscosity of secretion.

Key words： Intensive Care Units; Internet of Things; Artificial Airway; Secretion; Airway Resistance; Warning; Nursing Administration Research

参考文献

[1] Boqué MC, Gualis B, Sandiumenge A,et al. Endotracheal tube intraluminal diameter narrowing after mechanical ventilation:use of acoustic reflectometry[J]. Intensive Care Med, 2004, 30(12):2204-2209.
[2] Coutinho WM, Vieira PJC, Kutchak FM,et al. Comparison of mechanical insufflation-exsufflation and endotracheal suctioning in mechanically ventilated patients:effects on respiratory mechanics,hemodynamics,and volume of secretions[J]. Indian J Crit Care Med, 2018, 22(7):485-490.
[3] Brady Scott J, Dubosky MN, Vines DL,et al. Evaluation of endotracheal tube scraping on airway resistance[J]. Respir Care, 2017, 62(11):1423-1427.
[4] Mietto C, Pinciroli R, Piriyapatsom A,et al. Tracheal tube obstruction in mechanically ventilated patients assessed by high-resolution computed tomography[J]. Anesthesiology, 2014, 121(6):1226-1235.
[5] 中华医学会呼吸病学分会呼吸治疗学组. 成人气道分泌物的吸引专家共识(草案)[J]. 中华结核和呼吸杂志, 2014, 37(11):809-811.
  Respiratory Therapy Group of Respiratory Medicine Branch of Chinese Medical Association. Expert consensus on attraction of airway secretions in adults(Draft)[J]. Chin J Tuberc Respir Dis, 2014, 37(11):809-811.
[6] Maghsoodi B, Sabetian G, Azimi A,et al. Sound level analysis in endotracheal tube obstruction in spontaneous breathing and mechanical ventilation-an animal model study[J]. J Clin Monit Comput, 2017, 31(6):1235-1240.
[7] Pedersen TH, Ueltschi F, Hornshaw T,et al. Optimisation of air-way management strategies:a prospective before-and-after study on events related to airway management[J]. Br J Anaesth, 2021, 127(5):798-806.
[8] Kawati R, Vimlati L, Guttmann J,et al. Change in expiratory flow detects partial endotracheal tube obstruction in pressure-controlled ventilation[J]. Anesth Analg, 2006, 103(3):650-657.
[9] Menezes Júnior JDN, Silva LM, Santos LJM,et al. Reproducibil-ity of respiratory mechanics measurements in patients on inva-sive mechanical ventilation[J]. Rev Bras Ter Intensiva,2020, 32(3):398-404.
[10] Dexter AM, Brady Scott J. Airway management and ventilator-associated events[J]. Respir Care, 2019, 64(8):986-993.
[11] Care AAFR, Restrepo RD, Walsh BK. Humidification during invasive and noninvasive mechanical ventilation:2012[J]. Respir Care, 2012, 57(5):782-788.
[12] 潘盼, 解立新. 智慧重症监护病房助力重症医疗可及性[J]. 中华医学杂志, 2023, 103(26):1966-1969.
  Pan P, Xie LX. Intelligent intensive care unit makes medicine more accessible[J]. Natl Med J China, 2023, 103(26):1966-1969.
[13] 孙乔, 王素梅, 程聪,等. ICU物联网智慧血液管理方案的构建及应用[J]. 中华急危重症护理杂志, 2023, 4(11):978-982.
  Sun Q, Wang SM, Cheng C,et al. Construction and application of the Internet of Things intelligent blood management scheme in ICU[J]. Chin J Emerg Crit Care Nurs, 2023, 4(11):978-982.
[14] Kilintzis V, Beredimas N, Kaimakamis E,et al. CoCross:an ICT platform enabling monitoring recording and fusion of clinical information chest sounds and imaging of COVID-19 ICU patients[J]. Healthcare, 2022, 10(2):276.
[15] De Corte T, Van Hoecke S, De Waele J. Artificial intelligence in infection management in the ICU[J]. Crit Care, 2022, 26(1):79.
[16] 刘裕文, 李庆印, 刘湉,等. 急性主动脉夹层患者急诊临床恶化动态预警方案的构建及应用研究[J]. 中华急危重症护理杂志, 2022, 3(6):485-490.
  Liu YW, Li QY, Liu T,et al. The construction and application of dynamic warning plan for emergency clinical deterioration of patients with acute aortic dissection[J]. Chin J Emerg Crit Care Nurs, 2022, 3(6):485-490.
[17] 王丽竹, 兰美娟, 杨燕,等. ICU精准化液体信息管理系统的构建及应用研究[J]. 中华急危重症护理杂志, 2021, 2(5):400-404.
  Wang LZ, Lan MJ, Yang Y,et al. The construction and application of precise fluid volume management system in ICU[J]. Chin J Emerg Crit Care Nurs, 2021, 2(5):400-404.
[18] 俞小玲, 钟寅波, 阮晓芬,等. 困难气道高危患者信息管理系统的设计及应用[J]. 中华急危重症护理杂志, 2022, 3(5):464-467.
  Yu XL, Zhong YB, Ruan XF,et al. Design and application of information management system for high-risk patients with difficult airway[J]. Chin J Emerg Crit Care Nurs, 2022, 3(5):464-467.
[19] 徐佳卿, 莫敏华, 高莹莹,等. 基于失效模式与效应分析的导管信息化平台在预防ICU中心静脉导管相关血流感染中的应用[J]. 中国实用护理杂志, 2023, 39(24):1846-1852.
  Xu JQ, Mo MH, Gao YY,et al. Application of catheter information platform based on failure mode and effect analysis in preventing central venous catheter-related infection in intensive care unit[J]. Chin J Pract Nurs, 2023, 39(24):1846-1852.
[20] 束嘉俊, 王金琪, 段霞,等. 危重症患者病情动态评分信息化系统的构建与应用[J]. 中华现代护理杂志, 2018, 24(35):4305-4308.
  Shu JJ, Wang JQ, Duan X,et al. Development and application of dynamic scoring information system for critically ill patients[J]. Chin J Mod Nurs, 2018, 24(35):4305-4308.
Options
文章导航

/

〈 〉