在机械通风过程中监测呼吸肌力的非侵入性方法
1Professor Emeritus Medicine, Anesthesiology and Engineering, The George Washington University, 700 New Hampshire Ave, NW Suite 510, Washington, DC, 20037, USA. gutier@gwu.edu.
Journal of clinical monitoring and computing
|May 11, 2024
概括
这项研究提出了一种非侵入性方法,用于测量机械通风 (MV) 期间使用气道信号测量呼吸肌力 (Pmus). 该方法在体积控制和压力控制模式中准确量化了患者的努力.
科学领域:
- 呼吸系统生理学 呼吸系统生理学
- 生物医学工程 生物医学工程
- 关键护理医学 关键护理医学
背景情况:
- 精确评估呼吸肌力努力对于优化机械通风 (MV) 设置至关重要.
- 目前用于量化呼吸肌力劳动的方法可能具有侵入性或缺乏自动化.
- 需要使用非侵入性技术来改善患者监测和呼吸器管理.
研究的目的:
- 引入和验证一种新的,非侵入性的方法,用于在MV期间自动量化呼吸肌力劳动 (Pmus).
- 通过气道压力 (Paw) 和空气流量 (Faw) 的测量,以数值方式解决呼吸系统的运动方程.
- 为了将计算的Pmus与不同MV模式中的已确定的生理反应相关联.
主要方法:
- 利用了250名入侵性MV患者的数据,分析了详细的Paw和Faw记录.
- 开发了一种算法,可以从体积控制 (VC) 和压力控制 (PC) 模式处理51,268个呼吸时代.
- 计算了Pmus及其压力-时间乘积 (PmusPTP) 并将它们与Paw的压力-时间乘积 (PawPTP) 和潮体积 (VT) 相关联.
主要成果:
- 在VC模式下PmusPTP和PawPTP (R2 = 0.91) 和PC模式下VT (R2 = 0.88) 之间显示出强烈的相关性.
- 通过显示每个模式的替代参数中预期的相关性和可以忽略的相关性来证实假设.
- 在不同的通风策略中验证了非侵入性方法的准确性.
结论:
- 这项研究证实了使用气道信号分析在MV期间非侵入性评估呼吸肌力劳动的可行性.
- 开发的方法显示了对实时监控和调整通风器设置的前景.
- 建议进行进一步的临床验证,以探索这种技术在重症监护机构的全部潜力.
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