van Oosten Julien P, Akoumianaki Evangelia, Jonkman Annemijn H
Intensive Care Volwassenen, Erasmus Medical Center, Rotterdam, The Netherlands.
Adult Intensive Care Unit, University Hospital of Heraklion, Heraklion.
Curr Opin Crit Care. 2025 Feb 1;31(1):12-20. doi: 10.1097/MCC.0000000000001229. Epub 2024 Nov 14.
To summarize basic physiological concepts of breathing effort and outline various methods for monitoring effort of inspiratory and expiratory muscles.
Esophageal pressure (Pes) measurement is the reference standard for respiratory muscle effort quantification, but various noninvasive screening tools have been proposed. Expiratory occlusion pressures (P0.1 and Pocc) could inform about low and high effort and the resulting lung stress, with Pocc outperforming P0.1 in identifying high effort. The pressure muscle index during an inspiratory hold could unveil inspiratory muscle effort, however obtaining a reliable inspiratory plateau can be difficult. Surface electromyography has the potential for inspiratory effort estimation, yet this is technically challenging for real-time assessment. Expiratory muscle activation is common in the critically ill warranting their assessment, that is, via gastric pressure monitoring. Expiratory muscle activation also impacts inspiratory effort interpretation which could result in both under- and overestimation of the resulting lung stress. There is likely a future role for machine learning applications to automate breathing effort monitoring at the bedside.
Different tools are available for monitoring the respiratory muscles' effort during mechanical ventilation - from noninvasive screening tools to more invasive quantification methods. This could facilitate a lung and respiratory muscle-protective ventilation approach.
总结呼吸用力的基本生理概念,并概述监测吸气和呼气肌肉用力的各种方法。
食管压力(Pes)测量是呼吸肌用力量化的参考标准,但已提出了各种非侵入性筛查工具。呼气阻断压力(P0.1和Pocc)可以反映低用力和高用力情况以及由此产生的肺应力,在识别高用力方面,Pocc优于P0.1。吸气末屏气时的压力肌肉指数可以揭示吸气肌用力情况,然而获得可靠的吸气平台可能很困难。表面肌电图有估计吸气用力的潜力,但这在实时评估中在技术上具有挑战性。呼气肌激活在危重症患者中很常见,需要对其进行评估,即通过胃压力监测。呼气肌激活也会影响吸气用力的解读,这可能导致对由此产生的肺应力的低估和高估。机器学习应用在床边自动监测呼吸用力方面可能会有未来的作用。
在机械通气期间,有不同的工具可用于监测呼吸肌的用力情况——从非侵入性筛查工具到更具侵入性的量化方法。这有助于采取肺和呼吸肌保护性通气方法。