监测急性呼吸衰竭患者的努力和呼吸驱动。

IF 3.4 3区 医学 Q1 CRITICAL CARE MEDICINE
Current Opinion in Critical Care Pub Date : 2025-06-01 Epub Date: 2025-04-04 DOI:10.1097/MCC.0000000000001271
Guillaume Carteaux, Rémi Coudroy
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引用次数: 0

摘要

回顾目的:准确监测呼吸驱动和吸气力对于优化急性呼吸衰竭时的通气支持至关重要。这篇综述评估了当前和新兴的床边方法来评估呼吸驱动和努力。最近的发现:虽然膈肌电活动和食管压力仍然是评估呼吸动力和努力的参考标准,但它们的临床应用在很大程度上仅限于研究。在床边,气道闭塞操作是最有用的工具:P0.1是驱动的可靠标志,可以检测异常的吸气力度,而闭塞压力(Pocc)在识别过度用力方面可能优于P0.1。压力-肌肉指数(PMI)可以帮助检测吸气力度不足,尽管其准确性取决于获得稳定的平台压力。其他技术,如中心静脉压力波动(ΔCVP),很有希望,但需要进一步研究。在不久的将来,新兴的机器学习和基于人工智能的算法可能在自动呼吸监测中发挥关键作用。总结:虽然Pes和EAdi仍然是参考方法,但气道闭塞操作是目前监测呼吸驱动和努力最实用的床边工具。诸如ΔCVP等非侵入性替代疗法值得进一步评估。人工智能和机器学习可能很快就会为床边监测呼吸驱动和努力提供自动化解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring effort and respiratory drive in patients with acute respiratory failure.

Purpose of review: Accurate monitoring of respiratory drive and inspiratory effort is crucial for optimizing ventilatory support during acute respiratory failure. This review evaluates current and emerging bedside methods for assessing respiratory drive and effort.

Recent findings: While electrical activity of the diaphragm and esophageal pressure remain the reference standards for assessing respiratory drive and effort, their clinical utility is largely limited to research. At the bedside, airway occlusion maneuvers are the most useful tools: P0.1 is a reliable marker of drive and detects abnormal inspiratory efforts, while occlusion pressure (Pocc) may outperform P0.1 in identifying excessive effort. The Pressure-Muscle-Index (PMI) can help detecting insufficient inspiratory effort, though its accuracy depends on obtaining a stable plateau pressure. Other techniques, such as central venous pressure swings (ΔCVP), are promising but require further investigation. Emerging machine learning and artificial intelligence based algorithms could play a pivotal role in automated respiratory monitoring in the near future.

Summary: Although Pes and EAdi remain reference methods, airway occlusion maneuvers are currently the most practical bedside tools for monitoring respiratory drive and effort. Noninvasive alternatives such as ΔCVP deserve further evaluation. Artificial intelligence and machine learning may soon provide automated solutions for bedside monitoring of respiratory drive and effort.

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来源期刊
Current Opinion in Critical Care
Current Opinion in Critical Care 医学-危重病医学
CiteScore
5.90
自引率
3.00%
发文量
172
审稿时长
6-12 weeks
期刊介绍: ​​​​​​​​​Current Opinion in Critical Care delivers a broad-based perspective on the most recent and most exciting developments in critical care from across the world. Published bimonthly and featuring thirteen key topics – including the respiratory system, neuroscience, trauma and infectious diseases – the journal’s renowned team of guest editors ensure a balanced, expert assessment of the recently published literature in each respective field with insightful editorials and on-the-mark invited reviews.
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