Suppr超能文献

优化机械通气:一种用于识别和管理重症监护中患者-呼吸机不同步的临床实用床边方法。

Optimizing Mechanical Ventilation: A Clinical and Practical Bedside Method for the Identification and Management of Patient-Ventilator Asynchronies in Critical Care.

作者信息

Costa Vasco, Cidade José Pedro, Medeiros Inês, Póvoa Pedro

机构信息

Department of Critical Care Medicine, Hospital de São Francisco Xavier, Unidade Local de Saúde Lisboa Ocidental (ULSLO), Estrada Forte do Alto Duque, 1449-005 Lisbon, Portugal.

NOVA Medical School, New University of Lisbon, 1169-056 Lisbon, Portugal.

出版信息

J Clin Med. 2025 Jan 2;14(1):214. doi: 10.3390/jcm14010214.

Abstract

The prompt identification and correction of patient-ventilator asynchronies (PVA) remain a cornerstone for ensuring the quality of respiratory failure treatment and the prevention of further injury to critically ill patients. These disruptions, whether due to over- or under-assistance, have a profound clinical impact not only on the respiratory mechanics and the mortality associated with mechanical ventilation but also on the patient's cardiac output and hemodynamic profile. Strong evidence has demonstrated that these frequently occurring and often underdiagnosed events have significant prognostic value for mechanical ventilation outcomes and are strongly associated with prolonged ICU stays and hospital mortality. Halting the consequences of PVA relies on the correct identification and approach of its underlying causes. However, this often requires advanced knowledge of respiratory physiology and the evaluation of complex ventilator waveforms in patient-ventilator interactions, posing a challenge to intensive care practitioners, in particular, those less experienced. This review aims to outline the most frequent types of PVA and propose a clinical algorithm to provide physicians with a structured approach to assess, accurately diagnose, and correct PVA.

摘要

及时识别并纠正患者 - 呼吸机不同步(PVA)仍然是确保呼吸衰竭治疗质量以及预防重症患者受到进一步伤害的基石。这些干扰,无论是由于辅助过度还是不足,不仅对呼吸力学以及与机械通气相关的死亡率有深远的临床影响,而且对患者的心输出量和血流动力学状况也有影响。有力证据表明,这些频繁发生且常常未被诊断出来的事件对机械通气结果具有重要的预后价值,并且与重症监护病房(ICU)住院时间延长和医院死亡率密切相关。阻止PVA的后果依赖于对其潜在原因的正确识别和处理方法。然而,这通常需要呼吸生理学的先进知识以及对患者 - 呼吸机相互作用中复杂呼吸机波形的评估,这对重症监护从业者,尤其是经验较少的从业者构成了挑战。本综述旨在概述PVA最常见的类型,并提出一种临床算法,为医生提供一种结构化的方法来评估、准确诊断和纠正PVA。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3637/11721790/ee39ead1fff2/jcm-14-00214-ch001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验