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Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities
Intensive Care Medicine Experimental Pub Date : 2019-07-01 , DOI: 10.1186/s40635-019-0234-5
Candelaria de Haro 1, 2 , Ana Ochagavia 1, 2 , Josefina López-Aguilar 1, 2 , Sol Fernandez-Gonzalo 1, 3 , Guillem Navarra-Ventura 1 , Rudys Magrans 1, 2 , Jaume Montanyà 4 , Lluís Blanch 1, 2 ,
Affiliation  

Mechanical ventilation is common in critically ill patients. This life-saving treatment can cause complications and is also associated with long-term sequelae. Patient-ventilator asynchronies are frequent but underdiagnosed, and they have been associated with worse outcomes. Asynchronies occur when ventilator assistance does not match the patient’s demand. Ventilatory overassistance or underassistance translates to different types of asynchronies with different effects on patients. Underassistance can result in an excessive load on respiratory muscles, air hunger, or lung injury due to excessive tidal volumes. Overassistance can result in lower patient inspiratory drive and can lead to reverse triggering, which can also worsen lung injury. Identifying the type of asynchrony and its causes is crucial for effective treatment. Mechanical ventilation and asynchronies can affect hemodynamics. An increase in intrathoracic pressure during ventilation modifies ventricular preload and afterload of ventricles, thereby affecting cardiac output and hemodynamic status. Ineffective efforts can decrease intrathoracic pressure, but double cycling can increase it. Thus, asynchronies can lower the predictive accuracy of some hemodynamic parameters of fluid responsiveness. New research is also exploring the psychological effects of asynchronies. Anxiety and depression are common in survivors of critical illness long after discharge. Patients on mechanical ventilation feel anxiety, fear, agony, and insecurity, which can worsen in the presence of asynchronies. Asynchronies have been associated with worse overall prognosis, but the direct causal relation between poor patient-ventilator interaction and worse outcomes has yet to be clearly demonstrated. Critical care patients generate huge volumes of data that are vastly underexploited. New monitoring systems can analyze waveforms together with other inputs, helping us to detect, analyze, and even predict asynchronies. Big data approaches promise to help us understand asynchronies better and improve their diagnosis and management. Although our understanding of asynchronies has increased in recent years, many questions remain to be answered. Evolving concepts in asynchronies, lung crosstalk with other organs, and the difficulties of data management make more efforts necessary in this field.

中文翻译:

机械通气期间的人机不同步:当前知识和研究重点

机械通气在重症患者中很常见。这种挽救生命的治疗可能会导致并发症,并且还与长期后遗症有关。患者-呼吸机不同步很常见,但未被充分诊断,并且与更糟糕的结果有关。当呼吸机辅助与患者的需求不匹配时,就会出现不同步。通气过度辅助或辅助不足会转化为不同类型的不同步,对患者产生不同的影响。由于潮气量过大,辅助不足会导致呼吸肌负荷过重、空气饥饿或肺损伤。过度帮助会导致患者吸气驱动降低,并可能导致反向触发,这也会加重肺损伤。识别异步的类型及其原因对于有效治疗至关重要。机械通气和不同步会影响血流动力学。通气期间胸内压升高会改变心室前负荷和后负荷,从而影响心输出量和血流动力学状态。无效的努力可以降低胸内压,但双循环可以增加它。因此,不同步会降低液体反应性的某些血流动力学参数的预测准确性。新的研究也在探索异步的心理影响。出院后很长一段时间内,危重病幸存者的焦虑和抑郁很常见。机械通气患者会感到焦虑、恐惧、痛苦和不安全感,在不同步的情况下会加重。不同步与较差的总体预后有关,但是,患者-呼吸机交互不良与不良结果之间的直接因果关系尚未得到明确证明。重症监护患者会产生大量未被充分利用的数据。新的监控系统可以与其他输入一起分析波形,帮助我们检测、分析甚至预测异步。大数据方法有望帮助我们更好地理解异步并改进其诊断和管理。尽管近年来我们对异步的理解有所增加,但仍有许多问题有待回答。不断发展的异步概念、肺与其他器官的串扰以及数据管理的困难使得该领域需要更多的努力。重症监护患者会产生大量未被充分利用的数据。新的监控系统可以与其他输入一起分析波形,帮助我们检测、分析甚至预测异步。大数据方法有望帮助我们更好地理解异步并改进其诊断和管理。尽管近年来我们对异步的理解有所增加,但仍有许多问题有待回答。不断发展的异步概念、肺与其他器官的串扰以及数据管理的困难使得该领域需要更多的努力。重症监护患者会产生大量未被充分利用的数据。新的监控系统可以与其他输入一起分析波形,帮助我们检测、分析甚至预测异步。大数据方法有望帮助我们更好地理解异步并改进其诊断和管理。尽管近年来我们对异步的理解有所增加,但仍有许多问题有待回答。不断发展的异步概念、肺与其他器官的串扰以及数据管理的困难使得该领域需要更多的努力。大数据方法有望帮助我们更好地理解异步并改进其诊断和管理。尽管近年来我们对异步的理解有所增加,但仍有许多问题有待回答。不断发展的异步概念、肺与其他器官的串扰以及数据管理的困难使得该领域需要更多的努力。大数据方法有望帮助我们更好地理解异步并改进其诊断和管理。尽管近年来我们对异步的理解有所增加,但仍有许多问题有待回答。不断发展的异步概念、肺与其他器官的串扰以及数据管理的困难使得该领域需要更多的努力。
更新日期:2019-07-01
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