Central mechanisms of respiratory adaptation to mechanical ventilation

 描述（由申请人提供）：重症监护室（ICU）患者机械通气期间的主要挑战是如何平稳有效地使呼吸机与患者的呼吸努力同步。不同步可能导致患者不适，增加镇静需求，延长住院时间和降低生存概率。当前用于ICU的机械呼吸机设计没有考虑患者对迷走神经容量相关反馈的深刻呼吸适应，并且易于发生患者-呼吸机并发症。了解呼吸控制和机械通气之间的双向关系，并将这些新概念带到床边，越来越多地被认为是重症监护医学中一个主要的未满足的优先事项。基于夹带的机械通气是一种新的通气方法，通过将呼吸神经生物学概念纳入机械通气设计中，可能会彻底改变该领域并改变临床实践。这种创新的机械通气技术的动机是最近的证据表明，神经回路在脑桥呼吸中枢通过学习和记忆的Hering-Breuer反射，在促进呼吸夹带到机械通气中起着重要作用。患者-呼吸机夹带是基于耦合振荡器之间相互夹带的经典物理理论的基本生理现象。在基于呼吸机的通气中，患者的自主呼吸节律和呼吸机节律以相同的克里思彼此锁相，就像两个人一起跳舞一样。这种新一代通气模式最近获得了FDA批准的临床试验研究器械豁免。为了促进基础技术从台式到床边的转化，R 01申请提出了一系列临床前研究，目的是更好地了解患者-呼吸机相互作用的神经生理学决定因素。由于患者-呼吸机夹带是一种复杂的现象，因此将实验测试和多尺度建模相结合的系统生物学方法用于定量数据分析和预测新结果是必不可少的。这将通过阐明在系统（目标1）和细胞水平（目标2）下呼吸适应机械通气的中枢机制来实现，并开发患者-呼吸机相互作用的多尺度神经网络模型，以便模拟实验结果并以定量方式预测开环和闭环呼吸机-呼吸机夹带频率和相位响应关系（目标3）.待检验的一个主要假设是，机械通气的呼吸夹带是由脑桥呼吸调节中心中的不同神经元群体介导的，其特征在于其对NMDA受体活性的关键依赖性和对抑制的敏感性。 神经肽生长抑素。

 DESCRIPTION (provided by applicant): A major challenge during mechanical ventilation of patients in the intensive care unit (ICU) is how to synchronize the ventilator with the patient's breathing effort smoothly and effectively. Dyssynchrony could lead to patient discomfort and increased need for sedation, longer hospital stay and lower probability of survival. Current mechanical ventilator designs for use in the ICU do not take into consideration the patient's profound respiratory adaptation to vagal volume-related feedback, and are prone to patient-ventilator asynchrony. Understanding the bidirectional relationship between control of breathing and mechanical ventilation and bringing such new concepts to the bedside is increasingly recognized as a major unmet priority in critical care medicine. Entrainment-based mechanical ventilation is a novel ventilation approach that may potentially revolutionize the field and shift clinical practice by incorporating respiratory neurobiology concepts into mechanical ventilator designs. This innovative mechanical ventilation technique is motivated by recent evidence indicating that neural circuits in the pontine pneumotaxic center plays an important role in promoting respiratory entrainment to mechanical ventilation through learning and memory of the Hering-Breuer reflex. Patient-ventilator entrainment is a fundamental physiologic phenomenon that is grounded in the classical physics theory of mutual entrainment between coupled oscillators. In entrainment-based ventilation, the patient's spontaneous respiratory rhythm and the ventilator rhythm are phase-locked to one another on the same tempo, just like two individuals dancing together. This new-generation ventilation mode has recently gained FDA approval of investigational device exemption for clinical trial. To facilitate the translation of te base technology from the benchtop to the bedside, this R01 application proposes a series of preclinical studies with an objective to gain better understanding of the neurophysiologic determinants of patient-ventilator interaction. Because patient-ventilator entrainment is a complex phenomenon, a systems biology approach combining experimental testing and multiscale modeling for quantitative data analysis and prediction of novel outcomes is essential. This will be achieved by elucidating the central mechanisms underlying respiratory adaptation to mechanical ventilation at both the systems (Aim 1) and cellular levels (Aim 2), and developing a multiscale neural network model of patient-ventilator interaction in order to simulate the experimental results and predict the open-loop and closed-loop respiratory-ventilator entrainment frequency and phase response relationships in a quantitative manner (Aim 3). A major hypothesis to be tested is that respiratory entrainment to mechanical ventilation is mediated by a distinct neuronal population in the pontine pneumotaxic center which is distinguished by its critical dependence on NMDA receptor activity and sensitivity to inhibition by the neuropeptide somatostatin.