Computational Analysis of Noninvasive Ventilation

描述(申请人提供)：尽管机械通风被认为是一种挽救生命的干预措施，也是现代重症和急救护理的基石，但它可能会让人感到不适和伤害。患者特征与呼吸机施加流量和压力的方式之间的复杂相互作用决定了支持的水平和充分性、患者的舒适度以及受伤的可能性。患者：呼吸机的相互作用是一个复杂而动态的过程，充满了反馈回路和适应。这种复杂性在压力支持无创通气(PSNIV)的设置中至关重要，PSNIV是一种越来越常见的支持呼吸衰竭患者的方法。目前还没有一个严格的概念基础来优化PSNIV的应用。我们将开发一个严格的数学概念框架和一套实用工具，通过实现四个具体目标来分析、监测和优化压力支持无创通风的应用。 1)具体目标一：开发、校准和验证用于压力支持无创通风实时分析的实用数学模型和工具； 2)具体目标二：利用人体数据构建辅助呼吸过程中患者阻抗(肺硬度和阻力)特征和呼吸作用力模式的库，系统地研究模型复杂性与模型预测的准确性和稳健性之间的交互作用； 3)具体目标三：应用当代数学技术，确定应用PSNIV的方法，以期提高在特定人群中获得适当支持的可能性； 4)具体目标4：开发自动化、非侵入性工具，以a)识别指示潜在患者：呼吸机冲突的气道开口流量和压力分布，以及b)客观地对患者的呼吸模式进行分类。 这些工具将从“第一原则”发展而来，具有严格的数学和实验室分析和优化，将与临床校准、评估和验证紧密联系在一起。这项工作最终将帮助我们更好地管理接受无创呼吸机治疗的患者，无论是急性疾病(如肺气肿加重)，还是在家中(如阻塞性睡眠呼吸暂停患者)。

DESCRIPTION (provided by applicant): Despite its recognition as a lifesaving intervention and cornerstone of modern intensive and emergency care, mechanical ventilation can be both uncomfortable and injurious. Complex interactions between patient characteristics and the manner in which the ventilator applies flow and pressure determine the level and adequacy of support, the degree of patient comfort, and the potential for injury. Patient: ventilator interaction is a complex and dynamic process, fraught with feedback loops and adaptation. This complexity is of greatest importance in the setting of pressure support noninvasive ventilation (PSNIV), an increasingly common approach to supporting individuals with respiratory failure. There is not at present a rigorous conceptual foundation for optimizing the application of PSNIV. We will develop both a mathematically rigorous conceptual framework and a suite of practical tools for analyzing, monitoring, and optimizing the application of pressure support noninvasive ventilation by accomplishing 4 specific aims. 1) Specific Aim One: Develop, calibrate, and validate practical mathematical models and tools for the real- time analysis of pressure support noninvasive ventilation; 2) Specific Aim Two: Use human data to construct libraries of patient impedance (lung "stiffness" and resistance) characteristics and patterns of respiratory effort during assisted breathing and systematically investigate interactions between model complexity and the accuracy and robustness of model predictions; 3) Specific Aim Three: Apply contemporary mathematical techniques to identify approaches for applying PSNIV that are anticipated to improve the likelihood of adequate support in specific populations; 4) Specific Aim Four: Develop automated, noninvasive tools to a) identify airway opening flow- and pressure profiles indicating potential patient: ventilator conflict and b) objectively classify patient breathing patterns. These tools will be developed from "first principles," with rigorous mathematical and laboratory analyses and optimization that will be tightly linked to clinical calibration, evaluation, and validation. This work will ultimately help us to better manage patients who are treated with noninvasive ventilation, whether for an acute illness (such as an exacerbation of emphysema), or at home (as in the case of obstructive sleep apnea).