PREVENTING VENTILATOR-ASSOCIATED LUNG INJURY USING FEEDBACK CONTROL ENGINEERING

该项目将开发肺泡通气动力学（包括气体交换、动态和非线性肺泡顺应性和支气管阻力）、心血管性能和血液（参考其携气能力）的多隔室、多标量数学模型。所开发的模型将被用来阐明的程度和分布的因素造成的肺损伤的病变，异质性，机械通气肺。通过将问题视为反馈控制之一，我们将研究机械呼吸机中的参数调整方法，以优化心输出量和动脉气体张力，同时最大限度地减少与VALI相关的因素。由于不可避免的复杂性的仿真模型，我们打算开发，先进的方法，从多变量鲁棒和最优控制理论将需要，以确定哪些参数的组合应进行调整，以及如何，以实现所需的减少VALI。这项工作将超越以前试图通过拟议的模拟平台的更高保真度来量化机械通气期间肺损伤风险因素的尝试。此外，我们将采用稳健性分析技术的建模，以提高我们的研究结果的可靠性和适用性。这将使我们能够进行群体建模，而不是常用的建模和研究单个理想化主题的方法，使我们的研究结果适用于群体和各种真实的患者，与以前的工作相比，理想化的主题实际上既不代表群体也不代表任何一个个体。

This project will develop a multi-compartmental, multi-scalar, mathematical model of alveolar ventilation dynamics (which includes gas exchange, dynamic and non-linear alveolar compliance and bronchial resistance), cardiovascular performance and blood (with reference to its gas-carrying abilities). The developed model will be used to elucidate the extent and distribution of the factors causative of lung injury in diseased, heterogeneous, mechanically-ventilated lungs. By treating the problem as one of feedback control, we will investigate methods of parameter adjustment in the mechanical ventilator to optimise cardiac output and arterial gas tensions while minimising the factors associated with VALI. Due to the inevitable complexity of the simulation model which we intend to develop, advanced methods from multivariable robust and optimal control theory will be required in order to identify which combinations of parameters should be adjusted, and how, in order to achieve the desired reduction in VALI. The work will go beyond that previously attempted in quantifying the factors that risk lung injury during mechanical ventilation through the greater fidelity of the proposed simulation platform. In addition, we will apply robustness analysis techniques to the modelling to improve the reliability and applicability of our findings. This will allow us to perform population modelling, rather than the commonly used approach of modelling and studying a single, idealized subject, rendering our findings applicable to populations and to a variety of real patients, in contrast to previous work where the idealized subject is in fact representative of neither the population nor any one individual.