PREVENTING VENTILATOR-ASSOCIATED LUNG INJURY USING FEEDBACK CONTROL ENGINEERING

该项目将开发一个多室、多标量的肺泡通风动力学(包括气体交换、动态和非线性的肺泡顺应性和支气管阻力)、心血管性能和血液(参考其载气能力)的数学模型。开发的模型将被用来阐明在病变的、异质的、机械通气的肺中导致肺损伤的因素的范围和分布。通过将问题视为反馈控制问题，我们将研究机械呼吸机参数调整的方法，以优化心输出量和动脉血气张力，同时将与VALI相关的因素降至最低。由于我们打算开发的仿真模型不可避免地复杂，因此需要多变量鲁棒和最优控制理论的先进方法来确定哪些参数组合应该调整，以及如何调整，以实现期望的VALI降低。这项工作将超越先前的尝试，通过拟议的模拟平台的更高保真度来量化机械通风过程中肺损伤的风险因素。此外，我们将把稳健性分析技术应用到建模中，以提高我们研究结果的可靠性和适用性。这将允许我们进行人口建模，而不是通常使用的对单个理想化对象进行建模和研究的方法，使我们的发现适用于人群和各种真实的患者，而不是以前的工作，即理想化对象实际上既不代表人群，也不代表任何一个人。

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.