Development, validation and application of population-based pulmonary disease models using robustness analysis and ensemble forecasting

影响危重病人的肺部疾病(急性呼吸窘迫综合征(ARDS)、肺炎和呼吸机相关性肺损伤)是影响大部分或全部双肺的急性严重损伤。患有这些疾病的患者在气体交换和组织氧合方面存在缺陷，并经常因为呼吸衰竭而需要机械通气(生命支持)。虽然医学研究人员和生理学家多年来一直在研究这些疾病，但在了解其根本原因和机制(特别是ARDS和VALI)方面取得的进展非常有限。由于分子系统生物学在过去十年中取得了爆炸性的发展和公认的成功，许多研究人员现在正试图应用类似的基于系统的计算方法来开发和分析器官水平的生理模拟模型。然而，由于患者和疾病的异质性而产生的不确定性，以及严格验证将这种不确定性考虑在内的模拟模型的困难，是在临床环境中应用系统方法取得进展的严重障碍。我们建议采取一种非常新颖和跨学科的方法来解决这两个问题。利用控制工程领域的方法，我们将开发基于人群的疾病模型，明确考虑单个患者内部和患者群体不同成员之间的不确定性和可变性，并使用先进的稳健性分析技术严格验证这些模型。通过采用气候预测领域的整体建模技术，我们将开发适用于单个患者群体的可靠的疾病严重程度描述符和新的疾病特定治疗策略，而不是单一的“典型”患者。通过将最先进的技术从控制工程和气候科学转化为重症监护医学，该项目将显著改善对ARDS、肺炎和VALI的理解、诊断和治疗，并相应减少它们对患者的影响和NHS的相关成本。

Lung diseases affecting the critically ill (acute respiratory distress syndrome (ARDS), pneumonia and ventilator-associated lung injury) are acute, severe injuries affecting most or all of both lungs. Patients with these conditions experience defects in gas-exchange and tissue oxygenation and often require mechanical ventilation (life support) because of respiratory failure. While medical researchers and physiologists have been studying these disease for many years, very limited progress has been made in understanding the underlying causes and mechanisms (especially of ARDS and VALI). As a result of the explosive developments and demonstrated successes of molecular systems biology in the last decade, many researchers are now attempting to apply similar systems-based computational approaches to develop and analyse physiological simulation models at the organ level. However, the uncertainty that arises due to patient and disease heterogeneity, and the difficulty in rigorously validating simulation models which take this uncertainty into account, represent serious roadblocks to progress in applying systems approaches in a clinical setting. We propose to tackle both of these problems by adopting a highly novel and interdisciplinary approach. Using approaches from the field of Control Engineering, we will develop population-based disease models which explicitly take account of uncertainty and variability both within a single patient and between diverse members of a patient population, and rigorously validate these models using advanced robustness analysis techniques. By adapting ensemble modelling techniques from the field of climate forecasting, we will develop reliable descriptors of disease severity and novel disease-specific therapeutic strategies that are applicable to populations of individual patients, rather than a single "typical" patient. By transferring state-of-the-art technologies from control engineering and climate science into critical-care medicine, this project will deliver a significant improvement in the understanding, diagnosis and treatment of ARDS, pneumonia and VALI, and a corresponding reduction in both their impact on patients and the associated cost to the NHS.