Age Dependent Mechanical Ventilator-Induced Inflammation: Modeling & Experiments

年龄依赖性机械呼吸机引起的炎症：建模

基本信息

批准号：
8867114
负责人：
Rebecca Long Heise
金额：
$ 32.45万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-01 至 2017-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8867114
关键词：
Accounting Acute Acute Lung Injury Adult Adult Respiratory Distress Syndrome Age Algorithms Alveolar Alveolus Animal Experiments Animals Asthma Blood Breathing Carbon Dioxide Cell model Cells Clinical Trials Computer Simulation Coupled Couples Data Dependence Differential Equation Diffusion Disease Elderly Elements Endothelial Cells Environment Environmental air flow Exhalation Finite Element Analysis Future Gases Human Hybrids In Vitro Incidence Individual Inflammation Inflammation Mediators Inflammatory Inflammatory Response Link Liquid substance Literature Lung Lung Inflammation Mechanical Ventilators Mechanical ventilation Mechanics Mediating Methods Modeling Movement Multiple Organ Failure Organ Output Oxygen Partial Pressure Patients Physics Play Production Property Protocols documentation Respiratory Failure Respiratory System Respiratory physiology Rodent Signal Transduction Solid Stress Stretching Structure of parenchyma of lung Testing Tidal Volume Tissues Translating Treatment Protocols Validation Ventilator Ventilator-induced lung injury age related aged base cytokine in vivo lung injury macrophage model development mortality multi-scale modeling normal aging preclinical study pressure research study response trial design

项目摘要

DESCRIPTION (provided by applicant): Incidences of respiratory failure in US are about 137-253 per 100,000 US residents with mortality rates of these patients at about 36%-44%. Mechanical ventilation is a method that partially or fully assists patients whose respiratory systems fails to achieve adequate gas exchange function due to acute lung injury (ALI) and various lung and airway disease, such as acute respiratory distress syndrome and asthma. The majority of patients receiving mechanical ventilation are elderly. The mortality rate for patients on mechanical ventilation increases with both the patient's age and the duration of the ventilation. Despite its necessity, mechanical ventilation can itself cause lung injury or ventilatr-associated lung injury (VALI). As individuals age, there are changes in their lung function, e.g. alveolar volumes increase and diffusion rates decrease, which play a key role in their response to mechanical ventilation. These responses may result in harmful inflammation. In order to understand the mechanisms behind ventilator-induced inflammation in an aged lung and to develop age-dependent treatment protocols that minimize the inflammatory response, we will develop a multi- scale hybrid computational model of ventilator-induced inflammation in the lung. This multi-scale hybrid model will be created by linking models at the cellular, tissue and organ level that will be used to determine the levels of inflammation in the lung, with a model for gas exchange. The hybrid model couples finite element modeling of mechanical lung mechanical properties with agent based modeling of inflammation and differential equation modeling of gas exchange. We will experimentally validate the cellular level model by recording macrophage and several pro-inflammatory cytokine levels in stretched non-aged and aged adult lung rodent cells. Organ and tissue level models will be validated with existing data from literature. The cellular level model (discrete method) takes the inputs of the strain environment generated within the tissue by mechanical ventilation and determines the levels of overall inflammation. The organ and tissue level models (continuum methods) will determine these stresses and strains using ventilator parameters. We will use a model of gas exchange (partial differential equations) to quantify changes in the partial pressure of oxygen and carbon dioxide in circulating blood due to mechanical ventilator-induced inflammation. After validation, these models will be combined to create our multi-scale hybrid model for ventilator-induced inflammation. We will validate this model using whole animal experiments, in which aged rodents are mechanically ventilated and the resulting inflammation will be characterized and quantified. After validating the integrated model, the proposed project can be used to investigate several age dependent ventilator protocols that may be developed to minimize inflammation and reducing the likelihood of ventilator induced lung injury.

描述（由申请人提供）：美国呼吸衰竭的发生率约为每10万名美国居民，这些患者死亡率约为36％-44％。机械通气是一种部分或充分帮助呼吸系统由于急性肺损伤（ALI）以及各种肺和气道疾病（例如急性呼吸遇险综合征和哮喘）而无法实现足够气体交换功能的患者。大多数接受机械通气的患者是老年人。患者的机械通气死亡率随患者的年龄和通气持续时间而增加。尽管有必要，机械通气本身可能会导致肺损伤或与通风相关的肺损伤（VALI）。随着个体的年龄，其肺功能发生了变化，例如牙槽量增加，扩散率降低，这在它们对机械通气的反应中起着关键作用。这些反应可能导致有害炎症。为了了解呼吸机诱导的肺部炎症背后的机制，并制定了年龄依赖性的治疗方案，以最大程度地减少炎症反应，我们将开发出呼吸机诱导的肺部炎症的多尺度混合计算模型。这种多尺度混合模型将通过连接细胞，组织和器官水平的模型来创建，该模型将用于确定肺部的炎症水平，并使用用于天然气交换。杂种模型将机械肺机械性能的有限元建模与基于代理的炎症建模和气体交换的微分方程建模。我们将通过记录巨噬细胞和多个促炎性细胞因子水平在拉伸的非老年和老年成年肺啮齿动物细胞中验证细胞水平模型。器官和组织水平模型将通过文献中的现有数据验证。细胞水平模型（离散方法）通过机械通气来采用组织内产生的应变环境的输入，并确定总体炎症的水平。器官和组织水平模型（连续方法）将使用呼吸机参数确定这些应力和菌株。我们将使用气体交换模型（部分微分方程）来量化由于机械呼吸机诱导的炎症而导致的循环血液中氧和二氧化碳的部分压力的变化。验证后，这些模型将组合起来，以创建我们用于呼吸机诱导炎症的多尺度混合模型。我们将使用全动物实验验证该模型，其中年龄啮齿动物被机械通风，并将表征和定量所产生的炎症。在验证了集成模型之后，该提出的项目可用于研究几种依赖年龄的呼吸机协议，这些方案可能被开发以最大程度地减少炎症并降低呼吸机诱导的肺损伤的可能性。