Predicting and Preventing Ventilator-Induced Lung Injury

预测和预防呼吸机引起的肺损伤

• 批准号: 10543770
• 负责人: Bradford J Smith
• 金额: $ 56.9万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10543770
• 关键词: Acute; Acute Respiratory Distress Syndrome; Affect; Algorithms; Alveolar; Alveolus; Animal Model; Biochemical; Biological Markers; Blood capillaries; COVID-19; Caring; Clinical; Complex; Computer Models; Conflict (Psychology); Data; Feedback; Functional disorder; Future; Gases; Heterogeneity; Hospital Mortality; Hospitals; Human Resources; Individual; Inflammation; Inflammatory; Injury; Lung; Lung Compliance; Manuals; Measurement; Measures; Mechanical ventilation; Mechanics; Modeling; Monitor; Mus; Outcome; Pathogenesis; Patients; Pattern; Periodicity; Persons; Phenotype; Physiology; Pneumonia; Process; Protocols documentation; Provider; Pulmonary Edema; Resources; Respiratory Failure; Risk; Risk Factors; Rodent; Safety; Sepsis; Stretching; Structure; System; Testing; Thinness; Time; Tissues; Training; Trauma; United States; Ventilator; Ventilator-induced lung injury; Volutrauma; Workload; atelectrauma; cost; experience; improved; in silico; innovation; lung injury; mathematical model; mechanical force; mortality; pandemic disease; patient population; patient variability; personalized approach; pharmacologic; prediction algorithm; predictive modeling; pressure; prevent; prospective; pulmonary function; real time model; research clinical testing; respiratory; response; simulation; skills; surfactant; temporal measurement; tool; ventilation

Project Summary Acute respiratory distress syndrome (ARDS) is a rapid onset respiratory failure that is caused by factors ranging from pneumonia to sepsis. The impact of ARDS is substantial with more than 200,000 cases per year in the United States and an estimated mortality rate of 40%. All ARDS patients are mechanically ventilated to overcome the derangements in lung function caused by pulmonary edema, surfactant inactivation, and alveolar collapse. However, this essential mechanical ventilation can cause additional ventilator-induced lung injured (VILI) through tissue overdistension (volutrauma), the cyclic collapse and reopening of small airways and alveoli (atelectrauma), and inflammatory effects (biotrauma). Since VILI is a risk in all ARDS patients, and a significant contributor to ARDS mortality, improvements in ventilatory management are a key step in improving ARDS survival. However, further refinement of ventilation protocols to reduce VILI is challenging because of differences between patients and the changes in lung function that occur over time as ARDS worsens or resolves. Because of this inter- and intra-patient variability, ventilation that is beneficial in one person can be harmful in another. To overcome this challenge, we postulate that ventilation should be guided using a VILI cost function that provides real-time feedback of ventilation safety by describing the amount of VILI that is occurring. Our study will define such VILI cost functions based on the changes in lung function, structure, and inflammation that are the result of injurious ventilation. Using the cost function as a guide, the optimally safe ventilation for each patient could be determined by manually adjusting the ventilator settings. However, given the large number of permutations of ventilation adjustments this is not a practical approach. Instead, we will develop a mathematical model to predict optimal ventilation for each patient. These simulations will be personalized by fitting to real time pressure-flow measurements and then used to find the ventilation pattern that minimizes the VILI Cost Function. The predicted optimally safe ventilation will then be applied, and the process repeated to account for changes in lung function over time. The potential benefits of the proposed study are substantial. The VILI cost functions we define will provide an essential measurement of ventilation safety. Our innovative approach to optimize lung-protective ventilation using predictive models may lead to decreased ARDS mortality by protecting the injured lung while, at the same time, reducing provider workload. The proposed system also represents a paradigm shift in the way that ventilation strategies are established. Instead of testing a strategy in animal models and then in the heterogeneous ARDS patient population, where the effect may be beneficial to some patients and harmful to others, focus may be directed towards identifying algorithms that predict and prevent VILI independent of ARDS phenotype and lung mechanical function.

项目摘要 急性呼吸窘迫综合征（ARDS）是一种快速发作呼吸衰竭，是由因素范围造成的 从肺炎到败血症。 ARDS的影响很大，每年超过200,000例 美国，估计死亡率为40％。所有ARDS患者均已机械通风以克服 由肺水肿，表面活性剂灭活和肺泡塌陷引起的肺功能的扰动。 但是，这种必不可少的机械通气可以通过 组织过距（Volutrauma），小气道和肺泡（atelectrauma）的环状崩溃和重新开放， 和炎症效应（生物瘤）。由于Vili在所有ARDS患者中都是风险，并且是 ARDS死亡率，改善通风管理是改善ARDS生存的关键步骤。然而， 由于患者之间的差异，对减少VILI的通风协议的进一步完善是具有挑战性的 随着ARDS恶化或解决方案，随着时间的推移发生的肺功能变化。因为这个间和 患者内变异性，一个人对一个人有益的通风可能在另一个人中有害。克服这一点 挑战，我们假设应使用提供实时的VILI成本功能来指导通风 通过描述正在发生的VILI的数量，对通风安全的反馈。我们的研究将定义这种Vili 成本功能基于肺功能，结构和炎症的变化，这是受害的结果 通风。使用成本功能作为指导，可以确定每个患者的最佳安全通风 通过手动调整呼吸机设置。但是，考虑到大量通风排列 调整这不是一种实际的方法。相反，我们将开发一个数学模型来预测最佳 每个患者的通风。这些模拟将通过拟合实时压力流来个性化 测量，然后用于找到最大程度地减少VILI成本函数的通风模式。预测 然后将应用最佳安全通风，并重复该过程以说明肺功能的变化 随着时间的推移。拟议研究的潜在好处是很大的。我们定义的VILI成本功能将 提供对通风安全性的基本测量。我们优化肺部保护的创新方法 使用预测模型的通风可能会通过保护受伤的肺而导致ARDS死亡率降低，同时 同时，减少提供商的工作量。提出的系统还代表了方式的范式转变 建立了通风策略。而不是在动物模型中测试策略，然后在 异质ARDS患者人群，其影响可能对某些患者有益，对 其他，重点可能是针对识别预测和防止Vili独立于ARDS的算法 表型和肺机械功能。