Predicting and Preventing Ventilator-Induced Lung Injury

项目摘要 急性呼吸窘迫综合征(ARDS)是一种由多种因素引起的快速发作的呼吸衰竭。 从肺炎到败血症。ARDS的影响是巨大的，每年有20多万例 美国的死亡率估计为40%。所有ARDS患者均接受机械通气以克服 肺功能紊乱由肺水肿、表面活性物质失活和肺泡塌陷引起的肺功能紊乱 然而，这种基本的机械通气可通过以下途径导致额外的呼吸机诱导的肺损伤(VILI) 组织过度扩张(肺损伤)，小气道和肺泡周期性塌陷和重新开放(肺不张损伤)， 和炎症效应(生物创伤)。由于VILI是所有ARDS患者的风险因素，也是导致 改善呼吸管理是提高ARDS存活率的关键一步。然而， 由于患者之间的差异，进一步完善呼吸方案以减少VILI具有挑战性 随着ARDS的恶化或缓解，随着时间的推移，肺功能会发生变化。因为这件事-和 患者内部的可变性，对一个人有益的通风可能对另一个人有害。要克服这一点 挑战，我们假设应该使用VILI成本函数来指导通风，该成本函数提供实时 通过描述发生的VILI量来反馈通风安全。我们的研究将定义这样的Vili 成本函数基于肺功能、结构和炎症的变化，这些变化是损伤的结果 通风。使用成本函数作为指导，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。然而，考虑到通风的大量排列 调整这是一种不切实际的方法。相反，我们将开发一个数学模型来预测最优 为每位患者提供呼吸机。这些模拟将通过与实时压力流进行拟合来实现个性化 然后用来找出使VILI成本函数最小化的通风模式。被预测的 然后将应用最安全的通风，并重复该过程以考虑肺功能的变化 随着时间的推移。拟议研究的潜在好处是巨大的。我们定义的VILI成本函数将 提供通风安全的基本措施。我们优化肺保护的创新方法 使用预测模型的通风可以通过保护受损的肺来降低ARDS死亡率， 同时，减少提供商的工作负载。拟议的系统也代表了一种方式的范式转变 已经建立了通风策略。而不是在动物模型中测试策略，然后在 不同的ARDS患者群体，其影响可能对某些患者有利，而对 其他方面，重点可能是识别独立于ARDS预测和预防VILI的算法 表型和肺机械功能。

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.