Predicting and Preventing Ventilator-Induced Lung Injury

项目摘要 急性呼吸窘迫综合征（ARDS）是一种快速发作的呼吸衰竭， 从肺炎到败血症急性呼吸窘迫综合征的影响是巨大的，在美国每年有超过20万例病例。 美国，估计死亡率为40%。所有的ARDS患者都是机械通气，以克服 由肺水肿、表面活性剂失活和肺泡萎陷引起的肺功能紊乱。 然而，这种必要的机械通气可能会导致额外的呼吸机诱导的肺损伤（VILI）， 组织过度膨胀（体积创伤），小气道和肺泡的周期性塌陷和重新开放（肺创伤不全）， 和炎症效应（生物创伤）。由于VILI是所有ARDS患者的一种风险，并且是 提高ARDS死亡率，改善辅助治疗是提高ARDS生存率的关键。然而，在这方面， 由于患者之间的差异，进一步完善通气方案以减少VILI具有挑战性 以及随着时间的推移发生的肺功能的变化，如ARDS恶化或消退。由于这种内部和 患者内的可变性，对一个人有益的通气可能对另一个人有害。为了克服这个 挑战，我们假设应该使用VILI成本函数来指导通气， 通过描述发生的VILI的量来反馈通气安全性。我们的研究将定义这样的VILI 基于肺功能、结构和炎症的变化的成本函数，这些变化是损伤性肺损伤的结果。 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气 通过手动调节呼吸机设置。然而，考虑到大量的通风排列 这不是一个实际的办法。相反，我们将开发一个数学模型来预测最佳的 为每一位患者提供治疗。这些模拟将通过拟合真实的时间压力-流量来个性化 测量，然后用于找到使VILI成本函数最小化的通风模式。预测 然后应用最佳安全通气，并重复该过程以考虑肺功能的变化 随着时间拟议研究的潜在益处是巨大的。我们定义的VILI成本函数将 提供通风安全的基本测量。我们的创新方法，以优化肺部保护 使用预测模型的通气可以通过保护受伤的肺而导致降低的ARDS死亡率， 同时，减少供应商的工作量。拟议中的系统也代表了一种范式转变， 建立通风策略。而不是在动物模型中测试策略，然后在 异质性ARDS患者人群，其中效果可能对某些患者有益，对某些患者有害。 其他人，焦点可能指向识别预测和预防独立于ARDS的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.