Prediction of COPD Progression by PRM

项目摘要 慢性阻塞性肺疾病（COPD）是一种高度流行的异质性疾病， 近三千万美国人目前的疾病分期和治疗主要基于肺量测定和临床 特色由于标准表型分型方法的局限性， 可能会表现出截然不同的发展模式小气道疾病（SAD），一种可治疗但隐匿的 在COPD的早期表现中，COPD的主要组分是气流阻塞的重要促成因素。近年来， SAD被认为是肺实质不可逆破坏的前兆，即 肺气肿预测SAD是否以及何时会导致肺气肿的能力将具有直接的临床意义。 影响COPD患者的护理。2012年，我们报告了参数响应图（PRM）分析 当应用于成对的吸气和呼气CT扫描时， 可视化和量化单个COPD患者中"功能性" SAD（fSAD）和肺气肿的程度。 从那时起，我们已经取得了三个关键进展：首先，PRM衍生的fSAD预测肺功能下降， COPD患者和肺气肿发展;第二，我们已经在人肺样本中验证了PRM- 导出的fSAD是小气道狭窄和损失的量度;最后，应用技术捕获 肺内fSAD的区域变化，我们增强了PRM（拓扑PRM [tPRM]）， 与原始PRM概念相比，这是一种敏感的局部疾病严重程度测量方法。基于我们 根据这些发现，我们假设PRM或其高级形式tPRM具有预测长期患者的潜力。 进展本提案的目标是使用基线、5年级和最近可用的10年级COPDGene 通过以下三个特定目的确定PRM预测疾病进展的能力的数据：1）表征 5年和10年期间PRM衍生的fSAD进展模式; 2）确定 由tPRM确定的疾病分布，识别局部肺气肿的区域发作;以及3）应用机器 学习PRM/tPRM和其他临床指标的策略，以开发预测患者疾病的模型 轨迹我们期望PRM指标能够识别出COPD患者更快发病的风险 但利用区域信息和机器学习战略将进一步提高我们的 approach.这种分析的结果既可以确定患者适合更强烈，有针对性的 在疾病的早期阶段进行治疗，并有助于我们了解小气道疾病的进展 和慢性阻塞性肺病的肺气肿。

PROJECT ABSTRACT Chronic obstructive pulmonary disease (COPD) is a highly prevalent and heterogeneous disorder that afflicts nearly 30 million Americans. Current disease staging and therapy is based primarily on spirometry and clinical characteristics. Due to limitations in the standard phenotyping approaches, patients with similarly staged COPD may exhibit strikingly different progression patterns. Small airways disease (SAD), a treatable but occult component of COPD, is a significant contributor to airflow obstruction manifesting early in COPD. In recent years, SAD has been has been implicated as a precursor to the irreversible destruction of lung parenchyma, i.e. emphysema. The ability to predict if and when SAD will lead to emphysema would have an immediate clinical impact on the care of COPD patients. In 2012 we reported on the Parametric Response Map (PRM) analytical technique that when applied to paired inspiratory and expiratory CT scans is capable of simultaneously visualizing and quantifying the extent of “functional” SAD (fSAD) and emphysema in a single COPD patient. Since then we have made three key advances: first, PRM-derived fSAD is predictive of spirometric decline in COPD patients and emphysema development; second, we have validated in human lung samples that PRM- derived fSAD is a measure of small airway narrowing and loss; and finally, applying techniques to capture regional variation of fSAD within the lung, we have enhanced PRM (topological PRM [tPRM]) to provide a more sensitive measure of local disease severity than what is possible with the original PRM concept. Based on our findings, we postulate that PRM, or its advanced form tPRM, has the potential to predict long-term patient progression. The goal of this proposal will be to use baseline, Year 5 and recently available Year 10 COPDGene data to determine the ability of PRM to predict disease progression through three Specific Aims: 1) Characterize PRM-derived fSAD progression patterns over a 5 and 10 year period; 2) Determine how regional differences in disease distribution, as determined by tPRM, identify regional onset of local emphysema; and 3) Apply machine learning strategies to PRM/tPRM and other clinical metrics to develop models that predict patient disease trajectories. It is our expectation that PRM metrics will identify COPD patients at risk for more rapid disease progression but that utilizing regional information and machine learning strategies will further enhance our approach. The results of such analyses could both identify patients appropriate for more intense, targeted therapy at an early disease stage and contribute to our understanding of the progression of small airways disease and emphysema in COPD.