Clinical Implications of Bronchiectasis in Smokers

项目总结/摘要 慢性阻塞性肺疾病（COPD）影响美国约2600万人。它 据估计，这些人中有16%-56%患有支气管扩张症（气道的病理性扩张）。的 COPD中存在支气管扩张与住院时间较长、 恶化和死亡率增加。支气管扩张症的高分辨率计算机视觉评估 断层摄影（CT）扫描是标准的诊断方法。 在这项调查中，视觉评估将用于进一步开发，验证，完善和应用 自动成像工具，使用基线和 来自COPD基因研究（一个大型吸烟者队列，有和没有COPD）的随访胸部CT扫描。在 目标1，我们将对基线和5年随访的一个子集进行支气管扩张的视觉评分和标记- CT扫描。然后，我们将评估基线支气管扩张和急性呼吸道感染之间的关系。 疾病发作、健康相关生活质量和死亡。在aim 2中，我们将执行基于点的（在单个 切片）客观测量支气管扩张和非支气管扩张患者的支气管扩张和气道形态 支气管扩张的气道切片标记为Aim 1，以识别区分支气管扩张的CT特征。我们将 建立受试者工作特征（ROC）曲线，以确定这些目标的预测能力 测量以检测支气管扩张。最后，在目标3中，我们将全面开发、验证和完善 自动化成像工具，其特征被发现对检测目的1和2中的支气管扩张具有区分性。 然后，我们将在所有基线和随访CT扫描中使用该工具确定支气管扩张的负担 （估计N= 16，300），并评估其与临床相关疾病结局（包括急性 呼吸道疾病发作、生活质量和死亡率。我们还将使用ROC分析以及 综合歧视指数（IDI）和净重新分类指数（NRI）措施，以评估 我们的成像工具来预测这些结果。

Project Summary/Abstract Chronic obstructive pulmonary disease (COPD) affects approximately 26 million people in the United States. It is estimated that 16%-56% of these people have bronchiectasis (pathologic dilation of the airways). The presence of bronchiectasis in COPD has been linked to longer hospital stays, prolonged recovery from exacerbations, and increased mortality. The visual assessment of bronchiectasis on high-resolution computed tomography (CT) scans is the standard diagnostic method. In this investigation the visual assessment will be used to further develop, validate, refine, and apply an automated imaging tool to objectively assess the burden of bronchiectasis in smokers using baseline and follow-up chest CT scans from the COPDGene Study (a large cohort of smokers with and without COPD). In Aim 1, we will perform a visual scoring and marking of bronchiectasis on a subset of baseline and 5-yr follow- up CT scans. We will then assess the associations between baseline bronchiectasis and acute respiratory disease episodes, health-related quality of life, and death. In aim 2, we will perform point-based (at a single slice) objective measurements of bronchial dilation and airway morphology in bronchiectatic and non- bronchiectatic airway sections labeled for Aim 1 to identify CT features that distinguish bronchiectasis. We will build receiver operating characteristics (ROC) curves to determine the predictive ability of these objective measurements to detect bronchiectasis. Finally, in Aim 3 we will fully develop, validate, and refine an automated imaging tool with the features found to be discriminatory to detect bronchiectasis in Aims 1 and 2. We will then determine the burden of bronchiectasis using this tool on all baseline and follow-up CT scans (estimated N= 16,300) and assess its association with clinically relevant disease outcomes including acute respiratory disease episodes, quality of life, and mortality. We will also use ROC analysis as well as the integrated discrimination index (IDI) and net reclassification index (NRI) measures to assess the contribution of our imaging tool to predict those outcomes.