Risk Stratification for COPD Exacerbations with CT Analysis and Multidimensional Trajectory Subtyping

通过 CT 分析和多维轨迹分型对 COPD 恶化进行风险分层

• 批准号: 10658547
• 负责人: James Ross
• 金额: $ 82.63万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658547
• 关键词: Acceleration; Acute; Age; Aging; Algorithms; Biological Markers; Blood Vessels; Cardiac; Cardiopulmonary; Chronic Obstructive Pulmonary Disease; Clinical; Collection; Complex; Data; Data Sources; Dimensions; Disease; Disease Progression; Early Diagnosis; Early identification; Event; Future; Genetic; Genetic Predisposition to Disease; Goals; Health; Health Status; Heterogeneity; Hospitalization; Image; Impaired health; Impairment; Individual; Intercept; Learning; Lung; Malignant neoplasm of lung; Measures; Modeling; Morphology; Nature; Outcome; Pathologic; Patients; Pattern; Persons; Phenotype; Population; Population Heterogeneity; Populations at Risk; Predisposition; Pulmonary Emphysema; Radiology Specialty; Research; Risk; Scanning; Severity of illness; Smoke; Smoker; Staging; Structural defect; Study Subject; Subgroup; Syndrome; System; Testing; Training; Work; X-Ray Computed Tomography; acute symptom; adverse event risk; adverse outcome; cohort; computed tomography screening; cost; disease heterogeneity; imaging biomarker; improved; innovation; lung cancer screening; mortality; novel; preempt; prevent; pulmonary function; pulmonary function decline; quantitative imaging; response; risk stratification; skeletal muscle wasting; tool

Project Summary The natural disease course of chronic obstructive pulmonary disease (COPD) is punctuated by events, termed exacerbations, when symptoms are acutely worse. Exacerbations are costly and burdensome – they are associated with accelerated lung function decline, impaired health status, increased hospitalization, and increased mortality. Evidence suggests that some individuals are particularly susceptible to exacerbations, but heterogeneity remains poorly understood. There is thus an urgent need to better delineate COPD heterogeneity and improve identification of groups at risk for these adverse outcomes as early as possible. Our long-term goal is to use quantitative imaging and trajectory-based subtype analysis to delineate COPD subpopulations, enabling early identification of subpopulations at risk for adverse, long-term outcomes. We have developed CT biomarkers of pulmonary vascular pruning, cardiac morphology, emphysema subtypes, airway thickening, and skeletal muscle wasting in CT imaging. However, we have not performed an integrative analysis of these biomarkers that could better delineate homogeneous subgroups. We have also developed a Bayesian trajectory algorithm that incorporates longitudinal data to identify distinct population subgroups with similar biomarker patterns while accounting for factors such as age and smoke exposure. Our overall objective in this proposal is to use multidimensional trajectory analysis to evaluate novel CT biomarkers in terms of exacerbation risk-stratification. Our central hypothesis is that multidimensional trajectory analysis of pulmonary and extra-pulmonary CT biomarkers can identify subgroups with latent susceptibility to exacerbations. The rationale for this work is that by identifying distinct trajectory subgroups using multiple CT biomarkers, we will better delineate COPD heterogeneity, leading to earlier, more precise risk-stratification – especially amongst those patients for whom CT imaging is the most reliably available data source, such as those who have undergone lung cancer CT screening. Aim 1 focuses on the methodical assessment of our novel CT biomarkers in terms of COPD exacerbation risk stratification using trajectory analysis. Aim 2 focuses on using CT biomarkers and trajectory analysis to identify subgroups within a lung cancer screening cohort that are at risk for hospitalizations due to COPD exacerbations. The approach is innovative, in our opinion, because it shifts focus from disease staging to identifying mechanistically similar subgroups (endotypes). The significance of these contributions will be an improved understanding of CT-assessed patterns of abnormality in cardio- pulmonary and extra-pulmonary systems and how these patterns present in trajectory subgroups at risk for adverse events. In turn, we expect this to better enable detection of early disease manifestations and subtype characterization. We expect these contributions to enable further studies of the mechanistic differences between subgroups as well approaches to preempt costly acute events by identifying the early-stage manifestations of at-risk groups.

项目摘要 慢性阻塞性肺疾病（COPD）的自然疾病过程被事件打断，称为 恶化，症状严重恶化。 与加速肺功能下降，健康状况受损，Hosspitalization增加有关 增加死亡率。 因此，异质性仍然是糟糕的联盟。 异质性并改善了对这些不良后果的风险的识别，同样是我们的 长期目标是使用定量成像和基于轨迹的亚型分析来描述COPD 子群体，可以早期鉴定出不利的长期结局风险的子 已经开发了肺血管修剪，心脏形态，肺气肿亚型的CT生物标志物， 气道增厚和CT成像中的骨骼肌浪费。 对可以更好地描述同质亚组的这些生物标志物的分析。 贝叶斯轨迹算法结合了纵向数据，以确定不同的种群亚组 同时，同时考虑了我们的整体目标，例如ASGH和烟雾 在这一建议中，对我们进行了多维轨迹分析，以评估新颖的CT生物标志物 我们的中心假设加剧了风险分层。 肺部的CT生物标志物可以识别具有潜在敏感性的亚组 基本原理分叉工作，使用多个CT生物标志物识别不同的轨迹亚组，我们将 更好地描述COPD异质性，导致更早，更精确的风险分层，尤其是在Sttt中 那些CT是最可靠的可用数据源的患者，例如 经历了肺癌CT筛选。 在使用轨迹分析的COPD加剧风险分层方面，生物标志物的重点是使用。 CT生物标志物和轨迹分析，以识别AT AT的肺癌筛查队列中的亚组。 我们认为，由于COPD加重而导致的住院风险。 从疾病分期到识别机械性相似亚组（内型）的转变 这些贡献将改善对心脏异常的CT评估模式的理解。 肺部和肺外系统，以及在轨迹亚组中如何存在的ttert型风险 不良事件，我们希望这能更好地检测早期疾病表现 表征我们预计能够进一步研究机械差异 在亚组之间，通过识别早期阶段来抢占昂贵的急性事件的方法 处于风险群体的表现。