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.

项目概要 慢性阻塞性肺疾病 (COPD) 的自然病程被称为“事件”的事件所打断。 病情加重，即症状急剧恶化。病情加重是昂贵且繁重的——它们是 与肺功能加速衰退、健康状况受损、住院率增加有关， 死亡率增加。有证据表明，有些人特别容易病情加重，但是 异质性仍然知之甚少。因此，迫切需要更好地描述 COPD 异质性，并尽早识别有这些不良后果风险的群体。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 亚人群，能够及早识别面临不良长期后果风险的亚人群。我们 开发了肺血管修剪、心脏形态、肺气肿亚型的 CT 生物标志物， CT 成像中气道增厚和骨骼肌萎缩。但我们还没有进行整合 对这些生物标志物的分析可以更好地描述同质亚组。我们还开发了一个 贝叶斯轨迹算法结合纵向数据来识别不同的人口亚组 类似的生物标志物模式，同时考虑年龄和烟雾暴露等因素。我们的总体目标 该提案的目的是使用多维轨迹分析来评估新型 CT 生物标志物 恶化风险分层。我们的中心假设是肺的多维轨迹分析 肺外 CT 生物标志物可以识别对病情加重具有潜在易感性的亚组。这 这项工作的基本原理是，通过使用多种 CT 生物标志物识别不同的轨迹子组，我们将 更好地描述慢性阻塞性肺病的异质性，从而实现更早、更精确的风险分层——尤其是在 CT 成像是最可靠的可用数据源的患者，例如患有以下疾病的患者： 接受过肺癌CT筛查。目标 1 侧重于对我们的新型 CT 进行系统评估 使用轨迹分析进行 COPD 恶化风险分层的生物标志物。目标 2 侧重于使用 CT 生物标志物和轨迹分析可识别肺癌筛查队列中的亚组 由于 COPD 恶化而住院的风险。我们认为这种方法是创新的，因为它 将重点从疾病分期转移到识别机制相似的亚组（内型）。意义 这些贡献将有助于更好地理解 CT 评估的心脏异常模式 肺和肺外系统以及这些模式如何在有风险的轨迹亚组中呈现 不良事件。反过来，我们期望这能够更好地检测早期疾病表现和亚型 表征。我们期望这些贡献能够进一步研究机制差异 亚组之间以及通过识别早期阶段来预防代价高昂的紧急事件的方法 高危人群的表现。