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

项目摘要 慢性阻塞性肺疾病（COPD）的自然病程被称为 急性加重，症状急剧恶化。急性加重是昂贵的和沉重的-他们是 与肺功能加速下降、健康状况受损、住院治疗增加以及 增加死亡率。有证据表明，有些人特别容易恶化，但 异质性仍然知之甚少。因此，迫切需要更好地描述COPD 异质性，并尽早改善对这些不良结局风险群体的识别。我们 长期目标是使用定量成像和基于自动分类的亚型分析来描述COPD 亚群，使早期识别亚群的风险，不利的，长期的结果。我们 已经开发出肺血管修剪、心脏形态、肺气肿亚型的CT生物标志物， 气道增厚和骨骼肌萎缩。然而，我们没有进行综合评估。 分析这些生物标志物，可以更好地描绘同质亚组。我们还开发了一个 贝叶斯轨迹算法，结合纵向数据，以确定不同的人口亚组， 类似的生物标志物模式，同时考虑年龄和烟雾暴露等因素。我们的整体目标 在这个建议中，使用多维轨迹分析来评估新的CT生物标志物， 加重风险分层。我们的中心假设是肺动脉高压的多维轨迹分析 肺外CT生物标记物可以识别潜在的急性加重易感性亚组。的 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亚组，我们将 更好地描述COPD异质性，从而更早、更精确地进行风险分层，尤其是在 CT成像是最可靠的可用数据源的患者，例如 肺癌CT筛查目的1侧重于我们的新型CT的系统评估 使用轨迹分析的COPD急性加重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，以确定肺癌筛查队列中的亚组， COPD急性加重导致住院的风险。我们认为，这种做法是创新的，因为它 将焦点从疾病分期转移到识别机制相似的亚组（内型）。意义 这些贡献将是对CT评估的心脏异常模式的更好理解， 肺和肺外系统，以及这些模式如何出现在有风险的轨迹亚组中 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 特征化我们希望这些贡献，使进一步研究的机制差异 以及通过识别早期阶段， 高危人群的表现。

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.