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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

基本信息

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
项目类别：
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

项目摘要

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.

项目总结