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:
- 金额:$ 82.63万
- 依托单位:
- 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:
- 关键词:AccelerationAcuteAgeAgingAlgorithmsBiological MarkersBlood VesselsCardiacCardiopulmonaryChronic Obstructive Pulmonary DiseaseClinicalCollectionComplexDataData SourcesDimensionsDiseaseDisease ProgressionEarly DiagnosisEarly identificationEventFutureGeneticGenetic Predisposition to DiseaseGoalsHealthHealth StatusHeterogeneityHospitalizationImageImpaired healthImpairmentIndividualInterceptLearningLungMalignant neoplasm of lungMeasuresModelingMorphologyNatureOutcomePathologicPatientsPatternPersonsPhenotypePopulationPopulation HeterogeneityPopulations at RiskPredispositionPulmonary EmphysemaRadiology SpecialtyResearchRiskScanningSeverity of illnessSmokeSmokerStagingStructural defectStudy SubjectSubgroupSyndromeSystemTestingTrainingWorkX-Ray Computed Tomographyacute symptomadverse event riskadverse outcomecohortcomputed tomography screeningcostdisease heterogeneityimaging biomarkerimprovedinnovationlung cancer screeningmortalitynovelpreemptpreventpulmonary functionpulmonary function declinequantitative imagingresponserisk stratificationskeletal muscle wastingtool
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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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James Ross其他文献
James Ross的其他文献
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{{ truncateString('James Ross', 18)}}的其他基金
Machine Learning Development for Subtyping COPD
用于 COPD 分型的机器学习开发
- 批准号:
9316700 - 财政年份:2016
- 资助金额:
$ 82.63万 - 项目类别:
An Integrated Platform for In Vivo Neuromuscular Stimulation and Recording Using
体内神经肌肉刺激和记录的集成平台
- 批准号:
8326607 - 财政年份:2011
- 资助金额:
$ 82.63万 - 项目类别:
An Integrated Platform for In Vivo Neuromuscular Stimulation and Recording Using
体内神经肌肉刺激和记录的集成平台
- 批准号:
8058139 - 财政年份:2011
- 资助金额:
$ 82.63万 - 项目类别:
A Microneedle Array System for Transcutaneous Nerve Mapping
用于经皮神经标测的微针阵列系统
- 批准号:
8647434 - 财政年份:2009
- 资助金额:
$ 82.63万 - 项目类别:
A Microneedle Array System for Transcutaneous Nerve Mapping
用于经皮神经标测的微针阵列系统
- 批准号:
7747062 - 财政年份:2009
- 资助金额:
$ 82.63万 - 项目类别:
A Microneedle Array System for Transcutaneous Nerve Mapping
用于经皮神经标测的微针阵列系统
- 批准号:
8787158 - 财政年份:2009
- 资助金额:
$ 82.63万 - 项目类别:
A Microneedle Array System for Transcutaneous Nerve Mapping
用于经皮神经标测的微针阵列系统
- 批准号:
8986824 - 财政年份:2009
- 资助金额:
$ 82.63万 - 项目类别:
Simultaneous Stimulation and Recording in Scalable Multielectrode Arrays
可扩展多电极阵列中的同步刺激和记录
- 批准号:
7651158 - 财政年份:2008
- 资助金额:
$ 82.63万 - 项目类别:
An Automated Platform for High-throughput Network Electrophysiology
高通量网络电生理学自动化平台
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8696889 - 财政年份:2008
- 资助金额:
$ 82.63万 - 项目类别:
Simultaneous Stimulation and Recording in Scalable Microelectrode Arrays
可扩展微电极阵列中的同步刺激和记录
- 批准号:
8058252 - 财政年份:2008
- 资助金额:
$ 82.63万 - 项目类别:
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