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

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $802,927

## Abstract

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 mechanist...

## Key facts

- **NIH application ID:** 10823300
- **Project number:** 5R01HL164380-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** James Ross
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $802,927
- **Award type:** 5
- **Project period:** 2023-04-15 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10823300

## Citation

> US National Institutes of Health, RePORTER application 10823300, Risk Stratification for COPD Exacerbations with CT Analysis and Multidimensional Trajectory Subtyping (5R01HL164380-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10823300. Licensed CC0.

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