Computed tomography for early detection and phenotyping of pulmonary hypertension associated with chronic obstructive pulmonary disease with implications for treatment

Project Summary Pulmonary hypertension (PH) is a heterogeneous disorder characterized by elevated pulmonary artery pressure. PH is a frequent complication of chronic lung diseases, most commonly chronic obstructive pulmonary disease (COPD-PH). Despite significant mortality risk associated with COPD-PH, diagnosis often occurs at an advanced disease stage and there are no approved therapies. Clinical trials evaluating the efficacy of pulmonary arterial hypertension (PAH) therapy in COPD-PH have by-and-large demonstrated no consistent benefit. However, cumulative data suggest a signal towards clinical improvement in certain COPD-PH subgroups. Heterogeneity of patient inclusion in these trials limit understanding of which COPD-PH patients may benefit from repurposing existing PAH therapies. Therefore, efforts to identify earlier disease, sub-phenotype COPD-PH patients and decipher clinical features associated with treatment response are critical to improving outcomes. To date, quantitative data from chest computed tomography (CT), a non-invasive and frequently performed diagnostic test in COPD patients, remains an underutilized resource poised to inform research for this morbid disease. To this end, I propose the study, “Computed tomography for early detection and phenotyping of pulmonary hypertension associated with chronic obstructive pulmonary disease with implications for treatment.” This study includes three foundational steps: 1) identify and validate quantitative CT imaging features for early detection of COPD-PH 2) apply advanced machine learning analytics to identify novel COPD-PH sub-phenotypes that inform mortality risk and 3) associate COPD-PH patient CT characteristics with therapeutic response to pulmonary vasodilator therapy. These aims are directly in line with a major NHLBI research priority, to identify disease sub- phenotypes and patients likely to respond to disease-specific treatments. Completion of this proposal will advance the field of COPD-PH by identifying imaging features predictive of early disease, novel disease sub- phenotypes with differential survival trajectories and CT characteristics predictive of response to the PAH therapy, Tadalafil, which is frequently used off-label in this population. The skills I anticipate gaining through this mentored project across three academic institutions, Brigham and Women’s Hospital/Harvard Medical School, the VA Boston Healthcare System and the T.H. Chan School of Public Health, will advance my career goal of becoming an independent investigator focused on PH phenotyping and clinical trial enrichment.