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Development of Quantitative Phenotyping Tools to Improve Pulmonary Thromboendarterectomy Risk-Benefit Assessment in Patients with Chronic Thromboembolic Pulmonary Hypertension
- Bird, Elizabeth
- Advisor(s): Contijoch, Francisco
Abstract
Chronic Thromboembolic Pulmonary Hypertension (CTEPH) is a disease where patients have unresolved clots in their pulmonary vasculature. Patients can be successfully treated by Pulmonary Thromboendarterectomy (PTE) when the pulmonary hemodynamic impairment is due to surgically accessible CTEPH disease burden (fibrotic clot and resulting downstream perfusion deficits). Unfortunately, up to 10 - 50% of patients who undergo the surgery will have residual pulmonary hypertension, which carries higher complication and mortality rates. Residual pulmonary hypertension is thought to occur in patients with either microvascular remodeling, mixed pulmonary hypertension types, or chronic clot too distal in the vasculature for complete surgical removal. As such, key preoperative questions to identify patients likely to benefit from PTE are (1) the extent to which their vascular obstructions and associated perfusion deficits are the drivers of hemodynamic impairment and (2) whether the obstructions are surgically accessible for removal (aka located in lobar to sub- segmental vessels).Both of the above questions are currently assessed by expert visual interpretation of imaging studies alongside hemodynamic measures prior to PTE surgery. Unfortunately, there are currently no quantitative tools to aid these evaluations. As a result, the impact of specific lesions, the total disease burden, and the amount of surgically-accessible disease on PTE risk-benefit determination is unclear. Thus, there is a need to develop tools to systematically and objectively assess CTEPH patients being evaluated for PTE surgery. This work aims to develop quantitative imaging measures to assess CTEPH characteristics important for surgical planning. To accomplish this, first we focus on the ability of Dual Energy CT Pulmonary Angiograms to evaluate agreement between CTEPH disease burden and hemodynamic severity. Next, we evaluate the ability of Machine Learning Analysis of Ventilation Perfusion Images predict surgical disease level, an indication of clot location with important accessibility implications.
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