A structure-based multi-scale model evaluation of hemodynamics and the impact of remodelling in pulmonary hypertension

Abstract

Pulmonary hypertension (PH) has various causes and presentations. Therefore, diagnosis, prognosis, and treatment can be challenging. PH reduces quality of life and potentially leads to right heart failure so, effective management is critical. Distinguishing between different PH phenotypes is challenging clinically, since most types share common symptoms but require different treatment strategies. In this study, computational models of the pulmonary vasculature are provided that cover spatial and temporal features relevant to PH. These models aim to better understand the emergence of dysfunction from key types of PH, to contribute to improving diagnosis and management. The first model is a 1D whole organ perfusion model that uses wave-transmission theory to predict dynamic metrics which are essential to analyse the pulmonary system behaviour in both health and disease. The model incorporates the anatomical structure of the lungs and gravitational contributors to perfusion in a single framework. This model is first tested against normal physiological function, and then applied to two common types of PH, pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH), which allows us to predict and estimate the effects of disease distribution on dynamic pulmonary vascular function indicators, which might help with PH diagnosis and distinction. The model predicts that dynamic data acquired from catheterization may be used to discriminate between distal and proximal vasculopathy, which are both common in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension. The model, however, reveals a non-linear association between these data and vascular structural alterations seen in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension, which might make it difficult to compare cohorts using these measures. Then, a second model which incorporates the different spatial scales of the pulmonary circulation is developed. This model couples a 3D computational fluid dynamics model of the main pulmonary arteries with a 1D network model of the pulmonary arteries and a simplified yet physiological acinar structure. The coupled model is used to predict the influence of vascular remodelling and occlusion on both macro-scale functional drivers (wall shear stress) and micro-scale gas exchange contributors. Again, this model is assessed against known properties of physiological function. Distal remodelling is known to be an indicator for advancing proximal vascular remodelling. Therefore, main pulmonary artery (MPA) WSS can provide prognostic information on pulmonary hemodynamics and proximal vascular remodelling. The model has the advantage of simulating WSS in the upper vasculature and is designed to provide a further understanding on the pulmonary vasculature in disease and help with clinical decision making. Overall, this thesis aims to establish computational models that can contribute to a better understanding of PH and the pulmonary circulation. These models can provide predictions non-invasively and even estimates on metrics that can not be measured experimentally such as the wall shear stress using a multi-scale model that encapsulates an anatomical model that takes into account a physiological representation of distal vessels. This model offers a framework to investigate patient status in disease which may provide first steps toward clinical insights for treatment strategy.