A computational model of contributors to pulmonary hypertensive disease: impacts of whole lung and focal disease distributions

Abstract

Pulmonary hypertension (PH) has multiple etiologies, and so can be difficult to diagnose, prognose, and treat. Diagnosis is typically made via invasive hemodynamic measurements in the main pulmonary artery, and is based on observed elevation of mean pulmonary artery pressure. This static mean pressure enables diagnosis, but does not easily allow assessment of the severity of PH, nor the etiology of the disease, which may impact treatment. Assessment of the dynamic properties of pressure and flow data obtained from catheterization potentially allows more meaningful assessment of the strain on the right heart, and may help to distinguish between disease phenotypes. However, mechanistic understanding of how the distribution of disease in the lung leading to PH impacts the dynamics of blood flow in the main pulmonary artery and/or the pulmonary capillaries is lacking. We present a computational model of the pulmonary vasculature, parameterized to characteristic features of pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) to help understand how the two conditions differ in terms of pulmonary vascular response to disease. Our model incorporates key features known to contribute to pulmonary vascular function in health and disease, including anatomical structure and multiple contributions from gravity. The model suggests that dynamic measurements obtained from catheterization potentially distinguish between distal and proximal vasculopathy typical of PAH and CTEPH. However, the model suggests a non-linear relationship between these data and vascular structural changes typical of PAH and CTEPH which may impede analysis of these metrics to distinguish between cohorts.

Subject-specific one-dimensional fluid dynamics model of chronic thromboembolic pulmonary hypertension

“…Colebank et al employed an exponential stiffness model, with values similar to those reported in Fig. 5 a. Ebrahimi et al ( 2021 ) simulated PAH and CTEPH conditions using a transmission-line model of the whole pulmonary circulation. These authors showed that a combination of vascular narrowing and changes in stiffness could replicate flow heterogeneity seen in clinical CTEPH.…”

Simulating Multi-Scale Pulmonary Vascular Function by Coupling Computational Fluid Dynamics With an Anatomic Network Model

“…Network models can simulate remodeling in CTEPH Colebank et al (2021); Ebrahimi et al (2019Ebrahimi et al ( , 2021; Qureshi et al (2014). However, without accurate coupling to macro-vascular models, measurable changes in flow dynamics and shear stress in the largest pulmonary arteries cannot be predicted.…”

“…Previous CFD studies have suggested that the error in predicted spatially averaged WSS from static versus pulsatile simulations is small Kheyfets et al (2015). However, pulsatile simulations in the future would provide important insights into dynamic changes in the circulation, which may play a role in response to disease Ebrahimi et al (2021).…”

“…Several computational models of the branching network of arteries and veins in the pulmonary circulation have been proposed. These range from 3D computational fluid dynamics (CFD) simulations representing the branching network of large arteries Kheyfets et al (2015); Tang et al (2011Tang et al ( , 2012, through to 1-dimensional (1D) network models that aim to capture the distribution of blood flow within the entire pulmonary circulation Clark et al (2011bClark et al ( , 2014; Ebrahimi et al (2021). These models typically target two main areas: 1) wall shear stress (WSS) distribution in pulmonary artery networks which is intimately associated (and correlated) with endothelial dysfunction Kheyfets et al (2015); Tang et al (2012), but that cannot be measured experimentally; 2) the major drivers of pulmonary perfusion distribution in the lung in health and disease, and how local perfusion contributes to ventilation-perfusion matching and gas exchange Clark et al (2014); Kang et al (2018).…”