The role of wall shear stress in the assessment of right ventricle hydraulic workload

Abstract

Pulmonary hypertension (PH) is a devastating disease affecting approximately 15-50 people per million, with a higher incidence in women. PH mortality is mostly attributed to right ventricle (RV) failure, which results from RV hypotrophy due to an overburdened hydraulic workload. The objective of this study is to correlate wall shear stress (WSS) with hemodynamic metrics that are generally accepted as clinical indicators of RV workload and are well correlated with disease outcome. Retrospective right heart catheterization data for 20 PH patients were analyzed to derive pulmonary vascular resistance (PVR), arterial compliance (C), and an index of wave reflections (Γ). Patient-specific contrast-enhanced computed tomography chest images were used to reconstruct the individual pulmonary arterial trees up to the seventh generation. Computational fluid dynamics analyses simulating blood flow at peak systole were conducted for each vascular model to calculate WSS distributions on the endothelial surface of the pulmonary arteries. WSS was found to be decreased proportionally with elevated PVR and reduced C. Spatially averaged WSS (SAWSS) was positively correlated with PVR (R (2) = 0.66), C (R (2) = 0.73), and Γ (R (2) = 0.5) and also showed promising preliminary correlations with RV geometric characteristics. Evaluating WSS at random cross sections in the proximal vasculature (main, right, and left pulmonary arteries), the type of data that can be acquired from phase-contrast magnetic resonance imaging, did not reveal the same correlations. In conclusion, we found that WSS has the potential to be a viable and clinically useful noninvasive metric of PH disease progression and RV health. Future work should be focused on evaluating whether SAWSS has prognostic value in the management of PH and whether it can be used as a rapid reactivity assessment tool, which would aid in selection of appropriate therapies.

Keywords: computational fluid dynamics; pulmonary hypertension; pulmonary vascular resistance; right ventricle; wall shear stress.

Figure 1

a, Reconstructed pulmonary vasculature with hypothetical structured tree extruding from 3 random outlets (i, j, and k), with calculations of pressure coupled to outlet flow rate (Q_i). MPA: main pulmonary artery; PCWP: pulmonary capillary wedge pressure. b, The patient-specific structured-tree distal resistance at a given outlet, PVR_i, is calculated according to the α and β specific for each patient.

Figure 2

Computational fluid dynamics solutions of wall shear stress (WSS) superimposed on four exemplary pulmonary vasculatures. Generally, higher WSS is seen in the distal vessels, with occasional high stress concentrations at the left/right arterial bifurcations (see d).

Figure 3

Correlations between spatially averaged wall shear stress (SAWSS) and pulmonary vascular resistance (PVR; a), between SAWSS and compliance (C; b), between SAWSS and the index of wave reflections (Γ; c), and between SAWSS and the diastolic midcavitary right ventricle (RV) diameter (d). Note that all correlations satisfied the criterion for statistical significance, P < 0.05.

Figure 4

a, Schematic showing a sample reconstructed pulmonary vasculature, with lines along the endothelium found at the intersection of the main, right, and left pulmonary arteries (MPA, RPA, and LPA, respectively) and three arbitrary planes normal to the centerline of the lumen at these vessels, represented by the green lines. Average wall shear stress (WSS) was measured along these lines (WSS_MPA, WSS_RPA, and WSS_LPA), which is the typical WSS that can be calculated from phase-contrast magnetic resonance imaging (PC-MRI). b, A weak correlation between WSS_MPA (typically available from PC-MRI) and spatially averaged WSS (SAWSS, available from computational fluid dynamics simulations). This weak relationship could suggest that the inconsistency (between patients) in locating the plane of interest for PC-MRI could make the imaging technique unsatisfactory for approximating patient-specific pulmonary endothelium WSS distribution. c, A weak correlation between WSS_MPA and pulmonary vascular resistance (PVR) suggests that approximating WSS from PC-MRI might not be accurate for predicting right ventricular afterload. d, A strong correlation between WSS_LPA and WSS_RPA values suggests a consistent flow distribution in the vasculature and a relatively similar velocity profile downstream of the bifurcation. Note that all correlations satisfied the criterion for statistical significance, P < 0.05.

Figure 5

Impact of vascular reconstruction on the computational fluid dynamics solution. a, Wall shear stress (WSS) distribution on the same vasculature reconstructed to the third (right) and seventh (left) generations. Regions of stress concentrations in the proximal vasculature are common between the two models, but with a spatially averaged WSS difference of 14%. b, The full model calculates a more physiological flow split (52% to the right lung and 48% to the left lung), while the truncated model likely overestimates perfusion into the right lung (63% vs. 37% to the left lung). c, All patient-specific models in this study were reconstructed to the limit of image resolution (ca. seventh generation), leading to a 51.0% ± 6.7% (indicated by the horizontal dotted line) flow split between the two lungs. SD: standard deviation.