Effects of Stent Implantation on Pulmonary Artery Stenosis: Insights from Subject-Specific Computational Modeling

Introduction: : Pulmonary artery stenosis is a challenging problem in the management of heart disease. Untreated pulmonary artery stenosis may contribute to increased mortality and morbidity, leading to right heart failure [1]. Intravascular stent implantation has emerged as one of the preferred treatment options for pulmonary artery stenosis by reducing the right ventricular (RV) afterload. However, issues regarding effects of stent implantation on RV mechanics (e.g., active contraction) remain unclear [2]. It is difficult, if not impossible, to quantify the effects of stent implantation on RV mechanics solely relying on clinical or experimental studies. To address these limitations, computational models have been developed. Existing computational models have used simplified representations of heart [3], characterized disease states without simulating interventions [4] or are lack of direct interventional validation [5]. Here, a subject-specific computational model that integrates finite element (FE) biventricular mechanics model in a closed-loop lumped parameter circulatory model of systemic and pulmonary circulations is developed. Calibrated and validated by the hemodynamics measurements from a dog model with pulmonary artery stenosis, the computer model is applied to simulate the effects of stent implantation on RV mechanics, providing unparalleled mechanistic insight into the intervention's effects.

Materials and

Methods: : Based on our previous work [6,7], the computational framework that consists of a biventricular FE model in a closed-loop lumped representation of the systemic and pulmonary circulations is developed (Fig. 1). Subject-specific biventricular geometries were reconstructed based on CT images from dogs pre- and post-stent implantation. The pressure-volume (PV) relationships of the left ventricle (LV) and RV are described based on the biventricular FE model, which can be generalized by minimizing the Lagrangian function.

In the lumped model, the change of volume of each component depends on the blood flow rate difference between two adjacent vascular components. Blood flow rates in the arteries and veins are determined by each segment’s resistance and the pressure difference between the two connecting compartments. Pressures in the compartments representing the peripheral arterial and venous networks are determined by their resting volume and compliance. The pump characteristics of the right and left atriums are represented by a time-varying elastance model.

In simulation cases, the developed model is calibrated based on the patient-specific measurements of RV and pulmonary artery (PA) pressure obtained from right heart catheterization. The validated model is then applied to simulate the effects of stent implantation by varying specific parameters in the lumped-parameter model, including a reduction of pulmonary outflow resistance (Rpvv) and pulmonary vascular resistance (Rpa), while an increase of pulmonary arterial compliance (Cpa) to represent the widened, less resistive, and more compliant stented vessel segment. Critically, both LV and RV contractility were reduced to align with the measured post-stent hemodynamics.

Results, Conclusions, and Discussions::

Results: In the pre-stent state, the subject-specific FE model closely reproduced the measurements. Predicted peak LV pressure was 85 mmHg (Fig. 2a); peak RV systolic pressure was 115 mmHg, matching the measured 113 mmHg; and mean pulmonary-artery pressure (mPAP) was 15 mmHg versus 14 mmHg measured (Fig. 2b). Predicted volumes yielded an RV EDV of 36 mL (EF 28 %) and an LV EDV of 12 mL (EF 46 %) (Fig. 2c). End-systolic myocardial stress peaked at 120 kPa and wall displacement at 0.54 cm, both localized to the RV free wall (Fig. 2d–e). Virtual stenting reproduced the observed hemodynamic relief. Peak RV systolic pressure fell to 18 mmHg (19 mmHg measured) while LV pressure remained 85 mmHg (Fig. 3a–b). The RV pressure–volume loop shifted leftward and downward into the physiological range, and mPAP held at 14 mmHg, matching the measurements (Fig. 3b–c). Tissue-level unloading paralleled these gains: peak myocardial stress dropped by 90 % to 12 kPa, became more uniform, and maximal wall displacement decreased to 0.34 cm (Fig. 3d–e).

Conclusions: Relative to the pre-stent state, pulmonary-artery stenting (i) lowers peak RV systolic pressure by 86 % and shifts the RV pressure–volume loop into a physiological window (Fig. 2c vs. 3c); (ii) reduces peak RV wall stress by 90 %, homogenizing the stress field and limiting deformation (Fig. 2d–e vs. 3d–e); and (iii) necessitates concurrent reductions in LV and RV contractility, indicating rapid biventricular adaptation to the relieved afterload. This biventricular FE framework offers a robust, clinically anchored platform for in-silico optimization of PA interventions.

Discussions: The significance of this study lies in the rigorous validation of a closed-loop biventricular FE model against paired pre- and post-intervention hemodynamic data together with corresponding regional mechanics in the same subject. It advances beyond earlier work limited to pathophysiology [4] or lumped-parameter models [3]. The validated model establishes a mechanistic bridge between stent-induced parameter changes and their hemodynamic consequences, as underscored by the regional stress reduction (Fig. 2d vs. 3d). The requirement to retune both ventricular contractilities emphasizes the strength of ventricular interdependence, a feature often obscured in simplified representations [8].

Acknowledgements and/or References (Optional)::

References:
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