Growth and remodeling in the pulmonary autograft: Computational evaluation using kinematic growth models and constrained mixture theory

Constitutive neural networks for main pulmonary arteries: discovering the undiscovered

Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross procedure, using neural networks to discover the most suitable material model. The Ross procedure is a complex cardiac surgery where the patient's own pulmonary valve is used to replace the diseased aortic valve. Ensuring the successful long-term outcomes of this intervention requires a detailed understanding of the mechanical properties of pulmonary tissue. Constitutive artificial neural networks offer a novel approach to capture such complex stress-strain relationships. Here, we design and train different constitutive neural networks to characterize the hyperelastic, anisotropic behavior of the main pulmonary artery. Informed by experimental biaxial testing data under various axial-circumferential loading ratios, these networks autonomously discover the inherent material behavior, without the limitations of predefined mathematical models. We regularize the model discovery using cross-sample feature selection and explore its sensitivity to the collagen fiber distribution. Strikingly, we uniformly discover an isotropic exponential first-invariant term and an anisotropic quadratic fifth-invariant term. We show that constitutive models with both these terms can reliably predict arterial responses under diverse loading conditions. Our results provide crucial improvements in experimental data agreement, and enhance our understanding into the biomechanical properties of pulmonary tissue. The model outcomes can be used in a variety of computational frameworks of autograft adaptation, ultimately improving the surgical outcomes after the Ross procedure.

Cell signaling and tissue remodeling in the pulmonary autograft after the Ross procedure: A computational study

In the Ross procedure, a patient's pulmonary valve is transplanted in the aortic position. Despite advantages of this surgery, reoperation is still needed in many cases due to excessive dilatation of the pulmonary autograft. To further understand the failure mechanisms, we propose a multiscale model predicting adaptive processes in the autograft at the cell and tissue scale. The cell-scale model consists of a network model, that includes important signaling pathways and relations between relevant transcription factors and their target genes. The resulting gene activity leads to changes in the mechanical properties of the tissue, modeled as a constrained mixture of collagen, elastin and smooth muscle. The multiscale model is calibrated with findings from experiments in which seven sheep underwent the Ross procedure. The model is then validated against a different set of sheep experiments, for which a qualitative agreement between model and experiment is found. Model outcomes at the cell scale, including the activity of genes and transcription factors, also match experimentally obtained transcriptomics data.

Hemodynamics and wall shear metrics in a pulmonary autograft: Comparing a fluid-structure interaction and computational fluid dynamics approach

OBJECTIVE

In young patients, aortic valve disease is often treated by placement of a pulmonary autograft (PA) which adapts to its new environment through growth and remodeling. To better understand the hemodynamic forces acting on the highly distensible PA in the acute phase after surgery, we developed a fluid-structure interaction (FSI) framework and comprehensively compared hemodynamics and wall shear-stress (WSS) metrics with a computational fluid dynamic (CFD) simulation.

METHODS

The FSI framework couples a prestressed non-linear hyperelastic arterial tissue model with a fluid model using the in-house coupling code CoCoNuT. Geometry, material parameters and boundary conditions are based on in-vivo measurements. Hemodynamics, time-averaged WSS (TAWSS), oscillatory shear index (OSI) and topological shear variation index (TSVI) are evaluated qualitatively and quantitatively for 3 different sheeps.

RESULTS

Despite systolic-to-diastolic volumetric changes of the PA in the order of 20 %, the point-by-point correlation of TAWSS and OSI obtained through CFD and FSI remains high (r > 0.9, p < 0.01) for TAWSS and (r > 0.8, p < 0.01) for OSI). Instantaneous WSS divergence patterns qualitatively preserve similarities, but large deformations of the PA leads to a decrease of the correlation between FSI and CFD resolved TSVI (r < 0.7, p < 0.01). Moderate co-localization between FSI and CFD is observed for low thresholds of TAWSS and high thresholds of OSI and TSVI.

CONCLUSION

FSI might be warranted if we were to use the TSVI as a mechano-biological driver for growth and remodeling of PA due to varying intra-vascular flow structures and near wall hemodynamics because of the large expansion of the PA.

Multi-scaled temporal modeling of cardiovascular disease progression: An illustration of proximal arteries in pulmonary hypertension

The progression of cardiovascular disease is intricately influenced by a complex interplay between physiological pathways, biochemical processes, and physical mechanisms. This study aimed to develop an in-silico physics-based approach to comprehensively model the multifaceted vascular pathophysiological adaptations. This approach focused on capturing the progression of proximal pulmonary arterial hypertension, which is significantly associated with the irreversible degradation of arterial walls and compensatory stress-induced growth and remodeling. This study incorporated critical characteristics related to the distinct time scales for the deformation, thus reflecting the impact of mean pressure on artery growth and tissue damage. The in-silico simulation of the progression of pulmonary hypertension was realized based on computational code combined with the finite element method (FEM) for the simulation of disease progression. The parametric studies further explored the consequences of these irreversible processes. This computational modeling approach may advance our understanding of pulmonary hypertension and its progression.

Multimodal experimental studies of the passive mechanical behavior of human aortas: Current approaches and future directions

Cardiovascular diseases are the leading cause of death worldwide and include, among others, critical conditions of the aortic wall. Importantly, such critical conditions require effective diagnosis and treatment, which are not yet accurate enough. However, they could be significantly strengthened with predictive material models of the aortic wall. In particular, such predictive models could support surgical decisions, preoperative planning, and estimation of postoperative tissue remodeling. However, developing a predictive model requires experimental data showing both structural parameters and mechanical behavior. Such experimental data can be obtained using multimodal experiments. This review therefore discusses the current approaches to multimodal experiments. Importantly, the strength of the aortic wall is determined primarily by its passive components, i.e., mainly collagen, elastin, and proteoglycans. Therefore, this review focuses on multimodal experiments that relate the passive mechanical behavior of the human aortic wall to the structure and organization of its passive components. In particular, the multimodal experiments are classified according to the expected results. Multiple examples are provided for each experimental class and summarized with highlighted advantages and disadvantages of the method. Finally, future directions of multimodal experiments are envisioned and evaluated. STATEMENT OF SIGNIFICANCE: Multimodal experiments are innovative approaches that have gained interest very quickly, but also recently. This review presents therefore a first clear summary of groundbreaking research in the field of multimodal experiments. The benefits and limitations of various types of multimodal experiments are thoroughly discussed, and a comprehensive overview of possible results is provided. Although this review focuses on multimodal experiments performed on human aortic tissues, the methods used and described are not limited to human aortic tissues but can be extended to other soft materials.

Computational modeling reveals inflammation-driven dilatation of the pulmonary autograft in aortic position

The pulmonary autograft in the Ross procedure, where the aortic valve is replaced by the patient's own pulmonary valve, is prone to failure due to dilatation. This is likely caused by tissue degradation and maladaptation, triggered by the higher experienced mechanical loads in aortic position. In order to further grasp the causes of dilatation, this study presents a model for tissue growth and remodeling of the pulmonary autograft, using the homogenized constrained mixture theory and equations for immuno- and mechano-mediated mass turnover. The model outcomes, compared to experimental data from an animal model of the pulmonary autograft in aortic position, show that inflammation likely plays an important role in the mass turnover of the tissue constituents and therefore in the autograft dilatation over time. We show a better match and prediction of long-term outcomes assuming immuno-mediated mass turnover, and show that there is no linear correlation between the stress-state of the material and mass production. Therefore, not only mechanobiological homeostatic adaption should be taken into account in the development of growth and remodeling models for arterial tissue in similar applications, but also inflammatory processes.

Understanding Pulmonary Autograft Remodeling After the Ross Procedure: Stick to the Facts

The Ross, or pulmonary autograft, procedure presents a fascinating mechanobiological scenario. Due to the common embryological origin of the aortic and pulmonary root, the conotruncus, several authors have hypothesized that a pulmonary autograft has the innate potential to remodel into an aortic phenotype once exposed to systemic conditions. Most of our understanding of pulmonary autograft mechanobiology stems from the remodeling observed in the arterial wall, rather than the valve, simply because there have been many opportunities to study the walls of dilated autografts explanted at reoperation. While previous histological studies provided important clues on autograft adaptation, a comprehensive understanding of its determinants and underlying mechanisms is needed so that the Ross procedure can become a widely accepted aortic valve substitute in select patients. It is clear that protecting the autograft during the early adaptation phase is crucial to avoid initiating a sequence of pathological remodeling. External support in the freestanding Ross procedure should aim to prevent dilatation while simultaneously promoting remodeling, rather than preventing dilatation at the cost of vascular atrophy. To define the optimal mechanical properties and geometry for external support, the ideal conditions for autograft remodeling and the timeline of mechanical adaptation must be determined. We aimed to rigorously review pulmonary autograft remodeling after the Ross procedure. Starting from the developmental, microstructural and biomechanical differences between the pulmonary artery and aorta, we review autograft mechanobiology in relation to distinct clinical failure mechanisms while aiming to identify unmet clinical needs, gaps in current knowledge and areas for further research. By correlating clinical and experimental observations of autograft remodeling with established principles in cardiovascular mechanobiology, we aim to present an up-to-date overview of all factors involved in extracellular matrix remodeling, their interactions and potential underlying molecular mechanisms.