Pulmonary arterial hypertension (PAH) imposes pressure overload on the right ventricle (RV), leading to RV enlargement via the growth of cardiac myocytes and remodeling of the collagen fiber architecture. The effects of these alterations on the functional behavior of the right ventricular free wall (RVFW) and organ-level cardiac function remain largely unexplored. Computational heart models in the rat (RHMs) of the normal and hypertensive states can be quite valuable in simulating the effects of PAH on cardiac function to gain insights into the pathophysiology of underlying myocardium remodeling. We thus developed high-fidelity biventricular finite-element RHMs for the normal and post-PAH hypertensive states using extensive experimental data collected from rat hearts. We then applied the RHM to investigate the transmural nature of RVFW remodeling and its connection to wall stress elevation under PAH. We found a strong correlation between the longitudinally-dominated fiber-level adaptation of the RVFW and the transmural alterations of relevant wall stress components. We further conducted several numerical experiments to gain new insights on how the RV responds both normally and in the post-PAH state. We found that the effect of pressure overload alone on the increased contractility of the RV is secondary to the effects of changes in the RV geometry and stiffness. Furthermore, our RHMs provided fresh perspectives on long-standing questions of the functional role of the interventricular septum in RV function. Specifically, we demonstrated that an inaccurate identification of the mechanical adaptation of the septum can lead to a significant underestimation of RVFW contractility in the post-PAH state. These findings showed that how integrated experimental-computational models can facilitate a more comprehensive understanding of the cardiac remodeling events during PAH.
Pulmonary arterial hypertension (PAH) exerts substantial pressure overload on the right ventricle (RV), inducing RV remodeling and myocardial tissue adaptation often leading to right heart failure. The associated RV free wall (RVFW) adaptation involves myocardial hypertrophy, augmented intrinsic contractility, collagen fibrosis, and structural remodeling in an attempt to cope with pressure overload. If RVFW adaptation cannot maintain the RV stroke volume (SV), RV dilation will prevail as an exit mechanism, which usually decompensates RV function, leading to RV failure. Our knowledge of the factors determining the transition from the upper limit of RVFW adaptation to RV decompensation and the role of fiber remodeling events such as extracellular fibrosis and fiber reorientation in this transition remains very limited. Computational heart models that connect the growth and remodeling (G&R) events at the fiber and tissue levels with alterations in the organ-level function are essential to predict the temporal order and the compensatory level of the underlying mechanisms. In this work, building upon our recently developed rodent heart models (RHM) of PAH, we integrated mathematical models that describe volumetric growth of the RV and structural remodeling of the RVFW. The time-evolution of RV remodeling from control and post-PAH time points was simulated. The results suggest that the augmentation of the intrinsic contractility of myofibers, accompanied by an increase in passive stiffness of RVFW, is among the first remodeling events through which the RV strives to maintain the cardiac output. Interestingly, we found that the observed reorientation of the myofibers toward the longitudinal (apex-to-base) direction was a maladaptive mechanism that impaired the RVFW contractile pattern and advanced along with RV dilation at later stages of PAH. In fact, although individual fibers were more contractile post-PAH, the disruption in the optimal transmural fiber architecture compromised the effective contractile function of the RVFW, contributing to the depressed ejection fraction (EF) of the RV. Our findings clearly demonstrate the critical need for developing multiscale approaches that can model and delineate relationships between pathological alterations in cardiac function and underlying remodeling events across fiber, cellular, and molecular levels.
BACKGROUND: Global indices of right ventricle (RV) function provide limited insights into mechanisms underlying RV remodeling in pulmonary hypertension (PH). While RV myocardial architectural remodeling has been observed in PH, its effect on RV adaptation is poorly understood. METHODS: Hemodynamic assessments were performed in 2 rodent models of PH. RV free wall myoarchitecture was quantified using generalized Q-space imaging and tractography analyses. Computational models were developed to predict RV wall strains. Data from animal studies were analyzed to determine the correlations between hemodynamic measurements, RV strains, and structural measures. RESULTS: In contrast to the PH rats with severe RV maladaptation, PH rats with mild RV maladaptation showed a decrease in helical range of fiber orientation in the RV free wall (139º versus 97º; P =0.029), preserved global circumferential strain, and exhibited less reduction in right ventricular-pulmonary arterial coupling (0.029 versus 0.017 mm/mm Hg; P =0.037). Helical range correlated positively with coupling ( P =0.036) and stroke volume index ( P <0.01). Coupling correlated with global circumferential strain ( P <0.01) and global radial strain ( P <0.01) but not global longitudinal strain. CONCLUSIONS: Data analysis suggests that adaptive RV architectural remodeling could improve RV function in PH. Our findings suggest the need to assess RV architecture within routine screenings of PH patients to improve our understanding of its prognostic and therapeutic significance in PH.
In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters $$a_f$$ a f and $$b_f$$ b f associated with fiber direction ($$R^2_{a_f}=99.471\%$$ R a f 2 = 99.471 % and $$R^2_{b_f}=92.837\%$$ R b f 2 = 92.837 % ). After conducting permutation feature importance analysis, the ML performance further improved for $$b_f$$ b f ($$R^2_{b_f}=96.240\%$$ R b f 2 = 96.240 % ), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases.
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