Changes in aorta hemodynamics in Left-Right Type 1 bicuspid aortic valve patients after replacement with bioprosthetic valves: An in-silico study.

Bicuspid aortic valve (BAV) is the most common cardiac congenital abnormality with a high rate of concomitant aortic valve and ascending aorta (AAo) pathologic changes throughout the patient's lifetime. The etiology of BAV-related aortopathy was historically believed to be genetic. However, recent studies theorize that adverse hemodynamics secondary to BAVs also contribute to aortopathy, but their precise role, specifically, that of wall shear stress (WSS) magnitude and directionality remains controversial. Moreover, the primary therapeutic option for BAV patients is aortic valve replacement (AVR), but the role of improved post-AVR hemodynamics on aortopathy progression is also not well-understood. To address these issues, this study employs a computational fluid dynamics model to simulate personalized AAo hemodynamics before and after TAVR for a small cohort of 6 Left-Right fused BAV patients. Regional distributions of five hemodynamic metrics, namely, time-averaged wall shear stress (TAWSS) and oscillating shear index (OSI), divergence of wall shear (DWSS), helicity flux integral & endothelial cell activation potential (ECAP), which are hypothesized to be associated with potential aortic injury are computed in the root, proximal and distal ascending aorta. BAVs are characterized by strong, eccentric jets, with peak velocities exceeding 4 m/s and axially circulating flow away from the jets. Such conditions result in focused WSS loading along jet attachment regions on the lumen boundary and weaker, oscillating WSS on other regions. The jet attachment regions also show alternating streaks of positive and negative DWSS, which may increase risk for local tissue stretching. Large WSS magnitudes, strong helical flows and circumferential WSS have been previously implicated in the progression of BAV aortopathy. Post-intervention hemodynamics exhibit weaker, less eccentric jets. Significant reductions are observed in flow helicity, TAWSS and DWSS in localized regions of the proximal AAo. On the other hand, OSI increases post-intervention and ECAP is observed to be low in both pre- and post-intervention scenarios, although significant increases are also observed in this ECAP. These results indicate a significant alleviation of pathological hemodynamics post AVR.

Smoothed particle hydrodynamics based FSI simulation of the native and mechanical heart valves in a patient-specific aortic model.

The failure of the aortic heart valve is common, resulting in deterioration of the pumping function of the heart. For the end stage valve failure, bi-leaflet mechanical valve (most popular artificial valve) is implanted. However, due to its non-physiological behaviour, a significant alteration is observed in the normal haemodynamics of the aorta. While in-vivo experimentation of a human heart valve (native and artificial) is a formidable task, in-silico study using computational fluid dynamics (CFD) with fluid structure interaction (FSI) is an effective and economic tool for investigating the haemodynamics of natural and artificial heart valves. In the present work, a haemodynamic model of a natural and mechanical heart valve has been developed using meshless particle-based smoothed particle hydrodynamics (SPH). In order to further enhance its clinical relevance, this study employs a patient-specific vascular geometry and presents a successful validation against traditional finite volume method and 4D magnetic resonance imaging (MRI) data. The results have demonstrated that SPH is ideally suited to simulate the heart valve function due to its Lagrangian description of motion, which is a favourable feature for FSI. In addition, a novel methodology for the estimation of the wall shear stress (WSS) and other related haemodynamic parameters have been proposed from the SPH perspective. Finally, a detailed comparison of the haemodynamic parameters has been carried out for both native and mechanical aortic valve, with a particular emphasis on the clinical risks associated with the mechanical valve.

Three-dimensional fluid-structure interaction simulation of the Wheatley aortic valve.

Valvular heart diseases (such as stenosis and regurgitation) are recognized as a rapidly growing cause of global deaths and major contributors to disability. The most effective treatment for these pathologies is the replacement of the natural valve with a prosthetic one. Our work considers an innovative design for prosthetic aortic valves that combines the reliability and durability of artificial valves with the flexibility of tissue valves. It consists of a rigid support and three polymer leaflets which can be cut from an extruded flat sheet, and is referred to hereafter as the Wheatley aortic valve (WAV). As a first step towards the understanding of the mechanical behavior of the WAV, we report here on the implementation of a numerical model built with the ICFD multi-physics solver of the LS-DYNA software. The model is calibrated and validated using data from a basic pulsatile-flow experiment in a water-filled straight tube. Sensitivity to model parameters (contact parameters, mesh size, etc.) and to design parameters (height, material constants) is studied. The numerical data allow us to describe the leaflet motion and the liquid flow in great detail, and to investigate the possible failure modes in cases of unfavorable operational conditions (in particular, if the leaflet height is inadequate). In future work the numerical model developed here will be used to assess the thrombogenic properties of the valve under physiological conditions.

Wall shear stress and pressure patterns in aortic stenosis patients with and without aortic dilation captured by high-performance image-based computational fluid dynamics.

Spatial patterns of elevated wall shear stress and pressure due to blood flow past aortic stenosis (AS) are studied using GPU-accelerated patient-specific computational fluid dynamics. Three cases of moderate to severe AS, one with a dilated ascending aorta and two within the normal range (root diameter less than 4cm) are simulated for physiological waveforms obtained from echocardiography. The computational framework is built based on sharp-interface Immersed Boundary Method, where aortic geometries segmented from CT angiograms are integrated into a high-order incompressible Navier-Stokes solver. The key question addressed here is, given the presence of turbulence due to AS which increases wall shear stress (WSS) levels, why some AS patients undergo much less aortic dilation. Recent case studies of AS have linked the existence of an elevated WSS hotspot (due to impingement of AS on the aortic wall) to the dilation process. Herein we further investigate the WSS distribution for cases with and without dilation to understand the possible hemodynamics which may impact the dilation process. We show that the spatial distribution of elevated WSS is significantly more focused for the case with dilation than those without dilation. We further show that this focal area accommodates a persistent pocket of high pressure, which may have contributed to the dilation process through an increased wall-normal forcing. The cases without dilation, on the contrary, showed a rather oscillatory pressure behaviour, with no persistent pressure "buildup" effect. We further argue that a more proximal branching of the aortic arch could explain the lack of a focal area of elevated WSS and pressure, because it interferes with the impingement process due to fluid suction effects. These phenomena are further illustrated using an idealized aortic geometry. We finally show that a restored inflow eliminates the focal area of elevated WSS and pressure zone from the ascending aorta.

Modeling isovolumetric phases in cardiac flows by an Augmented Resistive Immersed Implicit Surface method.

A major challenge in the computational fluid dynamics modeling of the heart function is the simulation of isovolumetric phases when the hemodynamics problem is driven by a prescribed boundary displacement. During such phases, both atrioventricular and semilunar valves are closed: consequently, the ventricular pressure may not be uniquely defined, and spurious oscillations may arise in numerical simulations. These oscillations can strongly affect valve dynamics models driven by the blood flow, making unlikely to recovering physiological dynamics. Hence, prescribed opening and closing times are usually employed, or the isovolumetric phases are neglected altogether. In this article, we propose a suitable modification of the Resistive Immersed Implicit Surface (RIIS) method (Fedele et al., Biomech Model Mechanobiol 2017, 16, 1779-1803) by introducing a reaction term to correctly capture the pressure transients during isovolumetric phases. The method, that we call Augmented RIIS (ARIIS) method, extends the previously proposed ARIS method (This et al., Int J Numer Methods Biomed Eng 2020, 36, e3223) to the case of a mesh which is not body-fitted to the valves. We test the proposed method on two different benchmark problems, including a new simplified problem that retains all the characteristics of a heart cycle. We apply the ARIIS method to a fluid dynamics simulation of a realistic left heart geometry, and we show that ARIIS allows to correctly simulate isovolumetric phases, differently from standard RIIS method. Finally, we demonstrate that by the new method the cardiac valves can open and close without prescribing any opening/closing times.

Real-Time Seismocardiogram Feature Extraction Using Adaptive Gaussian Mixture Models.

Wearable systems can provide accurate cardiovascular evaluations by estimating hemodynamic indices in real-time. Key hemodynamic parameters can be non-invasively estimated using the seismocardiogram (SCG), a cardiomechanical signal whose features link to cardiac events like aortic valve opening (AO) and closing (AC). However, tracking a single SCG feature is unreliable due to physiological changes, motion artifacts, and external vibrations. This work proposes an adaptable Gaussian Mixture Model (GMM) to track multiple AO/AC correlated features in quasi-real-time from the SCG. The GMM calculates the likelihood of an extremum being an AO/AC feature for each SCG beat. The Dijkstra algorithm selects heartbeat-related extrema, and a Kalman filter updates the GMM parameters while filtering features. Tracking accuracy is tested on a porcine hypovolemia dataset with varying noise levels. Blood volume loss estimation accuracy is also evaluated using the tracked features on a previously developed model. Experimental results show a 4.5 ms tracking latency and average root mean square errors (RMSE) of 1.47 ms for AO and 7.67 ms for AC at 10 dB noise, and 6.18 ms for AO and 15.3 ms for AC at -10 dB noise. When considering all AO/AC correlated features, the combined RMSE remains in similar ranges, specifically 2.70 ms for AO and 11.91 ms for AC at 10 dB noise, and 7.50 ms for AO and 16.35 ms for AC at -10 dB noise. The proposed algorithm offers low latency and RMSE for all tracked features, making it suitable for real-time processing. These systems enable accurate, timely extraction of hemodynamic indices for many cardiovascular monitoring applications, including trauma care in field settings.

Electrocardiogram to Determine Mitral and Aortic Valve Opening and Closure.

PURPOSE: Knowledge of the timing of cardiac valve opening and closing is important in cardiac physiology. The relationship between valve motion and electrocardiogram (ECG) is often assumed, however is not clearly defined. Here we investigate the accuracy of cardiac valve timing estimated using only the ECG, compared to Doppler echocardiography (DE) flow imaging as the gold standard. METHODS: DE was obtained in 37 patients with simultaneous ECG recording. ECG was digitally processed and identifiable features (QRS, T, P waves) were examined as potential reference points to determine opening and closure of aortic and mitral valves, as compared to DE outflow and inflow measurement. Timing offset of the cardiac valves opening and closure between ECG features and DE was measured from derivation set (n = 19). The obtained mean offset in combination with the ECG features model was then evaluated on a validation set (n = 18). Using the same approach, additional measurement was also done for the right sided valves. RESULTS: From the derivation set, we found a fixed offset of 22 ± 9 ms, 2 ± 13 ms, 90 ± 26 ms, and - 2 ± - 27 ms when comparing S to aortic valve opening, Tend to aortic valve closure, Tend to mitral valve opening, and R to mitral valve closure respectively. Application of this model to the validation set showed good estimation of aortic and mitral valve opening and closure timing value, with low model absolute error (median of the mean absolute error of the four events = 19 ms compared to the gold standard DE measurement). For the right-sided (tricuspid and pulmonic) valves in our patient set, there was considerably higher median of the mean absolute error of 42 ms for the model. CONCLUSION: ECG features can be used to estimate aortic and mitral valve timings with good accuracy as compared to DE, allowing useful hemodynamic information to be derived from this easily available test.

A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach.

Cardiac valves simulation is one of the most complex tasks in cardiovascular modeling. Fluid-structure interaction is not only highly computationally demanding but also requires knowledge of the mechanical properties of the tissue. Therefore, an alternative is to include valves as resistive flow obstacles, prescribing the geometry (and its possible changes) in a simple way, but, at the same time, with a geometry complex enough to reproduce both healthy and pathological configurations. In this work, we present a generalized parametric model of the aortic valve to obtain patient-specific geometries that can be included into blood flow simulations using a resistive immersed implicit surface (RIIS) approach. Numerical tests are presented for geometry generation and flow simulations in aortic stenosis patients whose parameters are extracted from ECG-gated CT images.

Effect of aortic curvature on bioprosthetic aortic valve performance.

Transvalvular pressure gradient (ΔP) after aortic valve replacement is an important surrogate of aortic bioprostheses performance. Invasive ΔP is often measured after transcatheter aortic valve replacement to exclude patient-prosthetic mismatch. However, invasive aortic pressures are usually recorded in the pressure recovery (PR) zone downstream of the valve, potentially resulting in ΔP underestimation compared to noninvasive measurements. PR was extensively studied in straight ascending aortas. However, the impact of various aortic arch configurations on ΔP has not been explored. PR was assessed in a pulse duplicating simulator at various cardiac conditions of cardiac output, heart rates and pressures. Three different aortic geometries with identical root dimensions but with different aortic arches were used: (1) curvature 1, (2) curvature 2, and (3) straight aortic models. Instantaneous pressure and peak ΔP measurements were recorded incrementally along the models for each cardiac condition. The models with aortic arches produced two distinct PR zones (after the valve and after the aortic arch), whereas the model without an aortic arch produced only one PR zone (after the valve). The trend of the pressure and ΔP curves for each model was independent of the cardiac condition used, but the individually measured pressure magnitudes did change with different conditions. In this study, we illustrated the differences in PR between distinct aortic curvatures and straight aorta. PR affects pressure and ΔP measurements. These effects are clear when recording aortic pressures by catheterization and echocardiography.

Semi-Automated Construction of Patient-Specific Aortic Valves from Computed Tomography Images.

This paper presents a semi-automatic method for the construction of volumetric models of the aortic valve using computed tomography angiography images. Although the aortic valve typically cannot be segmented directly from a computed tomography angiography image, the method described herein uses manually selected samples of an aortic segmentation derived from this image to inform the construction. These samples capture certain physiologic landmarks and are used to construct a volumetric valve model. As a demonstration of the capabilities of this method, valve models for 25 pediatric patients are created. A selected valve anatomy is used to perform fluid-structure interaction simulations using the immersed finite element/difference method with physiologic driving and loading conditions. Simulation results demonstrate this method creates a functional valve that opens and closes normally and generates pressure and flow waveforms that are similar to those observed clinically.

Modelling the rate-dependency of the mechanical behaviour of the aortic heart valve: An experimentally guided theoretical framework.

A theoretical framework, based on extant experimental findings, is presented to devise a novel viscous dissipation function Wv in order to model the rate-dependent mechanical behaviour of the aortic heart valve. The experimental data encompasses Cauchy stress-stretch (σ-λ) curves obtained across a 10,000-fold range of stretch rates (λ˙), from quasi-static (λ˙= 0.001 s-1) to upper-range of physiological (λ˙= 12.4 s-1) deformation rates. The analysis of the data elicits two important trends: (i) the mechanical behaviour of the aortic valve across the tested rates is rate-dependent, with specimens becoming stiffer by increasing rate; and (ii) there appears to be a plateau in the rate-effects on the σ-λ curves; i.e. the rate-effects approach an asymptote with increase in the stretch rate λ˙. Guided by these empirical observations, we devise our new Wv function and demonstrate that the well-known form of the dissipation function commonly used in the literature is a special case of our proposed Wv. The ensuing model is then compared against the experimental σ-λ curves and is shown to provide favourable predictions. An important advantage of the employed modelling framework is that it allows the incorporation of the rate of deformation, which is a direct experimental control parameter, as an explicit modelling variable. The application of the proposed model is thereby recommended for heart valves and other soft tissues that exhibit similar rate-dependent features.

Data-driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram.

Pre-ejection period (PEP), an indicator of sympathetic nervous system activity, is useful in psychophysiology and cardiovascular studies. Accurate PEP measurement is challenging and relies on robust identification of the timing of aortic valve opening, marked as the B point on impedance cardiogram (ICG) signals. The ICG sensitivity to noise and its waveform's morphological variability makes automated B point detection difficult, requiring inefficient and cumbersome expert visual annotation. In this article, we propose a machine learning-based automated algorithm to detect the aortic valve opening for PEP measurement, which is robust against noise and ICG morphological variations. We analyzed over 60 hr of synchronized ECG and ICG records from 189 subjects. A total of 3657 averaged beats were formed using our recently developed ICG noise removal algorithm. Features such as the averaged ICG waveform, its first and second derivatives, as well as high-level morphological and critical hemodynamic parameters were extracted and fed into the regression algorithms to estimate the B point location. The morphological features were extracted from our proposed "variable" physiologically valid search-window related to diverse B point shapes. A subject-wise nested cross-validation procedure was performed for parameter tuning and model assessment. After examining multiple regression models, Adaboost was selected, which demonstrated superior performance and higher robustness to five state-of-the-art algorithms that were evaluated in terms of low mean absolute error of 3.5 ms, low median absolute error of 0.0 ms, high correlation with experts' estimates (Pearson coefficient = 0.9), and low standard deviation of errors of 9.2 ms. For reproducibility, an open-source toolbox is provided.

A computational analysis of potential aortic dilation induced by the hemodynamic effects of bicuspid aortic valve phenotypes.

BACKGROUND AND OBJECTIVES: The bicuspid aortic valve (BAV) is a major risk factor for the progression of aortic dilation (AD) because of the induced abnormal blood flow environment in aorta. The differences in the development of AD induced by BAV phenotypes remains unclear. Therefore, the objective of this study was to assess the potential locations of AD induced by different phenotypes of BAV. The different effects of opening orifice area and leaflet orientation on ascending aortic hemodynamics in Type-1 BAV was investigated by means of numerical simulation. METHODS: Finite element dynamic analysis was performed on tricuspid aortic valve (TAV) and BAV models to simulate the motion of the leaflets and obtain the geometrical characteristics of AV at peak systole as a reference, which were used for aortic models. Then, four sets of aortic fluid models were designed according to the leaflet fusion types [TAV; BAV (left-right-coronary cusp fusion, LR; right-non-coronary cusp fusion, RN; left-non-coronary cusp fusion, LN)], and the computational fluid dynamics method was applied to compare the hemodynamic differences within the aorta at peak systole. RESULTS: The maximum opening area of BAV was significantly reduced, resulting in alterations in aortic hemodynamics compared with TAV. The velocity streamlines were essentially parallel to the aortic wall in TAV. The average pressure and wall shear stress in aorta tend to be stable. In contrary, the eccentricity of BAV orifice jet resulted in high-velocity flow directed toward the ascending aorta (AA) wall and aortic arch for LR and LN; RN features an asymmetrical velocity distribution toward the outer bend of the middle AA, and eccentric flow tends to impact the distal AA. As the flow angle is associated with distinct flow impingement locations, different degrees of WSS and pressure concentration occur along the aortic wall from the AA to the aortic arch in three BAV types. CONCLUSIONS: The BAV morphotype affects the aortic hemodynamics, and the abnormal blood flow associated with BAV may play a role in AD. The different BAV phenotypes determine the direction of blood flow jet and change the expression of dilation. LR is likely to cause dilation of the tubular AA; RN results in dilation of the middle AA to proximal aortic arch; and LN causes an increased incidence of the tubular AA and the proximal aortic arch.

Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor ß (TGFß), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan-a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.

A design-based model of the aortic valve for fluid-structure interaction.

This paper presents a new method for modeling the mechanics of the aortic valve and simulates its interaction with blood. As much as possible, the model construction is based on first principles, but such that the model is consistent with experimental observations. We require that tension in the leaflets must support a pressure, then derive a system of partial differential equations governing its mechanical equilibrium. The solution to these differential equations is referred to as the predicted loaded configuration; it includes the loaded leaflet geometry, fiber orientations and tensions needed to support the prescribed load. From this configuration, we derive a reference configuration and constitutive law. In fluid-structure interaction simulations with the immersed boundary method, the model seals reliably under physiological pressures and opens freely over multiple cardiac cycles. Further, model closure is robust to extreme hypo- and hypertensive pressures. Then, exploiting the unique features of this model construction, we conduct experiments on reference configurations, constitutive laws and gross morphology. These experiments suggest the following conclusions: (1) The loaded geometry, tensions and tangent moduli primarily determine model function. (2) Alterations to the reference configuration have little effect if the predicted loaded configuration is identical. (3) The leaflets must have sufficiently nonlinear material response to function over a variety of pressures. (4) Valve performance is highly sensitive to free edge length and leaflet height. These conclusions suggest appropriate gross morphology and material properties for the design of prosthetic aortic valves. In future studies, our aortic valve modeling framework can be used with patient-specific models of vascular or cardiac flow.

Engineering the aortic valve extracellular matrix through stages of development, aging, and disease.

For such a thin tissue, the aortic valve possesses an exquisitely complex, multi-layered extracellular matrix (ECM), and disruptions to this structure constitute one of the earliest hallmarks of fibrocalcific aortic valve disease (CAVD). The native valve structure provides a challenging target for engineers to mimic, but the development of advanced, ECM-based scaffolds may enable mechanistic and therapeutic discoveries that are not feasible in other culture or in vivo platforms. This review first discusses the ECM changes that occur during heart valve development, normal aging, onset of early-stage disease, and progression to late-stage disease. We then provide an overview of the bottom-up tissue engineering strategies that have been used to mimic the valvular ECM, and opportunities for advancement in these areas.

Shear type and magnitude affect aortic valve endothelial cell morphology, orientation, and differentiation.

Valvular endothelial cells line the outer layer of heart valves and can withstand shear forces caused by blood flow. In contrast to vascular endothelial cells, there is limited amount of research over valvular endothelial cells. For this reason, the exact physiologic behavior of valvular endothelial cells is unclear. Prior studies have concluded that valvular endothelial cells align perpendicularly to the direction of blood flow, while vascular endothelial cells align parallel to blood flow. Other studies have suggested that different ranges of shear stress uniquely impact the behavior of valvular endothelial cells. The goal of this study was to characterize the response of valvular endothelial cell under different types, magnitudes, and durations of shear stress. In this work, the results demonstrated that with increased shear rate and duration of exposure, valvular endothelial cells no longer possessed the traditional cuboidal morphology. Instead through the change in cell circularity and aspect ratio, valvular endothelial cells aligned in an organized manner. In addition, different forms of shear exposure caused the area and circularity of valvular endothelial cells to decrease while inducing mesenchymal transformation validated through αSMA and TGFß1 expression. This is the first investigation showing that valvular endothelial cells alignment is not as straightforward as once thought (perpendicular to flow). Different types and magnitudes of shear induce different local behaviors. This is also the first demonstration of valvular endothelial cells undergoing EndMT without chemical inducers on a soft surface in vitro. Findings from this study provide insights to understanding the pathophysiology of valvular endothelial cells which can potentially propel future artificial engineered heart valves.

Left atrial minimal volume: association with diastolic dysfunction and heart failure in patients in sinus rhythm or atrial fibrillation with preserved ejection fraction.

BACKGROUND: Evidence of diastolic dysfunction (DD) required for the diagnosis of heart failure with preserved ejection fraction (HFpEF) is elusive in atrial fibrillation (AF). Left ventricular (LV) and left atrial (LA) speckle-tracking echocardiography (STE) may provide rhythm independent indications of DD. We aimed to find common LV/LA myocardial mechanics parameters to demonstrate DD, using STE in patients with AF. METHODS: 176 echocardiographic assessments of patients were studied retrospectively by STE. 109 patients with history of AF were divided in three groups: sinus with normal diastolic function (n = 32, ND), sinus with DD (n = 35, DD) and patients with AF during echocardiography (n = 42). These assessments were compared to 67 normal controls. Demographic, clinical, echocardiographic and myocardial mechanic characteristics were obtained. RESULTS: The patients with DD in sinus rhythm and patients with AF were similar in age, mostly women, and had cardiovascular risk factors as well as higher dyspnea prevalence compared to either controls or patients with ND. In the AF group, LV ejection fraction (LVEF) (p = 0.008), global longitudinal strain and LA emptying were lower (p < 0.001), whereas LA volumes were larger (p < 0.001) compared to the other groups. In a multivariable analysis of patients in sinus rhythm, LA minimal volume indexed to body surface area (Vmin-I) was found to be the single significant factor associated with DD (AUC 83%). In all study patients, Vmin-I correlated with dyspnea (AUC 80%) and pulmonary hypertension (AUC 90%). CONCLUSIONS: Vmin-I may be used to identify DD and assist in the diagnosis of HFpEF in patients with AF.

Subject-specific multiscale modeling of aortic valve biomechanics.

A Finite Element workflow for the multiscale analysis of the aortic valve biomechanics was developed and applied to three physiological anatomies with the aim of describing the aortic valve interstitial cells biomechanical milieu in physiological conditions, capturing the effect of subject-specific and leaflet-specific anatomical features from the organ down to the cell scale. A mixed approach was used to transfer organ-scale information down to the cell-scale. Displacement data from the organ model were used to impose kinematic boundary conditions to the tissue model, while stress data from the latter were used to impose loading boundary conditions to the cell level. Peak of radial leaflet strains was correlated with leaflet extent variability at the organ scale, while circumferential leaflet strains varied over a narrow range of values regardless of leaflet extent. The dependency of leaflet biomechanics on the leaflet-specific anatomy observed at the organ length-scale is reflected, and to some extent emphasized, into the results obtained at the lower length-scales. At the tissue length-scale, the peak diastolic circumferential and radial stresses computed in the fibrosa correlated with the leaflet surface area. At the cell length-scale, the difference between the strains in two main directions, and between the respective relationships with the specific leaflet anatomy, was even more evident; cell strains in the radial direction varied over a relatively wide range ([Formula: see text]) with a strong correlation with the organ length-scale radial strain ([Formula: see text]); conversely, circumferential cell strains spanned a very narrow range ([Formula: see text]) showing no correlation with the circumferential strain at the organ level ([Formula: see text]). Within the proposed simulation framework, being able to account for the actual anatomical features of the aortic valve leaflets allowed to gain insight into their effect on the structural mechanics of the leaflets at all length-scales, down to the cell scale.

Inflammatory and Biomechanical Drivers of Endothelial-Interstitial Interactions in Calcific Aortic Valve Disease.

Calcific aortic valve disease is dramatically increasing in global burden, yet no therapy exists outside of prosthetic replacement. The increasing proportion of younger and more active patients mandates alternative therapies. Studies suggest a window of opportunity for biologically based diagnostics and therapeutics to alleviate or delay calcific aortic valve disease progression. Advancement, however, has been hampered by limited understanding of the complex mechanisms driving calcific aortic valve disease initiation and progression towards clinically relevant interventions.