Computational Study on the Effects of Valve Orientation on the Hemodynamics and Leaflet Dynamics of Bioprosthetic Pulmonary Valves.

Pulmonary valves do not display a fibrous annulus as do other valves in the heart; thus, pulmonary valves can be implanted at multiple orientations and locations within the right ventricular outflow tract (RVOT). This gives surgeons more freedom when implanting the valve but it also results in uncertainties regarding placement, particularly with respect to valve orientation. We investigate the pulmonary artery hemodynamics and valve leaflet dynamics of pulmonary valve replacements (PVRs) with various orientations via fluid-structure interaction (FSI) models. A canonical model of the branching pulmonary artery is coupled with a dynamic model of a pulmonary valve, and from this we quantify the effect of valve implant orientation on the postvalvular hemodynamics and leaflet dynamics. Metrics such as turbulent kinetic energy (TKE), branch pulmonary artery flow distributions, projected valve opening area (PVOA), and pressure differentials across the valve leaflets are analyzed. Our results indicate that off-axis orientation results in higher pressure forces and flow and energy asymmetry, which potentially have implications for long-term durability of implanted bioprosthetic valves.

Pancreatic duct pressure: A review of technical aspects and clinical significance.

Pancreatic duct pressure (PDP) dynamics comprise an intricately modulated system that helps maintain homeostasis of pancreatic function. It is affected by various factors, including the rate of pancreatic fluid secretion, patency of the ductal system, sphincter of Oddi function, and pancreatic fluid characteristics. Disease states such as acute and chronic pancreatitis can alter the normal PDP dynamics. Ductal hypertension or increased PDP is suspected to be involved in the pathogenesis of pancreatic pain, endocrine and exocrine pancreatic insufficiency, and recurrent pancreatitis. This review provides a comprehensive appraisal of the available literature on PDP, including the methods used in the measurement and clinical implications of elevated PDP.

Reconstructing the pressure field around swimming fish using a physics-informed neural network.

Fish detect predators, flow conditions, environments and each other through pressure signals. Lateral line ablation is often performed to understand the role of pressure sensing. In the present study, we propose a non-invasive method for reconstructing the instantaneous pressure field sensed by a fish's lateral line system from two-dimensional particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the pressure field near and on the fish's body that satisfies both the Navier-Stokes equations and the constraints put forward by the PIV measurements. The method was validated using a direct numerical simulation of a swimming mackerel, Scomber scombrus, and was applied to experimental data of a turning zebrafish, Danio rerio. The results demonstrate that this method is relatively insensitive to the spatio-temporal resolution of the PIV measurements and accurately reconstructs the pressure on the fish's body.

Quantifying the Effect of Body Habitus on Cardiac Auscultation Via Computational Hemoacoustics.

The effect of body habitus on auscultation of heart murmurs is investigated via computational hemoacoustic modeling. The source of the heart murmur is first obtained from a hemodynamic simulation of blood flow through a stenosed aortic valve. This sound source is then placed at the aortic valve location in four distinct human thorax models, and the propagation of the murmur in each thorax model is simulated by solving the elastic wave equations in the time-domain. Placing the same sound source in different thorax models allows for the disambiguation of the effect of body habitus on cardiac auscultation. The surface acceleration resulting from the murmur on each subject's chest surface shows that subjects with higher body-mass index and thoracic cross-sectional area yield smaller acceleration values for the S1 sound. Moreover, the spectral analysis of the signal shows that slope from linear regression in the normal heart sound frequency range (10-150 Hz) is larger for children at the aortic, pulmonic, and mitral auscultation points compared to that for adults. The slope in the murmur frequency range (150-400 Hz) was larger for female subjects at the mitral point compared to that for male subjects. The trends from the results show the potential of the proposed computational method to provide quantitative insights regarding the effect of various anatomical factors on cardiac auscultation.

Computational Modeling of Aortic Stenosis With a Reduced Degree-of-Freedom Fluid-Structure Interaction Valve Model.

In this study, a novel reduced degree-of-freedom (rDOF) aortic valve model is employed to investigate the fluid-structure interaction (FSI) and hemodynamics associated with aortic stenosis. The dynamics of the valve leaflets are determined by an ordinary differential equation with two parameters and this rDOF model is shown to reproduce key features of more complex valve models. The hemodynamics associated with aortic stenosis is studied for three cases: a healthy case and two stenosed cases. The focus of the study is to correlate the hemodynamic features with the source generation mechanism of systolic murmurs associated with aortic stenosis. In the healthy case, extremely weak flow fluctuations are observed. However, in the stenosed cases, simulations show significant turbulent fluctuations in the ascending aorta, which are responsible for the generation of strong wall pressure fluctuations after the aortic root mostly during the deceleration phase of the systole. The intensity of the murmur generation increases with the severity of the stenosis, and the source locations for the two diseased cases studied here lie around 1.0 inlet duct diameters (Do) downstream of the ascending aorta.

A Noninvasive Assessment of Flow Based on Contrast Dispersion in Computed Tomography Angiography: A Computational and Experimental Phantom Study.

Transluminal attenuation gradient (TAG), defined as the gradient of the contrast agent attenuation drop along the vessel, is an imaging biomarker that indicates stenosis in the coronary arteries. The transluminal attenuation flow encoding (TAFE) equation is a theoretical platform that quantifies blood flow in each coronary artery based on computed tomography angiography (CTA) imaging. This formulation couples TAG (i.e., contrast dispersion along the vessel) with fluid dynamics. However, this theoretical concept has never been validated experimentally. The aim of this proof-of-principle phantom study is to validate TAFE based on CTA imaging. Dynamic CTA images were acquired every 0.5 s. The average TAFE estimated flow rates were compared against four predefined pump values in a straight (20, 25, 30, 35, and 40 ml/min) and a tapered phantom (25, 35, 45, and 55 ml/min). Using the TAFE formulation with no correction, the flow rates were underestimated by 33% and 81% in the straight and tapered phantoms, respectively. The TAFE formulation was corrected for imaging artifacts focusing on partial volume averaging and radial variation of contrast enhancement. After corrections, the flow rates estimated in the straight and tapered phantoms had an excellent Pearson correlation of r = 0.99 and 0.87 (p < 0.001), respectively, with only a 0.6%±0.2 mL/min difference in estimation of the flow rate. In this proof-of-concept phantom study, we corrected the TAFE formulation and showed a good agreement with the actual pump values. Future clinical validations are needed for feasibility of TAFE in clinical use.

Computational Modeling and Analysis of Murmurs Generated by Modeled Aortic Stenoses.

In this study, coupled hemodynamic-acoustic simulations are employed to study the generation and propagation of murmurs associated with aortic stenoses where the aorta with a stenosed aortic valve is modeled as a curved pipe with a constriction near the inlet. The hemodynamics of the poststenotic flow is investigated in detail in our previous numerical study (Zhu et al., 2018, "Computational Modelling and Analysis of Haemodynamics in a Simple Model of Aortic Stenosis," J. Fluid Mech., 851, pp. 23-49). The temporal history of the pressure on the aortic lumen is recorded during the hemodynamic study and used as the murmur source in the acoustic simulations. The thorax is modeled as an elliptic cylinder and the thoracic tissue is assumed to be homogeneous, linear and viscoelastic. A previously developed high-order numerical method that is capable of dealing with immersed bodies is applied in the acoustic simulations. To mimic the clinical practice of auscultation, the sound signals from the epidermal surface are collected. The simulations show that the source of the aortic stenosis murmur is located at the proximal end of the aortic arch and that the sound intensity pattern on the epidermal surface can predict the source location of the murmurs reasonably well. Spectral analysis of the murmur reveals the disconnect between the break frequency obtained from the flow and from the murmur signal. Finally, it is also demonstrated that the source locations can also be predicted by solving an inverse problem using the free-space Green's function. The implications of these results for cardiac auscultation are discussed.

Flow Dynamics in the Aortic Arch and Its Effect on the Arterial Input Function in Cardiac Computed Tomography.

The arterial input function (AIF)-time-density curve (TDC) of contrast at the coronary ostia-plays a central role in contrast enhanced computed tomography angiography (CTA). This study employs computational modeling in a patient-specific aorta to investigate mixing and dispersion of contrast in the aortic arch (AA) and to compare the TDCs in the coronary ostium and the descending aorta. Here, we examine the validity of the use of TDC in the descending aorta as a surrogate for the AIF. Computational fluid dynamics (CFD) was used to study hemodynamics and contrast dispersion in a CTA-based patient model of the aorta. Variations in TDC between the aortic root, through the AA and at the descending aorta and the effect of flow patterns on contrast dispersion was studied via postprocessing of the results. Simulations showed complex unsteady patterns of contrast mixing and dispersion in the AA that are driven by the pulsatile flow. However, despite the relatively long intra-aortic distance between the coronary ostia and the descending aorta, the TDCs at these two locations were similar in terms of rise-time and up-slope, and the time lag between the two TDCs was 0.19 s. TDC in the descending aorta is an accurate analog of the AIF. Methods that use quantitative metrics such as rise-time and slope of the AIF to estimate coronary flowrate and myocardial ischemia can continue with the current practice of using the TDC at the descending aorta as a surrogate for the AIF.

A Computational Method for Analyzing the Biomechanics of Arterial Bruits.

A computational framework consisting of a one-way coupled hemodynamic-acoustic method and a wave-decomposition based postprocessing approach is developed to investigate the biomechanics of arterial bruits. This framework is then applied for studying the effect of the shear wave on the generation and propagation of bruits from a modeled stenosed artery. The blood flow in the artery is solved by an immersed boundary method (IBM) based incompressible flow solver. The sound generation and propagation in the blood volume are modeled by the linearized perturbed compressible equations, while the sound propagation through the surrounding tissue is modeled by the linear elastic wave equation. A decomposition method is employed to separate the acoustic signal into a compression/longitudinal component (curl free) and a shear/transverse component (divergence free), and the sound signals from cases with and without the shear modulus are monitored on the epidermal surface and are analyzed to reveal the influence of the shear wave. The results show that the compression wave dominates the detected sound signal in the immediate vicinity of the stenosis, whereas the shear wave has more influence on surface signals further downstream of the stenosis. The implications of these results on cardiac auscultation are discussed.

Computational Study of Computed Tomography Contrast Gradients in Models of Stenosed Coronary Arteries.

Recent computed tomography coronary angiography (CCTA) studies have noted higher transluminal contrast agent gradients in arteries with stenotic lesions, but the physical mechanism responsible for these gradients is not clear. We use computational fluid dynamics (CFD) modeling coupled with contrast agent dispersion to investigate the mechanism for these gradients. Simulations of blood flow and contrast agent dispersion in models of coronary artery are carried out for both steady and pulsatile flows, and axisymmetric stenoses of severities varying from 0% (unobstructed) to 80% are considered. Simulations show the presence of measurable gradients with magnitudes that increase monotonically with stenotic severity when other parameters are held fixed. The computational results enable us to examine and validate the hypothesis that transluminal contrast gradients (TCG) are generated due to the advection of the contrast bolus with time-varying contrast concentration that appears at the coronary ostium. Since the advection of the bolus is determined by the flow velocity in the artery, the magnitude of the gradient, therefore, encodes the coronary flow velocity. The correlation between the flow rate estimated from TCG and the actual flow rate in the computational model of a physiologically realistic coronary artery is 96% with a R2 value of 0.98. The mathematical formulae connecting TCG to flow velocity derived here represent a novel and potentially powerful approach for noninvasive estimation of coronary flow velocity from CT angiography.

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.

Effect of schooling on flow generated sounds from carangiform swimmers.

Computational models are used to examine the effect of schooling on flow generated noise from fish swimming using their caudal fins. We simulate the flow as well as the far-field hydrodynamic sound generated by the time-varying pressure loading on these carangiform swimmers. The effect of the number of swimmers in the school, the relative phase of fin flapping of the swimmers, and their spatial arrangement is examined. The simulations indicate that the phase of the fin flapping is a dominant factor in the total sound radiated into the far-field by a group of swimmers. For small schools, a suitable choice of relative phase between the swimmers can significantly reduce the overall intensity of the sound radiated to the far-field. The relative positioning of the swimmers is also shown to have an impact on the total radiated noise. For a larger school, even highly uncorrelated phases of fin movement between the swimmers in the school are very effective in significantly reducing the overall intensity of sound radiated into the far-field. The implications of these findings for fish ethology as well as the design and operation of bioinspired vehicles are discussed.

In silico modelling of the effect of pyloric intervention procedures on gastric flow and emptying in a stomach with gastroparesis.

Pyloric interventions are surgical procedures employed to increase the gastric emptying rate in gastroparesis patients. In this study, we use an in silico model to investigate the consequences of pyloric intervention on gastric flow and emptying for two phenotypes of gastroparesis: antral hypomotility and decreased gastric tone. The transpyloric pressure gradient predicted by the in silico model, based on viscous fluid flow equations, is compared against in vivo measurements. Both phenotypes exhibit a similar pre-procedural emptying rate reduction, but after pyloric surgery, antral hypomotility case with preserved gastric tone shows significant improvements in emptying rates, up to 131%, accompanied by bile reflux from the duodenum into the stomach. Conversely, severely reduced gastric tone cases exhibited a post-procedural reduction in the net emptying rate due to the relatively larger bile reflux. In cases with a combination of antral hypomotility and reduced gastric tone, post-procedural improvements were observed only when both conditions were mild. Our findings highlight the pivotal role of the relative increase in pyloric orifice diameter in determining post-operative emptying rates. The study suggests a possible explanation for the selective response of patients toward these procedures and underscores the potential of in silico modelling to generate valuable insights to inform gastric surgery.

Towards Longitudinal Monitoring of Leaflet Mobility in Prosthetic Aortic Valves via In-Situ Pressure Sensors: In-Silico Modeling and Analysis.

BACKGROUND: Transcatheter aortic valves (TAVs) are susceptible to leaflet thrombosis which may lead to thromboembolic events, and early detection and intervention are believed to be the key to avoiding such adverse outcomes. An embedded sensor system installed on the valve stent, coupled with an appropriate machine learning-based continuous monitoring algorithm can facilitate early detection to predict severity of reduced leaflet motion (RLM) and avoid adverse outcomes. METHODS: We present a data-driven, in silico, proof-of-concept analysis of a pressure microsensor based system for quantifying RLM in TAVs. We generate a dataset of 21 high-fidelity transvalvular flow simulations with healthy and mildly stenotic TAVs to train a logistic regression model to correlate individual leaflet mobility in each simulation with principal components of corresponding hemodynamic pressure recorded at strategic locations of the TAV stent. A separate test dataset of 7 simulations is also generated for prospective assessment of model performance. RESULTS: An array of 6 sensors embedded on the TAV stent, with two sensors tracking individual leaflet, successfully correlates leaflet mobility with recorded pressure. The sensors are placed along leaflet centerlines, one in the sinus, and the other at the sino-tubular junction. The regression model is tuned using cross-validation to achieve high accuracy on both training (R2 = 0.93) and test (R2 = 0.77) sets. CONCLUSION: Discrete blood pressure recordings on TAV stents can be successfully correlated with individual leaflet mobility. Further development of this technology can enable longitudinal monitoring of TAVs and early detection of valve failure.

Improved swimming performance in schooling fish via leading-edge vortex enhancement.

The hydrodynamics of schooling fish has been the subject of continued investigation over the last 50 years; fish schools exhibit a variety of arrangements and several distinct mechanisms have been proposed to explain the hydrodynamic benefits of schooling. In the current study, we use direct numerical simulations to show that a caudal fin swimmer trailing another similar swimmer can significantly improve its swimming performance by positioning itself such that the wake-induced flow of the leading fish, enhances the leading-edge vortex (LEV) on the fin of the trailing fish. Improvements of up to 12% in both the thrust and efficiency of the trailing fish are possible with this mechanism. The mechanisms underlying these interactional effects are quantitatively analyzed by applying the force partitioning method, a powerful data-driven method that partitions the pressure forces on the fish into mechanistically distinct components. The analysis reveals that the LEV on the fin dominates the overall thrust production for these swimmers and its enhancement therefore provides an effective and robust means for harnessing fish-fish hydrodynamic interactions in a school. In addition to confirming the potential energetic benefits of schooling, the LEV enhancement mechanism could be exploited in coordinated swimming of bioinspired multi-vehicle or multi-foil flapping foil propulsion systems.

Prosthetic Valve Monitoring via In Situ Pressure Sensors: In Silico Concept Evaluation using Supervised Learning.

PURPOSE: Patients receiving transcatheter aortic valve replacement (TAVR) can benefit from continuous, longitudinal monitoring of valve prosthesis to prevent leaflet thrombosis-related complications. We present a computational proof-of-concept study of a novel, non-invasive and non-toxic valve monitoring technique for TAVs which uses pressure measurements from microsensors embedded on the valve stent. We perform a data-driven analysis to determine the signal processing and machine learning required to detect reduced mobility in individual leaflets. METHODS: We use direct numerical simulations to describe hemodynamic differences in transvalvular flow in ascending aorta models with healthy and stenotic valves. A Cartesian-grid flow solver and a reduced-order valve model simulate the complex dynamics of blood flow and leaflet motion, respectively. The two-way fluid-structure interaction coupling is achieved using a sharp interface immersed boundary method. RESULTS: From a dataset of 21 simulations, we show leaflets with reduced mobility result in large, asymmetric pressure fluctuations in their vicinity, particularly in the region extending from the aortic sinus to the sino-tubular junction (STJ). We train a linear classifier algorithm by correlating sinus and STJ pressure measurements on the stent surface to individual leaflet status. The algorithm was shown to have >90% accuracy for prospective detection of individual leaflet dysfunction. CONCLUSIONS: We demonstrate that using only two discrete pressure measurements, per leaflet, on the TAV stent, individual leaflet status can be accurately predicted. Such a sensorized TAV system could enable safe and inexpensive detection of prosthetic valve dysfunction.

In vitro assessment of the biocompatibility of chemically treated silicone materials with human lens epithelial cells.

Cytotoxicity testing is a regulatory requirement for safety testing of new ocular implants. In vitro toxicity tests determine whether toxic chemicals are present on a material surface or leach out of the material matrix. A method of evaluating the cytotoxicity of ocular implants was developed using fluorescent viability dyes. To assess the assay's sensitivity in detecting toxic substances on biomaterials, zinc diethydithiocarbamate (ZDEC) and benzalkonium chloride (BAK) were deposited on silicone surfaces at different concentrations. Human lens epithelial cells (HLEC) were added to the surface of these treated silicone surfaces and were assessed for viability. The viability of both the adherent and non-adherent cells was determined using confocal microscopy with, annexin V, ethidium homodimer, and calcein. Cell metabolism was also evaluated using resazurin and the release of inflammatory cytokines was quantified using a multiplex Mesoscale Discovery platform. Confocal microscopy was shown to be a sensitive assay for evaluating material toxicity, as significant toxicity (p < 0.05) from ZDEC and BAK-treated surfaces compared to the untreated silicone control was detected. Patterns of cytokine release from cells varied depending on the toxin evaluated and the toxin concentration and did not directly correlate with the reduction in cell metabolic activity measured by alamarBlue.

Effect of stomach motility on food hydrolysis and gastric emptying: Insight from computational models.

The peristaltic motion of stomach walls combines with the secretion of digestive enzymes to initiate the process that breaks down food. In this study, the mixing, breakdown, and emptying of a liquid meal containing protein is simulated in a model of a human stomach. In this model, pepsin, the gastric enzyme responsible for protein hydrolysis, is secreted from the proximal region of the stomach walls and allowed to react with the contents of the stomach. The velocities of the retropulsive jet induced by the peristaltic motion, the emptying rate, and the extent of hydrolysis are quantified for a control case as well as for three other cases with reduced motility of the stomach, which may result from conditions such as diabetes mellitus. This study quantifies the effect of stomach motility on the rate of food breakdown and its emptying into the duodenum and we correlate these observations with the mixing in the stomach induced by the wall motion.

Mosquitoes buzz and fruit flies don't-a comparative aeroacoustic analysis of wing-tone generation.

Crepuscular mosquitoes, which swarm in low light conditions, exhibit a range of adaptations including large aspect-ratio wings, high flapping frequencies and small stroke amplitudes that taken together, facilitate the generation of wing-tones that are well-suited for acoustic communication. In the current study, we employ computational aeroacoustic modeling to conduct a comparative study of wing-tone and flight efficiency in a mosquito (maleCulex) and a similar sized flying insect: a fruit fly (Drosophila). Based on this analysis, we show that pound-for-pound, a mosquito generates wing-tones that are a factor of about 3.4 times more intense than a fruit fly, and the mosquito is more efficient by a factor of about 3.7 in converting mechanical power into acoustic power. The wing-tones for the mosquito are also more tilted in the forward direction, a characteristic that would be more conducive for acoustic signaling during a mate chase. The simulation data also shows that the specific power (mechanical power over mean lift) of the mosquito is nearly equal to that of the fruit fly, indicating that the adaptations that facilitate wing-tone based communication in mosquitoes, do not seem to compromise their flight efficiency.

Detecting Aortic Valve Anomaly From Induced Murmurs: Insights From Computational Hemodynamic Models.

Patients who receive transcatheter aortic valve replacement are at risk for leaflet thrombosis-related complications, and can benefit from continuous, longitudinal monitoring of the prosthesis. Conventional angiography modalities are expensive, hospital-centric and either invasive or employ potentially nephrotoxic contrast agents, which preclude their routine use. Heart sounds have been long recognized to contain valuable information about individual valve function, but the skill of auscultation is in decline due to its heavy reliance on the physician's proficiency leading to poor diagnostic repeatability. This subjectivity in diagnosis can be alleviated using machine learning techniques for anomaly detection. We present a computational and data-driven proof-of-concept analysis of a novel, auscultation-based technique for monitoring aortic valve, which is practical, non-invasive, and non-toxic. However, the underlying mechanisms leading to physiological and pathological heart sounds are not well-understood, which hinders development of such a technique. We first address this by performing direct numerical simulations of the complex interactions between turbulent blood flow in a canonical ascending aorta model and dynamic valve motion in 29 cases with healthy and stenotic valves. Using the turbulent pressure fluctuations on the aorta lumen boundary, we model the propagation of heart sounds, as elastic waves, through the patient's thorax. The heart sound may be recorded on the epidermal surface using a stethoscope/phonocardiograph. This approach allows us to correlate instantaneous hemodynamic phenomena and valve motion with the acoustic response. From this dataset we extract "acoustic signatures" of healthy and stenotic valves based on principal components of the recorded sound. These signatures are used to train a linear discriminant classifier by maximizing correlation between recorded heart sounds and valve status. We demonstrate that this classifier is capable of accurate prospective detection of anomalous valve function and that the principal component-based signatures capture prominent audible features of heart sounds, which have been historically used by physicians for diagnosis. Further development of such technology can enable inexpensive, safe and patient-centric at-home monitoring, and can extend beyond transcatheter valves to surgical as well as native valves.