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.

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.

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.

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.

A computational study of the hemodynamics of bioprosthetic aortic valves with reduced leaflet motion.

We employ a reduced degree-of-freedom aortic valve model to investigate the flow physics associated with early-stage reduced leaflet motion in bioprosthetic aortic valves. The model is coupled with a sharp-interface immersed boundary based incompressible flow solver to efficiently simulate the fluid-structure interaction. A total of 19 cases of flow through aortic valves with varying degrees of reduced leaflet motion (RLM) are considered. The characteristics of the aortic jet and the consequent aorta wall loading patterns are analyzed. Our results show that asymmetric RLM tilts the aortic jet and leads to large reverse and recirculating flow regions downstream from leaflets with restricted mobility. The changes in flow patterns increase wall pressure and shear stress fluctuations, and result in asymmetric oscillating shear on the aorta wall. These findings have implications for auscultation based diagnosis of this condition as well as the health of the aorta.

Biomechanical Evaluations of Ocular Injury Risk for Blast Loading.

Ocular trauma is one of the most common types of combat injuries resulting from the exposure of military personnel with improvised explosive devices. The injury mechanism associated with the primary blast wave is poorly understood. We employed a three-dimensional computational model, which included the main internal ocular structures of the eye, spatially varying thickness of the cornea-scleral shell, and nonlinear tissue properties, to calculate the intraocular pressure and stress state of the eye wall and internal ocular structure caused by the blast. The intraocular pressure and stress magnitudes were applied to estimate the injury risk using existing models for blunt impact and blast loading. The simulation results demonstrated that blast loading can induce significant stresses in the different components of the eyes that correlate with observed primary blast injuries in animal studies. Different injury models produced widely different injury risk predictions, which highlights the need for experimental studies evaluating mechanical and functional damage to the ocular structures caused by the blast loading.

Effectiveness of eye armor during blast loading.

Ocular trauma is one of the most common types of combat injuries resulting from the interaction of military personnel with improvised explosive devices. Ocular blast injury mechanisms are complex, and trauma may occur through various injury mechanisms. However, primary blast injuries (PBI) are an important cause of ocular trauma that may go unnoticed and result in significant damage to internal ocular tissues and visual impairment. Further, the effectiveness of commonly employed eye armor, designed for ballistic and laser protection, in lessening the severity of adverse blast overpressures (BOP) is unknown. In this paper, we employed a three-dimensional (3D) fluid-structure interaction computational model for assessing effectiveness of the eye armor during blast loading on human eyes and validated results against free field blast measurements by Bentz and Grimm (2013). Numerical simulations show that the blast waves focused on the ocular region because of reflections from surrounding facial features and resulted in considerable increase in BOP. We evaluated the effectiveness of spectacles and goggles in mitigating the pressure loading using the computational model. Our results corroborate experimental measurements showing that the goggles were more effective than spectacles in mitigating BOP loading on the eye. Numerical results confirmed that the goggles significantly reduced blast wave penetration in the space between the armor and the eyes and provided larger clearance space for blast wave expansion after penetration than the spectacles. The spectacles as well as the goggles were more effective in reducing reflected BOP at higher charge mass because of the larger decrease in dynamic pressures after the impact. The goggles provided greater benefit of reducing the peak pressure than the spectacles for lower charge mass. However, the goggles resulted in moderate, sustained elevated pressure loading on the eye, that became 50-100% larger than the pressure loading experienced by the unprotected eye after 0.2 ms of impact of blast wave, for lower as well as higher charge mass. The present model provides fundamental insights of flow and pressure fields in the ocular region, which helps to explain the effectiveness of the eye armor. Since the measurements of these fields are not trivial, the computational model aids in better understanding of development of PBI.