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The frequency domain analysis (FDA) offers greater computational efficiency than the method of characteristic (MOC) in simulating transient flow in pressurized pipes. However, its accuracy is hindered by linearisation errors. Violations of the assumptions for linearisation in friction term and valve equations during water distribution systems (WDSs) burst simulations make the FDA results meaningless. Linearisation procedures are modified as follows using the Heaviside property of pipeline bursts to address this problem: (1) the linearization of the friction term is adjusted, and (2) the valve equation is approximated using a three-step approach. The higher-order term dropped by the original FDA is linearly approximated to achieve better accuracy. The modified FDA is compared to the MOC in a real-life WDS by numerical experiment. Excellent precision can be observed even for a highly nonlinear case where the burst flow is 20% of the initial total demand. The simulation time is significantly shorter than when using the MOC. The proposed modification dramatically improves the applicability of the FDA for solving the nonlinear error issue during the simulation of the pipeline burst. This result implies the potential for its application in quick inverse analysis of pipeline bursts.

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.

Data on the long-term outcomes of prosthesis patient mismatch (PPM) after transcatheter aortic valve implantation (TAVI) remain controversial. This study aimed to investigate the incidence and clinical outcomes of measured PPM (PPMM) and predicted PPM (PPMP) in patients who underwent TAVI. This is a retrospective analysis of 3,016 patients who underwent TAVI at a large health care system between 2012 and 2021. Effective orifice area indexed to body surface area (EOAi) was measured at discharge using the continuity equation. EOAi was predicted according to the published predictive tables for each model and size of the valve. Primary end point was 5-year survival rate. Mean age was 80 years, and 55.6% were male. The mean Society of Thoracic Surgeons risk score was 4.66%. 74.9% of patients received a balloon-expandable valve (BEV), and 25.1% received a self-expanding valve (SEV). The incidence of severe PPM was markedly lower when defined by predicted versus measured EOAi (0.8% vs 6.3%, p <0.001) and when assessed in SEV versus BEV (5.3% vs 6.6%, p = 0.02). Neither severe PPMp nor severe PPMM was associated with 5-year mortality (hazard ratio 1.26, 95% confidence interval 0.96 to 1.66, p = 0.095; hazard ratio 1.03, 95% confidence interval 0.42 to 2.49, p = 0.954, respectively), irrespective of the presence of high residual pressure gradient. Neither BEV nor SEV was associated with an increased 5-year mortality, irrespective of PPM definition or severity. In this large health care system analysis, neither severe PPMP nor severe PPMM was associated with 5-year all-cause mortality. There was no difference between BEV and SEV in terms of mortality, irrespective of the definition or severity of PPM. In this retrospective analysis of 3,016 patients who underwent transcatheter aortic valve implantation at a large health care system over a 10-year period, neither severe measured prosthesis-patient mismatch (PPM) nor severe predicted PPM was associated with 5-year all-cause mortality, irrespective of the residual pressure gradient. Moreover, there was no difference between balloon and self-expanding valves in terms of mortality, irrespective of the definition or severity of PPM.

Calculation of effective orifice area (EOA) is crucial for the evaluation of prosthetic valve (PV) function and there is lack of data on the best method, particularly in obese patients, in whom two-dimensional (2D) transthoracic echocardiography (TTE) is cumbersome. We sought to compare two methods of calculating EOA through Continuity equation; one using standard 2D-TTE and other three-dimensional (3D) stoke volume (SV), in patients with bileaflet mechanical PV stratified by body mass index (BMI). On conventional TTE, SV mas measured using standard 2D derived data and 3D derived SV in 38 aortic and 62 mitral PV patients who were referred for further evaluation for mild/moderate symptoms of dyspnea. Patients were categorized with regard to transprosthetic flow into ‘normal-flow’ and ‘high-flow’ groups and several echocardiographic data including 2D and 3D EOA were compared. Rates of obesity (BMI ≥ 30) were similar within high and normal flow groups of mitral and aortic PV patients. Correlation and agreement of 2D and 3D EOA was sought in patients with and without obesity. After identifying patients with possible severe obstruction, ROC analysis was carried out to identify whether 2D and 3D derived EOA could discriminate those with obstruction. There was good correlation and agreement between two methods in patients without obesity in both mitral and aortic PV. In obese individuals, however, there was no correlation between 2D and 3D EOA; in whom echocardiographic criteria showing severe obstruction revealed that 3D EOA measurements were more accurate. ROC analysis supported that 3D EOA performs better to identify patients with obstructive characteristics. In patients with bileaflet PV, measurement of EAO by 3D derived SV yields more accurate results irrespective of BMI.

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 β (TGFβ), 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.

The zebrafish is a valuable model organism for studying cardiac development and diseases due to its many shared aspects of genetics and anatomy with humans and ease of experimental manipulations. Computational fluid–structure interaction (FSI) simulations are an efficient and highly controllable means to study the function of cardiac valves in development and diseases. Due to their small scales, little is known about the mechanical properties of zebrafish cardiac valves, limiting existing computational studies of zebrafish aortic valves and their interaction with blood. To circumvent these limitations, we took a largely first-principles approach called design-based elasticity that allows us to derive valve geometry, fiber orientation and material properties. In FSI simulations of an adult zebrafish aortic valve, these models produce realistic flow rates when driven by physiological pressures and demonstrate the spatiotemporal dynamics of valvular mechanical properties. These models can be used for future studies of zebrafish cardiac hemodynamics, development, and disease.

Understanding changes in the quality of life (QoL) and its risk factors can facilitate identify individuals who may benefit from transcatheter aortic valve replacement (TAVR). However, the relationships between frailty, mental health, cardiac function, and QoL in patients after TAVR have not been well-studied. From February 2022 to November 2023, 396 patients who underwent TAVR were selected as a convenience sample. A structural equation model was used to validate the hypothesized model. We employed descriptive statistics, Spearman correlation coefficients, independent-sample t-test, and one-way ANOVA analysis to determine direct correlations. Bootstrapping analysis was used to evaluate the indirect effects. In patients who underwent TAVR, QoL was negatively correlated with NYHA class and frailty phenotype. Specifically, frailty had a statistically direct impact on patients’ mental health (β = 0.237, 95% CI 0.068 to 0.365, p  < 0.01) and QoL (β=−0.375, 95% CI −0.524 to 0.193, p  < 0.01). Also, patients’ mental health (β = 0.159, 95% CI 0.031 to 0.30, p  < 0.05) and cardiac function (β=−0.356, 95% CI −0.599 to −0.168, p  < 0.01) showed significant direct effects on QoL. Meanwhile, patients’ mental health played a mediating role in the relationship between frailty and QoL (β = 0.038, 95% CI 0.006 to 0.072, p  < 0.05) in patients after TAVR. Future initiatives to raise patients’ awareness of frailty before and after TAVR, especially by considering the possible impact of mental health other than just anxiety and depression, may improve the overall QoL after TAVR. More extensive prospective trials, including psychological interventions tailored to patients after TAVR, are required.

The transvalvular pressure gradient (TPG) is commonly estimated using the Bernoulli equation. However, the method is known to be inaccurate. Therefore, an adjusted Bernoulli model for accurate TPG assessment was developed and evaluated. Numerical simulations were used to calculate TPGCFD in patient-specific geometries of aortic stenosis as ground truth. Geometries, aortic valve areas (AVA), and flow rates were derived from computed tomography scans. Simulations were divided in a training data set (135 cases) and a test data set (36 cases). The training data was used to fit an adjusted Bernoulli model as a function of AVA and flow rate. The model-predicted TPGModel was evaluated using the test data set and also compared against the common Bernoulli equation (TPGB). TPGB and TPGModel both correlated well with TPGCFD (r > 0.94), but significantly overestimated it. The average difference between TPGModel and TPGCFD was much lower: 3.3 mmHg vs. 17.3 mmHg between TPGB and TPGCFD. Also, the standard error of estimate was lower for the adjusted model: SEEModel = 5.3 mmHg vs. SEEB = 22.3 mmHg. The adjusted model’s performance was more accurate than that of the conventional Bernoulli equation. The model might help to improve non-invasive assessment of TPG.

As the demand for aortic valve prostheses grows, optimizing their mechanical performance and durability is essential. While mechanical valves offer longevity, their need for lifelong anticoagulation limits their use, making bioprosthetic valves a preferred alternative. However, bioprosthetic valves made from bovine pericardium face durability challenges due to structural degradation. Given that valve functionality is heavily influenced by the collagen architecture and mechanical properties of the tissue, selecting an optimal replacement is essential. This study evaluates treated ovine aortic valves as an alternative material, comparing their mechanical behavior to native human valves. Tensile tests showed an elastic modulus of 20.17 MPa for treated ovine leaflets, while human leaflets ranged from 6.15 MPa to 28.10 MPa. Stress relaxation tests indicated a 41% stress reduction in treated ovine valves compared to 21% in human valves after 300 s, suggesting greater viscoelasticity. Finite element analysis revealed lower peak systolic stress in treated ovine valves (0.36 MPa vs. 0.72 MPa in human valves), with stress distributions aligning with clinically observed degradation sites. These findings highlight ovine tissue’s potential for improved durability and flexibility, making it a strong candidate for next-generation bioprosthetic heart valves.

Abstract Background Predicting progression of myxomatous mitral valve disease (MMVD) in dogs can be challenging. Hypothesis/Objectives The mitral regurgitation severity index (MRSI) will predict time to congestive heart failure (CHF) and all-cause death in dogs with MMVD. Animals Eight hundred sixty-nine client-owned dogs. Methods Retrospective study pooling data from 4 previous samples including dogs with MMVD stage B2 or C. MRSI was calculated as: (heart rate [HR]/120) × left atrium-to-aorta ratio (LA:Ao) × (age in years/10) × 100. Alternative MRSI formulas substituting radiographic measures of left atrial size were also calculated. Cox proportional hazard modeling and time-dependent receiver-operator characteristic curves quantified prognostic performance. Results For Stage B2 pooled samples, MRSI > 156 was predictive of time to CHF (median 407 vs 1404 days; area under the curve [AUC] 0.68; hazard ratio 3.02 [95% CI 1.9-4.9]; P < .001). MRSI > 173 was predictive of all-cause death (median survival 868 vs 1843 days; AUC 0.64; hazard ratio 4.26 [95% CI 2.4-7.5]; P < .001). MRSI showed superior predictive value compared to the individual variables of HR, LA:Ao, and age. Variations of the MRSI equation substituting radiographic vertebral left atrial size for LA:Ao were also significantly predictive of outcome in stage B2. MRSI was not consistently predictive of outcome in Stage C. Conclusions and Clinical Importance MRSI was predictive of outcome (onset of CHF and all-cause death) in MMVD Stage B2, demonstrating utility as a useful prognostic tool. Echocardiographic LA:Ao can be effectively replaced by radiographically determined LA size in the MRSI formula.