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Review

Clinical Impact of Computational Heart Valve Models

1
Department of Osteopathic Manipulative Medicine, New York Institute of Technology College of Osteopathic Medicine, Northern Boulevard, P.O. Box 8000, Old Westbury, NY 11568, USA
2
Wallace H. Coulter School of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
3
Department of Biomedical Engineering, Francis College of Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
*
Author to whom correspondence should be addressed.
Current address: Department of Chemical Engineering, Faculty of Engineering, University of the West Indies, St. Augustine, Trinidad and Tobago.
Materials 2022, 15(9), 3302; https://doi.org/10.3390/ma15093302
Received: 11 March 2022 / Revised: 26 April 2022 / Accepted: 29 April 2022 / Published: 5 May 2022

Abstract

:
This paper provides a review of engineering applications and computational methods used to analyze the dynamics of heart valve closures in healthy and diseased states. Computational methods are a cost-effective tool that can be used to evaluate the flow parameters of heart valves. Valve repair and replacement have long-term stability and biocompatibility issues, highlighting the need for a more robust method for resolving valvular disease. For example, while fluid–structure interaction analyses are still scarcely utilized to study aortic valves, computational fluid dynamics is used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress, and oscillatory shear index in the thoracic aorta. It has been analyzed that computational flow dynamic analyses can be integrated with other methods to create a superior, more compatible method of understanding risk and compatibility.

1. Introduction

The art of heart valve repairs is constantly developing. Leonardo da Vinci conducted studies in animals and did more than 30 human dissections to accurately interpret the anatomy of fresh specimens and the motion of blood in the beating heart through small metallic tracers. Over half a millennia later, we are still investigating the movement of blood in the beating heart, albeit by means not available to Leonardo. Noteworthy technological improvements [1] have facilitated the evolution of computational methods for heart valve modeling.
The four heart valves include the mitral valve (MV), tricuspid valve (TV), aortic valve (AV), and pulmonary valve (PV). The configurations of the mitral and tricuspid valves are similar, comprising two and three valve leaflets, respectively, with inserted chordae tendineae and anchored to the ventricle walls via papillary muscles. On the other hand, the aortic and pulmonary valves are comprised of three equally sized semilunar cusps or leaflets, which are bound at three commissures. The pressure gradient across each valve controls its opening and closing dynamics. In heart valve disruptions, the design of the valve may be compromised, leading to stenosis (narrowing of the valve) or regurgitation (leakage of the valve). Treatment of these conditions can be surgical, transcatheter, or percutaneous, and include repair or replacement therapies.
Validated using in vitro models, e.g., [2], computational models can be used to (i) aid in the development of diagnostic tools, therapeutic instruments, and innovative prostheses for the treatment of heart valve ailments; (ii) indicate surgical consequences for repair or replacement procedures available for heart valve pathologies, or support pre-procedural planning of appropriate transcatheter or percutaneous therapies; (iii) provide help in medical device regulatory recommendations, and (iv) explain the cause-and-effect associations between cardiovascular biology and hemodynamics [3]. The latter has the prospect to extend our knowledge of disease evolution and progression, thereby allowing the expansion of new translational technologies, diagnoses, devices, and treatment alternatives [4,5].
Computational fluid dynamic (CFD) investigations are a cost-effective mechanism that can be used for the high-resolution evaluation of clinically pertinent flow parameters, e.g., wall shear stress and blood damage. These parameters are of interest during the creation and optimization of manufactured heart valves but are challenging to measure in vivo and/or in vitro. Thus, CFD can be used to augment the understanding gained from clinical and empirical reviews of artificial heart valves. To guide in conducting computational studies of transcatheter heart valve prostheses, a position paper was disseminated by an ISO working group [6], while more recently, the FDA has formulated procedures for evaluating the credibility of computational modeling and simulation in medical device recommendations [7]. Patient-specific modeling has been earning awareness because of its prospect to tailor possible therapies and enhance patient outcomes (e.g., [8,9]). However, there is presently no traditional practice for the patient-specific evaluation of artificial heart valve performance using CFD. Fully patient-specific computational simulations are fairly new and not yet exhaustively validated for a wide spectrum of applications. Similarly, the use of CFD for heart valve modeling is challenged by the intricacy of the interaction between blood flow and the anatomical and/or device configurations concerned, oftentimes necessitating the usage of more costly and convoluted fluid–structure interaction (FSI) models.
This examination emphasizes the role of engineering applications and computational strategies for heart valve modeling, with a focus on the treatment of heart valve conditions. This review summarizes recent computational studies that use various FSI analyses to investigate the heart valves. However, the use of FSI algorithms is still scarce. Hence, recent CFD studies without considering the FSI were also included. Similar review articles on the clinical impact of computational models with different priorities can be found [10,11].

1.1. Chordal Repair/Replacement

Degenerative MV disorder frequently leads to leaflet prolapse due to chordal stretching or rupture and resulting in MV regurgitation [12]. As noted before, computational strategies can expand the knowledge attained from empirical methodologies, such as artificial chordae for mitral and tricuspid valve restorations [13]. For example, Toma et al. and Singh-Gryzbon et al. developed a chordal material properties iteration technique which delivered a good match of the computational and experimental coaptation lines between the leaflets in contact when complete closures are reached in the mitral [14] and tricuspid [15] valves, respectively. Watton et al. used the immersed boundary (IB) approach to simulate a chorded prosthetic MV, lodged in a cylindrical conduit, subject to a physiological recurring fluid flow [16]. While it is inferred that the use of artificial chordae implantation is superior in a range of pathological environments, several issues remain in their usage, particularly the appropriate judgment of their length [17,18]. Computational procedures that unravel these points may enhance patient outcomes. In [19], the resulting regurgitation from 51 distinct potential ruptures in a single-subject subvalvular apparatus is demonstrated. Failure of MV reconstructive strategies usually may be elucidated to extreme or progressive alterations of subvalvular apparatus [20]. As articulated in [21], while the importance of keeping the integrity of papillary muscle, chordae tendineae, and MV cuspid is clear, the knowledge of the highest resistance that a primary tendinea chorda can resist is not known. An examination of the not-so-recent literature on recurrent mitral regurgitation due to ruptured synthetic chordae is delivered in [22]. More current chordal cutting/rupture investigations, and studies on the significance of preserving the MV apparatus, can be located [23,24,25,26,27,28,29,30]. A comparative analysis evaluated the medium-term outcomes of the loop procedure in comparison with the widely embraced leaflet resection approach for the restoration of isolated posterior mitral leaflet prolapse [31]. A comparison of survival aspects of MV repair versus prosthetic substitute for degenerative diseases during twenty years was completed demonstrating long-term data to defend the merit of restoration versus prosthetic valve replacement [32]. In patients with degenerative MV and ischemic heart conditions, MV repair grants a survival benefit over the substitute that becomes apparent about two years after the procedure [33]. When pursuing a higher benchmark for degenerative MV repair, evaluating the durability of MV repair is integral [34]. Long-term consequences of MV repair with chordal replacement were reviewed [35,36,37]. Nonetheless, the concern of how to safely estimate the neochordae length remains [17,18,38,39]. For that cause, techniques are devised to perform beating-heart implantation and off-pump adjustment of neochordal length [40]. Additional examination of enlarged hearts and papillary muscle displacement is essential to retain the complete range of pathologies. Anchoring of neochordae at the papillary muscles, thereby imitating the authentic anatomy, should be favored over the left ventricular apex [41]. In patients with chronic functional ischemic mitral regurgitation, papillary muscle relocation has the prospect to yield reverse left ventricular remodeling. Stitches connected between epicardial discs and individual trigones can be utilized for papillary muscle relocation [42,43]. The papillary muscle relocation strategies are seen to dramatically benefit ischemic patients by impacting the left ventricular form and operation more efficiently compared with the full retention of the mitral subvalvular apparatus if the MV is to be replaced [44]. Some methods are investigated independently versus in combination with other techniques. For instance, in patients with ischemic mitral regurgitation, combined MV repair and revascularization resulted in comparable five-year survival when compared with revascularization alone. Regardless, combined MV repair and revascularization generated less postoperative mitral regurgitation [45].

1.2. Heart Valve Repairs/Devices

Numerous studies have been executed to investigate the impact of medical devices on decreasing the stress in diverse MV regions [46,47,48]. While the physio ring is regarded as an improved rendition of the traditional rigid ring, and the physio ring is more widely employed, long-term results of repair for degenerative MV disease with the classic and physio rings are equivalent [48]. However, the low incidence of reoperation and late cardiac events indicates that the physio ring, with its intrinsic flexibility, presents an indisputable benefit in the application of remodeling strategies in MV reconstruction [49]. Some therapies of choice for chronic ischemic mitral regurgitation annul active annular movement and immobilize the posterior leaflet. In a model of chronic ischemic mitral regurgitation, septal–lateral annular cinching sought to uphold regular annular and leaflet dynamics was tested [50]. A decrease of the annulus with an undersized ring has once seemed to be the select surgical choice to rectify ischemic mitral incompetence [51,52]. Nonetheless, numerous investigations uncovered substantial residual and repeat rates of mild to severe mitral incompetence in 30% of patients within 6 months of surgery [53,54,55,56,57]. Mitral valve replacement strategies in patients with left ventricular dysfunction are often chaperoned with other techniques for more promising left ventricular remodeling compared with total retention of the mitral subvalvular apparatus during MV replacement [58]. Surgical repair is the most routine procedure used to rectify mitral regurgitation. However, the effectiveness of other techniques is still examined. The efficacy of a procedure is specified using an immense combination of factors, such as the durability of the repaired valve as well as the valve’s function and hemodynamics under stress states. Thus, a myriad of studies are carried out to assess these parameters at follow-ups [59,60,61,62]. By approximating edge-to-edge repair (to repair ruptured/elongated chords) with chordal replacement, it was discovered that edge-to-edge repair and chordal replacement are sufficiently suited for the restoration of both the anterior and posterior leaflets [63]. Regardless, among patients experiencing transcatheter MV edge-to-edge repair with the MitraClip device, a pertinent ratio (2–6%) requires open MV surgery within 1 year after unsuccessful clip implantation [64]. Both in vivo and in silico examinations evaluated the combined force transfer from the papillary muscle tips to the MV via the chordae tendineae, and thereby quantified the force shared through the papillary–chordal complex to augment left ventricular ejection [65,66].

1.3. Repair versus Replacement

The outcomes underscore the significance of early detection and review of mitral regurgitation [67]. The most satisfactory short-term and long-term results are gained in asymptomatic patients worked on in state-of-the-art repair centers with low operative mortality and high repair rates [68]. The durability of a successful mitral reconstruction for degenerative MV condition is not consistent, and this should be accepted when asymptomatic patients are proposed early MV repair [69]. Early diagnosis and surgery are paramount as a life-saving standard for infants with acute MV chordal rupture [61,70]. The unique vision of staging of the valvular diseases, newer predictors, and controversy of “watchful waiting” versus “early surgical intervention” for severe, asymptomatic, primary mitral regurgitation are examined in a study that outlines the current interpretation of primary, degenerative mitral regurgitation concerning etiology, complete examination, natural history, and control [71]. Based upon a sounder knowledge of the natural history of mitral regurgitation, the unsatisfactory effects of medical therapies, the adverse consequence of anomalous left ventricular dimensions and function, and manifestations of long-term survival, a directive presently exists for early surgical repair of mitral regurgitation before the start of symptoms and considerable left ventricular dysfunction [72]. New valve pathology after a repair oftentimes results in recurrent mitral regurgitation. Successive mitral re-repair is conducted in nearly half of patients and is associated with outstanding survival, enhanced ejection fraction, and more significant regression in ventricular proportions compared with valve replacement [73,74]. However, an observational analysis found that MV repair in coronary artery bypass grafting patients with ischaemic mitral regurgitation and depressed left ventricular ejection fraction is not incomparable to mitral valve replacement concerning operative early mortality and mid-term survival [75]. A meta-analysis of randomized controlled trials and adjusted observational studies demonstrated that for patients with ischemic mitral regurgitation, MV repair seems to be unassociated with a noteworthy reduction in both early and late all-cause mortality compared with MV replacement [76]. The mechanisms of MV repair failure as well as aspects that meaningfully impact the probability of a successful re-repair can be located in [77]. When comparing MV re-repair versus replacement following failed initial repair, it was uncovered that they are associated with comparable postoperative outcomes [78]. Repair of rheumatic MVs has been met with narrow success. Due to residual diseased leaflet tissue, the hemodynamic obstruction continually endures after repair. An assertive strategy to rheumatic MV repair with extreme excision of the diseased leaflets area, and subvalvular apparatus and subsequent reconstruction, intending to extract all diseased valvular tissue, was devised and executed [79]. Data comparing processes of MV repair and replacement for ischemic mitral regurgitation are primarily restricted to small, non-randomized retrospective trials [80]. The only randomized trial data to investigate this topic indicated no distinction in mortality with either replacement or repair; however, the replacement was shown to be invariably associated with higher rates of mitral regurgitation recurrence [80]. Regardless, the use of replacement heart valves persists to grow due to the raised preponderance of valvular heart disorders resulting from an aging population [81].

1.4. Tissue Engineering

Traditional replacement treatments for heart valve disorders are associated with considerable deficiencies. Heart valve diseases harbor a significant chance of morbidity and mortality. Results are particularly enhanced by valve replacement, but presently available mechanical and biological replacement valves are associated with difficulties of their own. Mechanical valves have a high rate of thromboembolism and demand lifelong anticoagulation. Biological prosthetic valves retain a considerably shorter lifespan, and they are inclined to degradation and ripping. Both types of valves lack the ability to grow, making them specifically troublesome in pediatric patients. Scientific and technological breakthroughs through the last 50 years have put forward diverse surgical options to patients with progressive heart failure encompassing surgical ventricular repair to surgical gene therapy and stem cell replacement of the diseased ventricles [82]. The specialization of tissue engineering has appeared as a compelling option in the quest for improved heart valve replacement designs. One of the tenets behind this vision is the transplantation of living elements, implanted in a suitable scaffold fabric, to the diseased area where the structure becomes merged with patients’ tissue to revive natural function [83]. There are various 3D printing procedures that rely on the types of materials employed. Various types of organs (bone, cartilage, heart valve, liver, and skin) assisted by 3D printed scaffolds and printing methods that are applied in the biomedical specializations were examined [84]. Flanagan and Pandit assembled a review of the advancement that has been made in the evolution of living manufactured heart valve options [85]. Some tissue-engineered heart valves have had clinical success, whereas others have failed, with structural deterioration resulting in patients’ deaths. Blum et al. discussed the need for tissue-engineered heart valves to treat pediatric patients with valve diseases, the history of tissue-engineered heart valves, and a future that would aid from the extension of the reverse translational trend in this area to retain small animal investigations [86]. Regardless, heart valve tissue engineering suffers from narrow long-term performance in vivo because of unbridled tissue remodeling phenomena, such as valve leaflet shortening, which often yields valve failure regardless of the bioengineering procedure employed to generate the implant [87]. The integration of computationally inspired heart valve configurations into tissue engineering procedures could steer tissue remodeling toward long-term functionality in tissue-engineered heart valves [88].

1.5. Imaging Modalities

The European Association of Echocardiography in collaboration with the American Society of Echocardiography has devised the guidance for the use of echocardiography in new transcatheter interventions for valvular heart disorders [89]. The use of echocardiography for catheter-based therapies is paramount for the success of the procedures [90]. Nevertheless, to evaluate the effective orifice area, it was discovered that the results are undervalued when using the 2D transesophageal echocardiography approach compared with the 3D methods (multislice CT, MRI and 3D transesophageal echocardiography) [91,92]. Using 3D echocardiography simultaneously with geometric modeling and rendering strategies, high-resolution, quantitative, 3D procedures for imaging the human MV are created [93]. Tamborini et al. conducted a comparison between different 3D echocardiographic rendering devices in the imaging of percutaneous edge-to-edge MV repair [94]. Echocardiography can determine MV features that are predictive of successful valve repair. However, even with echocardiography specifying MV attributes, repair in hypertrophic cardiomyopathy patients with symptomatic obstruction who experience myectomy, although long-lasting, is achievable in only about half of patients [95]. A multi-center analysis discovered that software modeling utilizing pre-procedural computed tomography angiography is a detailed methodology for indicating the risk of mild and severe mitral regurgitation due to paravalvular leak after transcatheter MV replacement [96]. The function of cardiac computed tomography for assessing the MV has been restricted since echocardiography is the primary form of evaluation. Yet, recent advances in cardiac computed tomography have facilitated thorough evaluation of the anatomy and geometry of the MV [97].

1.6. Transcatheter Repairs

Percutaneous intervention for MV disorder has been designated as an alternative to open surgical repair, especially in high-risk and inoperable candidates [98]. With procedures completed earlier in disease advancement and improved patient longevity, the demand for a repeat intervention is not rare. With the associated dangers of reoperation and patient comorbidities, percutaneous procedures for acute or delayed failure after ring annuloplasty are arising [99]. A myriad of catheter-based techniques for patients with regurgitant as well as stenotic valvular disease is presently at disposal. A thorough understanding of mitral valvular anatomy is vital for the selection of patients, the implementation of devices, and further improvements of these transcatheter practices if they are ultimately to deliver procedural and clinical triumph [100,101]. For mitral stenosis employing either a single balloon or double-balloon procedure, percutaneous MV dilatation is routinely executed. Computational approaches are utilized to compare the two techniques [102]. A screening algorithm to evaluate anatomical eligibility for transcatheter MV replacement in patients with severe mitral regurgitation, based on simple multislice computed tomography measures was designed [103]. The existing overall range of interventional treatment options enables patient-oriented therapies individually targeting various mitral regurgitation pathologies. The current variety of transcatheter treatments for relevant mitral regurgitation is debated in [104]. Similarly, new transcatheter procedures to execute tricuspid annuloplasty are unwinding and are presented in the clinical practice. Nonetheless, clinical experience is limited [105]. Furthermore, present refinements in transcatheter valvular interventions have resulted in a growing market for refined cardiac imaging to help conduct these operations [106]. A summary of fundamental notions linking to transcatheter MV replacement pre-procedural planning, with distinct priority on imaging-based techniques for indicating transcatheter MV replacement-related left ventricular outflow tract obstruction can be located in [107,108].

2. Computational Simulations

To determine the coupling between the fluid and structural domains, FSI strategies are utilized. Particularly in computational simulations meant to imitate the functions inside the human body, intricate dynamics are present, e.g., heart valves opening and closing every second interacting with blood flow. Consequently, for physiologically authentic simulations, the fluid dynamics associated with the valves, the structural mechanics of the valves, and tissue characteristics, should be modeled concurrently. Nonetheless, standard FSI studies present several challenges, e.g., considerable extra computation time.
FSI simulations can be separated into three significant classifications: (1) Pseudo-state simulations are generally used to investigate the downstream flow domains of heart valves under the supposition that the valve is unmoving, and they can be modeled utilizing ordinary computational fluid dynamics strategies for flow fields [109]. (2) One-way FSI lets heart valves move under a stipulated geometric deformation. The prescribed structure dynamic movement impacts the fluid flow but not contrariwise. In two-way FSI (3), the most demanding type of FSI simulation, the structural and fluid fields influence one another. The structural model of a two-way FSI solver requires adequately representing material properties and the interaction between the leaflets and the surrounding fluid. Naturally, most two-way FSI solvers can solve one-way FSI problems.
Two techniques are utilized for the coupling between the fluid and structure domains. (A) Partitioned technique: The fluid and solid domains are treated individually with two separate solvers (Figure 1a). Communication between the two solvers is passed along their domain interface. Since each domain is solved employing a different solver, autonomous numerical algorithms can be involved to solve the fluid and solid equations. Consequently, less memory storage is demanded compared to the monolithic approach. However, in the FSI heart valves simulations, which typically include large deformations, this technique tends to face converge issues due to stability problems [109]. (B) Monolithic approach: The fluid and structural domains are solved simultaneously by discretizing the problem into a single system of equations employing a single numerical algorithm [110]. This generates fewer convergence issues since the joint impact of the two domains on one another is incorporated directly. However, for extensive 3D problems, with a high number of degrees of freedom, a prohibitive quantity of memory storage is required.
An alternative method to classify FSI techniques is to (1) body-fitted and (2) non-body-fitted methods. This categorization depends on whether the computational fluid domain mesh conforms to the borders of the computational solid domain mesh. The Arbitrary Lagrangian–Eulerian (ALE) approach is an illustration of a body-fitted method, and the Immersed-Boundary (IB) method is one of the non-body-fitted methods. The IB approach is an efficient way of modeling fluid–structure interactions. Numerical simulations employing coupled MV and left ventricle models are devised utilizing IB and finite element methods (FEM) [111]. An FSI model of the left atrium and MV employing an IB-FEM framework is utilized in [112] to examine the impacts of diverse pathological conditions. Regardless, it bears two major constraints: namely, the difficulty of use and capacity to model static loading. Additionally, one other thing can be detected in all the IB analyses, i.e., 3D models employed seem to be geometrically streamlined with the purpose of evading computational instability and convergence problems.
Thus far, the ALE approach is the most traditional technique embraced in industrial applications. This conforming mesh method divides the computational domains associated with the structure and fluid. Considering the extensive deformation of the heart valve structures together with the connection between the fluid and solid elements, it demands mesh adaptations for the fluid domain, which significantly diminishes computational efficiency and results in poor mesh quality. Since remeshing is essential, it may result in artificial diffusivity and instabilities. The IB method embeds the structure to the static fluid mesh implicitly, which delivers a significant benefit for simulating largely moving/morphing structures. Nevertheless, the near-wall flow resolution of the leaflets of the IB approach may be inadequate to the ALE method.
Peskin et al., in 1997, presented FSI simulations in prosthetic and biological heart valve models with the muscular heart wall retained. Their models depicted the capacity to apply Navier–Stokes equations to moving solid immersed boundaries [113]. In 2003, Tang et al. employed a 3D thick-wall model to imitate blood flow in the carotid arteries and presented asymmetric stenosis to quantify the impact of stenosis while mimicking the pressure conditions on blood flow and artery contraction [114]. This strategy was then expanded upon, including geometries reconstructed from CT scans well resembling the intricate anatomy of the human artery [115,116]. The usage of traditional mesh-based numerical procedures for biomedical applications remains a challenge, and it is nevertheless the standard approach to streamline the computational models by skipping the fluid domain [117]. However, lately, studies can be encountered demonstrating the benefit of smoothed-particle hydrodynamics (SPH) techniques, as shown in Figure 1b, for accurately executing simulations even within the context of blood flow and thrombosis [118]. A more thorough overview of FSI algorithms employed to simulate heart valves can be found in [119].
The intricacy of computational simulations that involve heart valves (e.g., complex geometries and large deformations) makes SPH well suited to execute these FSI calculations, namely SPH methods mixed with high-order FEM. Employing SPH methods brings numerical stability because the communication between the solid and fluid domains is fairly straightforward to treat numerically. Moreover, it is more manageable to parallelize SPH. Consequently, it is achievable to run FSI simulations with convoluted geometries, i.e., conserving all their geometrical details; and, at the same time, maintaining the simulations numerically steady, accurate, and parallelized on a standard GPU workstation. Thus, the user can run these complicated simulations “under the table” rather than on large supercomputers, with the typical runtime being only hours/days as opposed to weeks/months.
The following literature cited is divided according to the four heart valves (Figure 2).

3. Mitral Valve

Computational hemodynamic simulations employing conventional numerical strategies are conducted to apprehend the impact of MV leaflets on blood flow [120,121]. Similarly, the vortex formation process inside the left ventricle is investigated concerning the dynamics of the mitral leaflets while they interact with the flow crossing the valve during diastole [122,123,124,125]. A computational analysis demonstrated that the presence of the MV and the shape of its leaflets significantly qualifies the building and development of vortex structures in the left ventricle [126]. Similarly, computational techniques are employed to evaluate MV leaflet in-plane strains from clinical images [127], while other investigations concentrate on the MV chordae, e.g., to quantify their load-dependent adaptations in the collagen fiber architecture for the strut chordae tendineae-leaflet insertion [128].
The intricacy of heart valve geometries, mixed with the extensive deformations they experience with every heartbeat between their fully opened and closed positions, make SPH well suited for running FSI computations. The SPH approach was illustrated and validated in several reports on MV closure [14,66,129,130]. Henceforward, it was utilized to evaluate several diseased MV states [19,131,132,133] and applications of medical devices developed to rectify them [46,134,135,136]. In addition to the MV, other valves have been researched using the same procedures [15,130,137,138,139,140]. The SPH technique has been validated to investigate the hemodynamics of the left ventricle [141]. Interaction between bioprosthetic heart valves and blood flow was alike analyzed employing SPH [142].
Chordal transposition is employed in MV repair [143], yet the impacts of second-order chordae transection on valve operation have not been broadly investigated. In vitro experimentations utilizing excised porcine valves indicate that second-order chordae may intercede leaflet tethering in the setting of apical displacement of papillary muscles, as might be witnessed in patients with ischemic mitral regurgitation [144]. Occasional investigations assessed leaflet coaptation, 3D anterior MV leaflet shape, and valve competence after clipping anterior second-order chordae in vivo [27] and in silico [19]. In vitro examinations using stress–strain analysis on excised porcine mitral chordae by Kunzelman and Cochran [145] have ascertained that primary (first-order) chordae are considerably more inflexible (with higher stress at any given degree of strain) than second-order chordae. Accordingly, it was hypothesized that due to their number and mechanical properties, primary chordae endure the prevalence of systolic pressure load exerted on the mitral leaflets. For instance, in [19], 51 potential chordal ruptures on a single MV were examined. A primary chord (one with a large diameter) was clipped in two distinct locations. Yet, one cut resulted in considerable regurgitation, and the other cut (on the same chord) did not particularly transform the regurgitant orifice area. In that singular location, a large number of second-order chords averted the leaflet prolapse.
During transcatheter MV implantation, encroachment on the left ventricular outflow tract may generate a flow obstruction. Therefore, the proper placement and dimensions of mitral prostheses in transcatheter MV implantation is vital [108]. Patient-specific CFD simulations of transcatheter MV implantation with various cardiac anatomy and insertion inclinations were conducted to anticipate the consequence of transcatheter MV implantation exploiting image-based computational models [146]. The quantification of structural and hemodynamic variables by computational modeling may foster more accurate prognoses of the left ventricular outflow tract obstruction in transcatheter MV replacement, especially for patients who are evaluated to bear a marginal risk of obstruction [147].
Image-based defined displacement can be executed to use the patient-specific shifting of the ventricle in the computational MV investigations. In [148], their pipeline contains image processing of the left ventricle and the MV and numerical examination of cardiac hemodynamics in a moving domain with image-based specified displacement. Patient-specific geometry and activity of the left ventricle are evaluated employing the ALE strategy, while the reconstructed MV is engrossed in the computational domain utilizing a resistive method. Hemodynamic testing employing 3D printing and CFD preoperatively may deliver more additional data in mitral repair than a conventional image dataset [149]. Computational investigations supply a visualization of flow patterns (in both long- and short-axes), which then can be quantified with flow analyses. It was discovered that in comparison to a native valve, valve implantation boosted the balance of the mitral inflow staying in the basal area and consequently increased the residual volume in the apical region [150]. Computational models can additionally be employed to optimize the treatment chances; e.g., FEM is used to compare several indirect mitral annuloplasty percutaneous restoration practices to determine the least-invasive remedies for a considerable inoperable patient population [151].
Nevertheless, whether stress distributions from these computational models translate into concrete and applicable intraoperative conclusions is debatable. For instance, a mere slight modification in the neochordae location or length could seriously transform the leaflet action and stress allocation in the MV computational model. It is irrational to anticipate that such diminutive adaptations could be executed by a surgeon operating in a restricted field of view [152]. While computational modeling of the MV is a hopeful path to enhance the surgical outcomes, the intricate MV geometry thwarts the usage of simplified models. In addition, the absence of comprehensive in vivo geometric data raises considerable challenges in the evolution of patient-specific computational models [153]. Nonetheless, geometry does play a major role in MV mechanics and thus highly affects the precision of computational models emulating MV function and repair [154]. While it is widely debated that conserving the intricacy of the complete mitral apparatus is essential for attaining practical results computationally [155,156,157], there is likewise demand for fast image-based MV simulations employing individualized semi-automatically produced computational models of MV geometries [158]. Despite the unprecedented advancement in artificial intelligence, numerical algorithms, computer capacity, and data (and its increasing effect on industrial/monetary refinement), the evolution of computational models that can be solved with lower computational endeavor is always reasonable and unremitting [159,160]. For instance, a semi-automated framework that incorporates machine learning image analysis with geometrical and biomechanical models to create a patient-specific MV manifestation that integrates image-derived material properties is formed [161]. It is worth mentioning that when extracting an MV geometry from the identical set of medical images, considerable discrepancies can be encountered when segmented by distinct users [162,163]. The sensitivity of MV model execution to the precision of the input geometry is addressed [164]. Three distinct chordae models, namely elaborate, ‘pseudo-fiber’, and simplified chordae, are compared to resolve how diverse chordae representatives influence the dynamics of the MV [165]. Investigations on the inter-user variability of landmarks in MV segmentation deduce that errors delivered as a result of the user dependency were comparable to the deviations of computed hemodynamics [166]. A review outlining the state-of-the-art modeling of the MV, including stationary and dynamics models, models with FSI, and models with the left ventricle interaction, can be located in [167]. A summary of pertinent MV conditions, and the development of numerical models of surgical valve repair strategies, can likewise be found in [168].

4. Tricuspid Valve

Tricuspid regurgitation is an ordinary discovery present in a considerable number of asymptomatic patients. However, mild or more harmful tricuspid regurgitation is associated with a poor prognosis [169]. The investigations highlight that the spectrum of TV disorders is beyond that of the annulus and the leaflets [170,171]. There is no class I indication for surgical remedy of isolated functional tricuspid regurgitation in the existing policies [172,173]. On the grounds that right heart failure and pulmonary hypertension are typical disorders in these patients [174], intrahospital mortality after isolated tricuspid surgery is around 10% [175]. Mechanically induced transformations in the TV extracellular matrix structural elements, e.g., collagen fiber spread and dispersal (to resolve the overall macro-scale tissue reactions and thereafter its function/malfunction in physiological/pathophysiological states) are quantified in [176]. Computational modeling can be employed to enhance the interpretation of TV biomechanics and addendum understanding attained from bench-top and large animal investigations. A computational model of the TV, using high-resolution micro-CT imaging and FSI simulations, was produced by Singh-Gryzbon et al. [15]. A computational multi-scale procedure was employed to analyze mechanically induced transformations in TV anterior leaflet microstructure [177]. The computational investigation concerning TVs is presently inadequate, and most analyses still concentrate on the development of structural models rather than engaging FSI calculations. For instance, three FEMs were assembled from human subjects with healthy TVs from CT images incorporating detailed leaflet geometries, realistic nonlinear anisotropic hyperelastic material properties of human TV, and physiological boundary conditions tracked from CT images [178]. As of late, an FEM of one porcine TV geometry was formed to study how diverse pathological disorders impact the general biomechanical function of the TV [179]. There were three immediate observations from that examination. Firstly, the outcomes of the papillary muscle displacement revealed more prominent inconstancies in the TV biomechanical function. Secondly, compared to uniform annulus dilation, the nonuniform dilation rendered more apparent differences in the stresses and strains for the three TV leaflets. Finally, outcomes of pulmonary hypertension exhibited opposing tendencies compared to the papillary muscle displacement and annulus dilation scenarios.
Some of the facts that have been fathered during the development and investigation of MV devices can also be applied to the TV. An in-depth understanding of the bizarre anatomy of the TV and of the right heart chambers, with disparities and similitudes between the two atrioventricular valves, is essential to overcoming the characteristic challenges connected to transcatheter TV treatments [180,181].

5. Aortic Valve

Even with the current improvements in computer technology, and numerical algorithms, that permit the inclusion of complex truly patient-specific geometries together with FSI strategies, designing simpler (e.g., structural only) analysis techniques for the simulation of AV closure will always be in demand [182]. An outline of numerical approaches for FSI models of AVs can be located in [183]. A more current assessment of general computational strategies for the aortic heart valve and its replacements can be encountered in [184].
A string of large eddy simulations validated by particle image velocimetry was completed on physiologically representative aortic stenosis models to systematically depict the blood flow in mild, moderate, and severe aortic stenoses [185]. Employing two patient-specific aortas diagnosed to carry pathological dilation of the ascending segment, a computational hemodynamics approach was devised to examine how the morphotype and the functional state of AV would impact the attributes of blood flow in aortas with pathological dilation, particularly the intensity and diffusion of flow turbulence [186]. The postoperative ventricular hemodynamics of substituting both aortic and MVs are not sufficiently comprehended. A computational FSI analysis was employed to generate an improved interpretation of this outcome by modeling a left ventricle with the aortic and MVs substituted with bioprostheses [187]. Other, more superficial, numerical investigations of patient-specific left ventricular models with both mitral and AVs using FSI calculations can be encountered [188,189].
Functional 3D modeling has developed to incorporate a hemodynamically relevant AV model, where multi-material 3D printing was employed to beget patient-specific functional models that were then validated utilizing Gorlin catheter-based and Doppler continuity-based techniques [190]. A proof-of-concept analysis integrating 3D FSI models with idealized geometries indicates that there are distinct discrepancies across haemodynamics and valve mechanics associated with bicuspid AV phenotypes, which may be essential to successive functions associated with their pathophysiology processes [191]. The distinctions in hemodynamics and mechanical properties of bicuspid AVs with various phenotypes throughout the cardiac cycle using FSI calculations were discovered [192]. The conclusions of that investigation imply distinctive contrasts in the hemodynamic characteristics and valve mechanics of different bicuspid AV phenotypes, including various severity of stenosis, flow patterns, and leaflet strain, which may be vital for the prognosis of other ensuing aortic disorders and differential treatment plan for specific bicuspid AV phenotype. While FSI calculations are yet barely employed to study AVs, CFD is utilized to evaluate the impact of various AV morphologies on velocity profiles, flow patterns, helicity, wall shear stress, and oscillatory shear index in the thoracic aorta [193].

6. Pulmonary Valve

The PV holds an equally consequential function in the circulatory system. However, there are remarkably few mathematical models to accurately emulate its function. Some models were devised employing simplified geometries and without accounting for the FSI [194]. A pilot study sought to prove the feasibility of reconstructing right ventricle action and simulating intracardiac flow in corrected tetralogy of Fallot patients, solely employing traditional cardiac MRI and an IB approach [195]. The impact of a percutaneous PV reducer on hemodynamics in dilated right ventricle outflow tract is analyzed by computational modeling [196]. Similarly, a reduced-order computational technique was presented as an efficient design analysis of a reducer stent to be percutaneously implanted in dilated right ventricular outflow tracts [197]. Since local shear stress and pressure are predictive for intimal hyperplasia and wall deterioration, CFD computation was employed to select the optimal degree of oversizing for a 12 mm native right ventricle outflow tract with the expectancy that the local hemodynamics may clarify intimal hyperplasia [198]. Likewise, to uncover the impacts of conduit oversizing on the hemodynamics observed after conduit implantation and outgrowth, three different sizes of valved conduits, including the largest possible conduit size, virtually implanted in a child-sized healthy pulmonary artery and the related adult-sized model were studied employing CFD [199].

7. Discussion

Valvular heart disorders are a prominent health burden [200]. The remedies for such conditions depend on medication, valve restoration, and replacement with artificial (mechanical and bioprosthetic) heart valves. However, as outlined, long-term stability and biocompatibility issues are conveyed, emphasizing the demand for developing more long-lasting and efficacious replacements [201]. They may assist in the evolution of unexplored or improved diagnostic mechanisms and therapeutic devices, and they can help anticipate patient outcomes. Computational procedures that unravel issues, such as flow patterns, wall stress, and anatomic eligibility, may enhance valve replacement patient outcomes.
Many computational investigations sidestep employing FSI computations. Computational fluid dynamics is still more standard. Simplified open-source software frameworks for cardiovascular integrated modeling and simulation are devised [202]. The purpose is to concoct a software environment that delivers powerful computational hemodynamics tools accessible to a broad audience. To attain that goal, model sharing and reproducibility examinations in scholarly publishing are urged to improve the quality of modeling and simulation analyses, which would also inform future users of computational models [203].
It is reasoned that computational simulations of heart valve closures ought to be patient-specific for them to be practical. To maintain patient-specificity, they need to employ geometries and boundary/initial conditions without simplifications and to retain the FSI calculations. Computational simulations bear the prospect of the predictive capability to determine if a valve would more assumably benefit from restoration or replacement. The mixture of smoothed-particle hydrodynamics and the high-order finite element method delivers the capacity to maintain the calculations integrity while minimizing needed runtime on the GPU workstations. Nevertheless, many simulations remain somewhat costly within a clinical context. In addition, the inevitable inter-user variability appears to be partially accountable for errors in calculated hemodynamics, specifically in complex valve geometries, such as the MV. Furthermore, the employment of adequately validated models is vital [204]. Similarly, just like detailed patient-specific geometries, proper realistic boundary and initial conditions need to be utilized to preserve the authenticity of the simulations. There are processes occurring simultaneously on molecular, cellular, and organ levels. Multiscaled computer models are developed to incorporate these levels into a single model. For example, Campbell et al. outlined an approach to create patient-specific computer models that integrate genomic, proteomic, imaging, and functional data to predict how each patient would respond to possible therapeutic interventions [205]. Other algorithms are developed to implement realistic heart rhythms [206]. The objective of computational modeling is to seize all that we know about disorders and to generate improved treatments tailored to the conditions of individuals, which is usually referred to as ‘computational medicine’ [207]. Computational medicine is applied in a myriad of areas, such as cancer, diabetes, cardiology, neurology, and so on [208]. However, more additional advancements in translating these computational methods to the clinic are essential [209,210]. The forthcoming computational medicine will also have to integrate more commonly the use of artificial intelligence to rectify the algorithm mistakes in order to enhance the predictive model confidence [211]. The advances in artificial intelligence and precision medicine are posed to revolutionize health care [212].

Author Contributions

Conceptualization, M.T., S.S.-G., Z.W. and A.P.Y.; methodology, M.T., S.S.-G., Z.W. and A.P.Y.; formal analysis, M.T., S.S.-G., E.F., Z.W. and A.P.Y.; investigation, M.T., S.S.-G., E.F., Z.W. and A.P.Y.; resources, M.T., S.S.-G., Z.W. and A.P.Y.; data curation, M.T., S.S.-G., E.F., Z.W. and A.P.Y.; writing—original draft preparation, M.T., S.S.-G., E.F., Z.W. and A.P.Y. writing—review and editing, M.T., S.S.-G., E.F., Z.W. and A.P.Y.; supervision, M.T. and A.P.Y.; project administration, M.T. and A.P.Y.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MVmitral vavle
TVtricuspid valve
AVaortic valve
PVpulmonary valve
CFDcomputational fluid dynamics
ISOinternational organization for standardization
FDAfood and drug administration
FSIfluid–structure interaction
IBimmersed boundary
FEMfinite-element method
2Dtwo-dimensional
3Dthree-dimensional
CTcomputed tomography
MRImagnetic resonance imaging
ALEarbitrary Lagrangian–Eulerian
SPHsmoothed-particle hydrodynamics
GPUgraphics processing unit

References

  1. Fedele, M.; Quarteroni, A. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. Int. J. Numer. Methods Biomed. Eng. 2021, 37, e3435. [Google Scholar] [CrossRef]
  2. Stephens, S.E.; Kammien, A.J.; Paris, J.C.; Applequist, A.P.; Ingels, N.B.; Jensen, H.K.; Rodgers, D.E.; Cole, C.R.; Wenk, J.F.; Jensen, M.O. In Vitro Mitral Valve Model with Unrestricted Ventricular Access: Using Vacuum to Close the Valve and Enable Static Trans-Mitral Pressure. J. Cardiovasc. Transl. Res. 2022, 1–10. [Google Scholar] [CrossRef]
  3. Atkins, S.; McNally, A.; Sucosky, P. Mechanobiology in Cardiovascular Disease Management: Potential Strategies and Current Needs. Front. Bioeng. Biotechnol. 2016, 4, 79. [Google Scholar] [CrossRef] [PubMed][Green Version]
  4. Dasi, L.P.; Sucosky, P.; De Zelicourt, D.; Sundareswaran, K.; Jimenez, J.; Yoganathan, A.P. Advances in Cardiovascular Fluid Mechanics: Bench to Bedside. Ann. N. Y. Acad. Sci. 2009, 1161, 1–25. [Google Scholar] [CrossRef] [PubMed]
  5. Toma, M.; Addepalli, D.; Chan-Akeley, R. The Intricacies of Computational Medical Research: An Advanced Study Approach. Recent Dev. Med. Med. Res. 2021, 4, 71–83. [Google Scholar] [CrossRef]
  6. Wei, Z.A.; Sonntag, S.J.; Toma, M.; Singh-Gryzbon, S.; Sun, W. Computational Fluid Dynamics Assessment Associated with Transcatheter Heart Valve Prostheses: A Position Paper of the ISO Working Group. Cardiovasc. Eng. Technol. 2018, 9, 289–299. [Google Scholar] [CrossRef]
  7. Food and Drug Administration. Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submissions, Draft Guidance for Industry and Food and Drug Administration Staff; U.S. Department of Health and Human Services Food and Drug Administration Center for Devices and Radiological Health: Rockville, MD, USA, 2022.
  8. Ammarullah, M.I.; Afif, I.Y.; Maula, M.I.; Winarni, T.I.; Tauviqirrahman, M.; Akbar, I.; Basri, H.; van der Heide, E.; Jamari, J. Tresca Stress Simulation of Metal-on-Metal Total Hip Arthroplasty during Normal Walking Activity. Materials 2021, 14, 7554. [Google Scholar] [CrossRef] [PubMed]
  9. Jamari, J.; Ammarullah, M.; Saad, A.; Syahrom, A.; Uddin, M.; van der Heide, E.; Basri, H. The Effect of Bottom Profile Dimples on the Femoral Head on Wear in Metal-on-Metal Total Hip Arthroplasty. J. Funct. Biomater. 2021, 12, 38. [Google Scholar] [CrossRef]
  10. Heijman, J.; Sutanto, H.; Crijns, H.J.G.M.; Nattel, S.; Trayanova, A.N. Computational models of atrial fibrillation: Achievements, challenges, and perspectives for improving clinical care. Cardiovasc. Res. 2021, 117, 1682–1699. [Google Scholar] [CrossRef]
  11. Holmes, J.W.; Lumens, J. Clinical Applications of Patient-Specific Models: The Case for a Simple Approach. J. Cardiovasc. Transl. Res. 2018, 11, 71–79. [Google Scholar] [CrossRef]
  12. Adams, D.H.; Rosenhek, R.; Falk, V. Degenerative mitral valve regurgitation: Best practice revolution. Eur. Heart J. 2010, 31, 1958–1966. [Google Scholar] [CrossRef] [PubMed][Green Version]
  13. Boon, R.; Hazekamp, M.; Hoohenkerk, G.; Rijlaarsdam, M.; Schoof, P.; Koolbergen, D.; Heredia, L.; Dion, R. Artificial chordae for pediatric mitral and tricuspid valve repair. Eur. J. Cardio-Thorac. Surg. 2007, 32, 143–148. [Google Scholar] [CrossRef] [PubMed][Green Version]
  14. Toma, M.; Bloodworth, C.H.; Einstein, D.R.; Pierce, E.L.; Cochran, R.P.; Yoganathan, A.P.; Kunzelman, K.S. High-resolution subject-specific mitral valve imaging and modeling: Experimental and computational methods. Biomech. Model. Mechanobiol. 2016, 15, 1619–1630. [Google Scholar] [CrossRef]
  15. Singh-Gryzbon, S.; Sadri, V.; Toma, M.; Pierce, E.L.; Wei, Z.A.; Yoganathan, A.P. Development of a Computational Method for Simulating Tricuspid Valve Dynamics. Ann. Biomed. Eng. 2019, 47, 1422–1434. [Google Scholar] [CrossRef] [PubMed]
  16. Watton, P.; Luo, X.; Singleton, R.; Wang, X.; Bernacca, G.; Molloy, P.; Wheatley, D. Modelling Chorded Prosthetic Mitral Valves using the Immersed Boundary Method. In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA, 1–5 September 2004; Volume 2, pp. 3745–3748. [Google Scholar] [CrossRef]
  17. Ibrahim, M.; Rao, C.; Savvopoulou, M.; Casula, R.; Athanasiou, T. Outcomes of mitral valve repair using artificial chordae. Eur. J. Cardio-Thorac. Surg. 2013, 45, 593–601. [Google Scholar] [CrossRef][Green Version]
  18. Zussa, C.; Polesel, E.; Da Col, U.; Galloni, M.; Valfré, C. Seven-year experience with chordal replacement with expanded polytetrafluoroethylene in floppy mitral valve. J. Thorac. Cardiovasc. Surg. 1994, 108, 37–41. [Google Scholar] [CrossRef][Green Version]
  19. Toma, M.; Bloodworth, C.H.; Pierce, E.L.; Einstein, D.R.; Cochran, R.P.; Yoganathan, A.P.; Kunzelman, K.S. Fluid-Structure Interaction Analysis of Ruptured Mitral Chordae Tendineae. Ann. Biomed. Eng. 2016, 45, 619–631. [Google Scholar] [CrossRef]
  20. Zussa, C.; Frater, R.W.; Polesel, E.; Galloni, M.; Valfré, C. Artificial mitral valve chorde: Experimental and clinical experience. Ann. Thorac. Surg. 1990, 50, 367–373. [Google Scholar] [CrossRef]
  21. Lobo, F.L.; Takeda, F.R.; Brandão, C.M.D.A.; Braile, D.M.; Jatene, F.B.; Pomerantzeff, P.M.A. Study of the traction resistance of mitral valve chordae tendineae. Clinics 2006, 61, 395–400. [Google Scholar] [CrossRef][Green Version]
  22. Bortolotti, U.; Celiento, M.; Pratali, S.; Anastasio, G.; Pucci, A. Recurrent mitral regurgitation due to ruptured artificial chordae: Case report and review of the literature. J. Heart Valve Dis. 2012, 21, 440. [Google Scholar]
  23. Calafiore, A.M.; Refaie, R.; Iacò, A.L.; Asif, M.; Al Shurafa, H.S.; Al-Amri, H.; Romeo, A.; Di Mauro, M. Chordal cutting in ischemic mitral regurgitation: A propensity-matched study. J. Thorac. Cardiovasc. Surg. 2014, 148, 41–46. [Google Scholar] [CrossRef] [PubMed][Green Version]
  24. Fayad, G.; Maréchaux, S.; Modine, T.; Azzaoui, R.; Larrue, B.; Ennezat, P.-V.; Bekhti, H.; Decoene, C.; Deklunder, G.; Le Tourneau, T.; et al. Chordal Cutting VIA Aortotomy in Ischemic Mitral Regurgitation: Surgical and Echocardiographic Study. J. Card. Surg. 2008, 23, 52–57. [Google Scholar] [CrossRef] [PubMed]
  25. Fattouch, K.; Sampognaro, R.; Bianco, G.; Navarra, E.; Moscarelli, M.; Speziale, G.; Ruvolo, G. Implantation of Gore-Tex Chordae on Aortic Valve Leaflet to Treat Prolapse Using “The Chordae Technique”: Surgical Aspects and Clinical Results. Ann. Thorac. Surg. 2008, 85, 2019–2024. [Google Scholar] [CrossRef] [PubMed]
  26. Athanasiou, T.; Chow, A.; Rao, C.; Aziz, O.; Siannis, F.; Ali, A.; Darzi, A.; Wells, F. Preservation of the mitral valve apparatus: Evidence synthesis and critical reappraisal of surgical techniques. Eur. J. Cardio-Thorac. Surg. 2008, 33, 391–401. [Google Scholar] [CrossRef][Green Version]
  27. A Timek, T.; Nielsen, S.L.; Green, G.; Dagum, P.; Bolger, A.F.; Daughters, G.T.; Hasenkam, J.; Ingels, N.B.; Miller, D. Influence of anterior mitral leaflet second-order chordae on leaflet dynamics and valve competence. Ann. Thorac. Surg. 2001, 72, 535–540. [Google Scholar] [CrossRef]
  28. Colli, A.; Manzan, E.; Rucinskas, K.; Janusauskas, V.; Zucchetta, F.; Zakarkaitė, D.; Aidietis, A.; Gerosa, G. Acute safety and efficacy of the NeoChord procedure. Interact. Cardiovasc. Thorac. Surg. 2015, 20, 575–581. [Google Scholar] [CrossRef][Green Version]
  29. A Borger, M.; Yau, T.M.; Rao, V.; E Scully, H.; E David, T. Reoperative mitral valve replacement: Importance of preservation of the subvalvular apparatus. Ann. Thorac. Surg. 2002, 74, 1482–1487. [Google Scholar] [CrossRef]
  30. Smerup, M.; Funder, J.; Nyboe, C.; Høyer, C.; Pedersen, T.F.; Ribe, L.; Ringgaard, S.; Kim, W.Y.; Pedersen, E.M.; Andersen, N.T.; et al. Strut chordal-sparing mitral valve replacement preserves long-term left ventricular shape and function in pigs. J. Thorac. Cardiovasc. Surg. 2005, 130, 1675–1682. [Google Scholar] [CrossRef][Green Version]
  31. Seeburger, J.; Falk, V.; Borger, M.; Passage, J.; Walther, T.; Doll, N.; Mohr, F.W.; Seeburger, J.; Falk, V.; Borger, M.; et al. Chordae Replacement Versus Resection for Repair of Isolated Posterior Mitral Leaflet Prolapse: À Ègalité. Ann. Thorac. Surg. 2009, 87, 1715–1720. [Google Scholar] [CrossRef]
  32. Daneshmand, M.A.; Milano, C.A.; Rankin, J.S.; Honeycutt, E.F.; Swaminathan, M.; Shaw, L.K.; Smith, P.K.; Glower, D.D. Mitral Valve Repair for Degenerative Disease: A 20-Year Experience. Ann. Thorac. Surg. 2009, 88, 1828–1837. [Google Scholar] [CrossRef]
  33. Gillinov, A.; Faber, C.; Houghtaling, P.L.; Blackstone, E.H.; Lam, B.-K.; Diaz, R.; Lytle, B.W.; Sabik, J.F.; Cosgrove, D.M. Repair versus replacement for degenerative mitral valve disease with coexisting ischemic heart disease. J. Thorac. Cardiovasc. Surg. 2003, 125, 1350–1361. [Google Scholar] [CrossRef][Green Version]
  34. Flameng, W.; Meuris, B.; Herijgers, P.; Herregods, M.-C. Durability of mitral valve repair in Barlow disease versus fibroelastic deficiency. J. Thorac. Cardiovasc. Surg. 2008, 135, 274–282. [Google Scholar] [CrossRef] [PubMed][Green Version]
  35. Hata, H.; Fujita, T.; Shimahara, Y.; Sato, S.; Ishibashi-Ueda, H.; Kobayashi, J. A 25-year study of chordal replacement with expanded polytetrafluoroethylene in mitral valve repair. Interact. Cardiovasc. Thorac. Surg. 2014, 20, 463–469. [Google Scholar] [CrossRef][Green Version]
  36. Tabata, M.; Kasegawa, H.; Fukui, T.; Shimizu, A.; Sato, Y.; Takanashi, S. Long-term outcomes of artificial chordal replacement with tourniquet technique in mitral valve repair: A single-center experience of 700 cases. J. Thorac. Cardiovasc. Surg. 2014, 148, 2033–2038. [Google Scholar] [CrossRef][Green Version]
  37. Chiappini, B.; Sanchez, A.; Noirhomme, P.; Verhelst, R.; Rubay, J.; Poncelet, A.; Funken, J.C.; El Khoury, G. Replacement of chordae tendineae with polytetrafluoroethylene (PTFE) sutures in mitral valve repair: Early and long-term results. J. Heart Valve Dis. 2006, 15, 657. [Google Scholar] [PubMed]
  38. Garcia-Villarreal, O.A. eComment. Let’s get real: The problem is how to safely measure the neochordae. Interact. Cardiovasc. Thorac. Surg. 2015, 20, 469. [Google Scholar] [CrossRef][Green Version]
  39. Savic, V.; Pozzoli, A.; Gülme, G.; Demir, H.; Batinkov, N.; Kuwata, S.; Weber, A.; Vogel, R.; Tanner, F.; Zuber, M.; et al. Transcatheter mitral valve chord repair. Ann. Cardiothorac. Surg. 2018, 7, 731–740. [Google Scholar] [CrossRef][Green Version]
  40. Maisano, F.; Cioni, M.; Seeburger, J.; Falk, V.; Mohr, F.W.; Mack, M.J.; Alfieri, O.; Vanermen, H. Beating-heart implantation of adjustable length mitral valve chordae: Acute and chronic experience in an animal model. Eur. J. Cardio-Thorac. Surg. 2011, 40, 840–847. [Google Scholar] [CrossRef]
  41. Weber, A.; Hurni, S.; Vandenberghe, S.; Wahl, A.; Aymard, T.; Vogel, R.; Carrel, T. Ideal site for ventricular anchoring of artificial chordae in mitral regurgitation. J. Thorac. Cardiovasc. Surg. 2012, 143, S78–S81. [Google Scholar] [CrossRef][Green Version]
  42. Jensen, H.; Jensen, M.O.; Vind-Kezunovic, S.; Vestergaard, R.; Ringgaard, S.; Smerup, M.H.; Hønge, J.L.; Hasenkam, J.M.; Nielsen, S.L. Surgical relocation of the papillary muscles in functional ischemic mitral regurgitation: What are the forces of the relocation stitches acting on the myocardium? J. Heart Valve Dis. 2013, 22, 524–531. [Google Scholar]
  43. Jensen, M.O.; Jensen, H.; Honge, J.L.; Hans, N.; Hasenkam, J.M.; Nielsen, S.L. External approach to in vivo force measurement on mitral valve traction suture. J. Biomech. 2012, 45, 908–912. [Google Scholar] [CrossRef] [PubMed]
  44. Yousefnia, M.A.; Dehestani, A.; Saidi, B.; Roshanali, F.; Mandegar, M.H.; Alaeddini, F. Papillary Muscle Repositioning in Valve Replacement for Left Ventricular Dysfunction: Ischemic Mitral Regurgitation. Ann. Thorac. Surg. 2010, 90, 497–502. [Google Scholar] [CrossRef] [PubMed]
  45. Kim, Y.-H.; Czer, L.S.; Soukiasian, H.J.; De Robertis, M.; Magliato, K.E.; Blanche, C.; Raissi, S.S.; Mirocha, J.; Siegel, R.J.; Kass, R.M.; et al. Ischemic Mitral Regurgitation: Revascularization Alone Versus Revascularization and Mitral Valve Repair. Ann. Thorac. Surg. 2005, 79, 1895–1901. [Google Scholar] [CrossRef] [PubMed]
  46. Toma, M.; Einstein, D.R.; Kohli, K.; Caroll, S.L.; Iv, C.H.B.; Cochran, R.P.; Kunzelman, K.S.; Yoganathan, A.P.; Bloodworth, C. Effect of Edge-to-Edge Mitral Valve Repair on Chordal Strain: Fluid-Structure Interaction Simulations. Biology 2020, 9, 173. [Google Scholar] [CrossRef] [PubMed]
  47. Jimenez, J.H.; Liou, S.W.; Padala, M.; He, Z.; Sacks, M.; Gorman, R.C.; Gorman, J.H.; Yoganathan, A.P. A saddle-shaped annulus reduces systolic strain on the central region of the mitral valve anterior leaflet. J. Thorac. Cardiovasc. Surg. 2007, 134, 1562–1568. [Google Scholar] [CrossRef][Green Version]
  48. Sidiki, A.I.; Faybushevich, A.G.; Lishchuk, A.N.; Koltunov, A.N.; Roshchina, E.A. The Carpentier-Edwards Classic And Physio Annuloplasty Rings in Repair of Degenerative Mitral Valve Disease: A Retrospective Study. J. Saudi Heart Assoc. 2020, 32, 224–232. [Google Scholar] [CrossRef]
  49. Accola, K.D.; Scott, M.L.; Thompson, P.A.; Palmer, G.J.; Sand, M.E.; Ebra, G. Midterm Outcomes Using the Physio Ring in Mitral Valve Reconstruction: Experience in 492 Patients. Ann. Thorac. Surg. 2005, 79, 1276–1283. [Google Scholar] [CrossRef]
  50. A Tibayan, F.; Rodriguez, F.; Langer, F.; Zasio, M.K.; Bailey, L.; Liang, D.; Daughters, G.T.; Ingels, N.B.; Miller, D. Does septal-lateral annular cinching work for chronic ischemic mitral regurgitation? J. Thorac. Cardiovasc. Surg. 2004, 127, 654–663. [Google Scholar] [CrossRef][Green Version]
  51. 2006 WRITING COMMITTEE MEMBERS; Bonow, R.O.; Carabello, B.A.; Chatterjee, K.; De Leon, A.C.; Faxon, D.P.; Freed, M.D.; Gaasch, W.H.; Lytle, B.W.; Nishimura, R.A.; et al. 2008 Focused Update Incorporated Into the ACC/AHA 2006 Guidelines for the Management of Patients with Valvular Heart Disease. Circulation 2008, 118, e523–e661. [Google Scholar] [CrossRef][Green Version]
  52. Romano, A.M.; Bolling, S.F. Mitral valve repair as an alternative treatment for heart failure patients. Heart Fail. Monit. 2003, 4, 7–12. [Google Scholar]
  53. McGee, E.C.; Gillinov, A.M.; Blackstone, E.H.; Rajeswaran, J.; Cohen, G.; Najam, F.; Shiota, T.; Sabik, J.F.; Lytle, B.W.; McCarthy, P.M.; et al. Recurrent mitral regurgitation after annuloplasty for functional ischemic mitral regurgitation. J. Thorac. Cardiovasc. Surg. 2004, 128, 916–924. [Google Scholar] [CrossRef] [PubMed][Green Version]
  54. Hung, J.; Papakostas, L.; Tahta, S.A.; Hardy, B.G.; Bollen, B.A.; Duran, C.M.; Levine, R.A. Mechanism of Recurrent Ischemic Mitral Regurgitation After Annuloplasty. Circulation 2004, 110, II-85–II-90. [Google Scholar] [CrossRef] [PubMed][Green Version]
  55. Matsunaga, A.; A Tahta, S.; Duran, C.M. Failure of reduction annuloplasty for functional ischemic mitral regurgitation. J. Heart Valve Dis. 2004, 13, 390–398. [Google Scholar] [PubMed]
  56. Gelsomino, S.; Lorusso, R.; De Cicco, G.; Capecchi, I.; Rostagno, C.; Caciolli, S.; Romagnoli, S.; Da Broi, U.; Stefàno, P.; Gensini, G.F. Five-year echocardiographic results of combined undersized mitral ring annuloplasty and coronary artery bypass grafting for chronic ischaemic mitral regurgitation. Eur. Heart J. 2007, 29, 231–240. [Google Scholar] [CrossRef][Green Version]
  57. A Tahta, S.; Oury, J.H.; Maxwell, J.M.; Hiro, S.P.; Duran, C.M.G. Outcome after mitral valve repair for functional ischemic mitral regurgitation. J. Heart Valve Dis. 2002, 11, 11–18. [Google Scholar]
  58. Yousefnia, M.A.; Mandegar, M.H.; Roshanali, F.; Alaeddini, F.; Amouzadeh, F. Papillary Muscle Repositioning in Mitral Valve Replacement in Patients With Left Ventricular Dysfunction. Ann. Thorac. Surg. 2007, 83, 958–963. [Google Scholar] [CrossRef]
  59. Minardi, G.; Manzara, C.; Pulignano, G.; Luzi, G.; Maselli, D.; Casali, G.; Musumeci, F. Rest and Dobutamine stress echocardiography in the evaluation of mid-term results of mitral valve repair in Barlow’s disease. Cardiovasc. Ultrasound 2007, 5, 17. [Google Scholar] [CrossRef][Green Version]
  60. Seeburger, J.; Borger, M.; Doll, N.; Walther, T.; Passage, J.; Falk, V.; Mohr, F.W. Comparison of outcomes of minimally invasive mitral valve surgery for posterior, anterior and bileaflet prolapse. Eur. J. Cardio-Thorac. Surg. 2009, 36, 532–538. [Google Scholar] [CrossRef]
  61. Minami, K.; Kado, H.; Sai, S.; Tatewaki, H.; Shiokawa, Y.; Nakashima, A.; Fukae, K.; Hirose, H. Midterm results of mitral valve repair with artificial chordae in children. J. Thorac. Cardiovasc. Surg. 2005, 129, 336–342. [Google Scholar] [CrossRef][Green Version]
  62. Croft, L.R.; Jimenez, J.H.; Gorman, R.C.; Gorman, J.H.; Yoganathan, A.P. Efficacy of the Edge-to-Edge Repair in the Setting of a Dilated Ventricle: An In Vitro Study. Ann. Thorac. Surg. 2007, 84, 1578–1584. [Google Scholar] [CrossRef]
  63. Espino, D.M.; Hukins, D.W.L.; Shepherd, D.E.T.; Buchan, K.G. Mitral valve repair: An in-vitro comparison of the effect of surgical repair on the pressure required to cause mitral valve regurgitation. J. Heart Valve Dis. 2006, 15, 375–381. [Google Scholar] [PubMed]
  64. Melillo, F.; Baldetti, L.; Beneduce, A.; Agricola, E.; Margonato, A.; Godino, C. Mitral valve surgery after a failed MitraClip procedure. Interact. Cardiovasc. Thorac. Surg. 2020, 32, 380–385. [Google Scholar] [CrossRef] [PubMed]
  65. Askov, J.B.; Honge, J.L.; Jensen, M.O.; Nygaard, H.; Hasenkam, J.M.; Nielsen, S.L. Significance of force transfer in mitral valve–left ventricular interaction: In vivo assessment. J. Thorac. Cardiovasc. Surg. 2013, 145, 1635–1641.e1. [Google Scholar] [CrossRef] [PubMed][Green Version]
  66. Toma, M.; Jensen, M.; Einstein, D.R.; Yoganathan, A.P.; Cochran, R.P.; Kunzelman, K.S. Fluid–Structure Interaction Analysis of Papillary Muscle Forces Using a Comprehensive Mitral Valve Model with 3D Chordal Structure. Ann. Biomed. Eng. 2015, 44, 942–953. [Google Scholar] [CrossRef][Green Version]
  67. Dalrymple-Hay, M.J.; Bryant, M.; A Jones, R.; Langley, S.M.; A Livesey, S.; Monro, J.L. Degenerative mitral regurgitation: When should we operate? Ann. Thorac. Surg. 1998, 66, 1579–1583. [Google Scholar] [CrossRef]
  68. Enriquez-Sarano, M.; Akins, C.W.; Vahanian, A. Mitral regurgitation. Lancet 2009, 373, 1382–1394. [Google Scholar] [CrossRef]
  69. Flameng, W.; Herijgers, P.; Bogaerts, K. Recurrence of Mitral Valve Regurgitation After Mitral Valve Repair in Degenerative Valve Disease. Circulation 2003, 107, 1609–1613. [Google Scholar] [CrossRef][Green Version]
  70. Asakai, H.; Kaneko, Y.; Kaneko, M.; Misaki, Y.; Achiwa, I.; Hirata, Y.; Kato, H. Acute Progressive Mitral Regurgitation Resulting From Chordal Rupture in Infants. Pediatr. Cardiol. 2011, 32, 634–638. [Google Scholar] [CrossRef]
  71. Maslow, A.D.; Poppas, A.; Apostolidou, E. Primary mitral valve regurgitation: Update and review. Glob. Cardiol. Sci. Pract. 2017, 2017, e201703. [Google Scholar] [CrossRef][Green Version]
  72. Northrup, W.F. Mitral valve repair: We must do a better job. Curr. Cardiol. Rep. 2005, 7, 94–100. [Google Scholar] [CrossRef]
  73. Suri, R.M.; Schaff, H.V.; Dearani, J.A.; Sundt, T.M.; Daly, R.C.; Mullany, C.J.; Enriquez-Sarano, M.; Orszulak, T.A. Recurrent mitral regurgitation after repair: Should the mitral valve be re-repaired? J. Thorac. Cardiovasc. Surg. 2006, 132, 1390–1397. [Google Scholar] [CrossRef] [PubMed][Green Version]
  74. Aphram, G.; De Kerchove, L.; Mastrobuoni, S.; Navarra, E.; Solari, S.; Tamer, S.; Baert, J.; Poncelet, A.; Rubay, J.; Astarci, P.; et al. Re-repair of the failed mitral valve: Insights into aetiology and surgical management. Eur. J. Cardio-Thorac. Surg. 2018, 54, 774–780. [Google Scholar] [CrossRef] [PubMed][Green Version]
  75. Lio, A.; Miceli, A.; Varone, E.; Canarutto, D.; Di Stefano, G.; Della Pina, F.; Gilmanov, D.; Murzi, M.; Solinas, M.; Glauber, M. Mitral valve repair versus replacement in patients with ischaemic mitral regurgitation and depressed ejection fraction: Risk factors for early and mid-term mortality. Interact. Cardiovasc. Thorac. Surg. 2014, 19, 64–69. [Google Scholar] [CrossRef] [PubMed][Green Version]
  76. Takagi, H.; Umemoto, T. Similar Survival After Repair vs Replacement for Ischemic Mitral Regurgitation. Semin. Thorac. Cardiovasc. Surg. 2016, 28, 748–756. [Google Scholar] [CrossRef]
  77. A Moore, R.; Gillinov, A.M.; Burns, D.J.; Pettersson, G.B.; Wierup, P. Techniques for Mitral Valve Re-repair. Oper. Tech. Thorac. Cardiovasc. Surg. 2021, 26, 42–65. [Google Scholar] [CrossRef]
  78. Veerappan, M.; Cheekoty, P.; Sazzad, F.; Kofidis, T. Mitral valve re-repair vs replacement following failed initial repair: A systematic review and meta-analysis. J. Cardiothorac. Surg. 2020, 15, 304. [Google Scholar] [CrossRef]
  79. El Oumeiri, B.; Boodhwani, M.; Glineur, D.; De Kerchove, L.; Poncelet, A.; Astarci, P.; Pasquet, A.; Vanoverschelde, J.-L.; Verhelst, R.; Rubay, J.; et al. Extending the Scope of Mitral Valve Repair in Rheumatic Disease. Ann. Thorac. Surg. 2009, 87, 1735–1740. [Google Scholar] [CrossRef]
  80. Sharma, A.; Agrawal, S.; Goel, S.; Borer, J.S. Surgical Treatment of Ischemic Mitral Regurgitation: Valve Repair Versus Replacement. Curr. Cardiol. Rep. 2017, 19, 3. [Google Scholar] [CrossRef]
  81. Soares, J.S.; Feaver, K.R.; Zhang, W.; Kamensky, D.; Aggarwal, A.; Sacks, M.S. Biomechanical Behavior of Bioprosthetic Heart Valve Heterograft Tissues: Characterization, Simulation, and Performance. Cardiovasc. Eng. Technol. 2016, 7, 309–351. [Google Scholar] [CrossRef][Green Version]
  82. Hetzer, R.; Javier, M.F.D.M.; Wagner, F.; Loebe, M.; Delmo, E.M.J. Organ-saving surgical alternatives to treatment of heart failure. Cardiovasc. Diagn. Ther. 2021, 11, 213–225. [Google Scholar] [CrossRef]
  83. Winston, T.S.; Suddhapas, K.; Wang, C.; Ramos, R.; Soman, P.; Ma, Z. Serum-Free Manufacturing of Mesenchymal Stem Cell Tissue Rings Using Human-Induced Pluripotent Stem Cells. Stem Cells Int. 2019, 2019, 5654324. [Google Scholar] [CrossRef] [PubMed]
  84. Chung, J.J.; Im, H.; Kim, S.H.; Park, J.W.; Jung, Y. Toward Biomimetic Scaffolds for Tissue Engineering: 3D Printing Techniques in Regenerative Medicine. Front. Bioeng. Biotechnol. 2020, 8, 586406. [Google Scholar] [CrossRef] [PubMed]
  85. Flanagan, T.C.; Pandit, A. Living artificial heart valve alternatives: A review. Eur. Cells Mater. 2003, 6, 28–45. [Google Scholar] [CrossRef]
  86. Blum, K.M.; Drews, J.D.; Breuer, C.K. Tissue-Engineered Heart Valves: A Call for Mechanistic Studies. Tissue Eng. Part B Rev. 2018, 24, 240–253. [Google Scholar] [CrossRef]
  87. Butcher, J.T. The root problem of heart valve engineering. Sci. Transl. Med. 2018, 10, eaat5850. [Google Scholar] [CrossRef] [PubMed]
  88. Emmert, M.Y.; Schmitt, B.A.; Loerakker, S.; Sanders, B.; Spriestersbach, H.; Fioretta, E.S.; Bruder, L.; Brakmann, K.; Motta, S.E.; Lintas, V.; et al. Computational modeling guides tissue-engineered heart valve design for long-term in vivo performance in a translational sheep model. Sci. Transl. Med. 2018, 10, eaan4587. [Google Scholar] [CrossRef] [PubMed][Green Version]
  89. Zamorano, J.L.; Badano, L.; Bruce, C.; Chan, K.-L.; Gonçalves, A.; Hahn, R.T.; Keane, M.G.; La Canna, G.; Monaghan, M.J.; Nihoyannopoulos, P.; et al. EAE/ASE recommendations for the use of echocardiography in new transcatheter interventions for valvular heart disease. Eur. Heart J. 2011, 32, 2189–2214. [Google Scholar] [CrossRef]
  90. Shiota, T. Role of echocardiography for catheter-based management of valvular heart disease. J. Cardiol. 2017, 69, 66–73. [Google Scholar] [CrossRef]
  91. Zhou, L.; Wei, H.-Y.; Ge, Y.-L.; Ding, Z.-N.; Shi, H.-W. Comparison of the effective orifice area of prosthetic mitral valves using two-dimensional versus three-dimensional transesophageal echocardiography. J. Int. Med. Res. 2021, 49, 300060521997621. [Google Scholar] [CrossRef]
  92. Chourdakis, E.; Koniari, I.; Kounis, N.G.; Velissaris, D.; Koutsogiannis, N.; Tsigkas, G.; Hauptmann, K.E.; Sontag, B.; Hahalis, G. The role of echocardiography and CT angiography in transcatheter aortic valve implantation patients. J. Geriatr. Cardiol. 2018, 15, 86–94. [Google Scholar] [CrossRef]
  93. Ryan, L.P.; Jackson, B.M.; Eperjesi, T.J.; Plappert, T.J.; John-Sutton, M.S.; Gorman, R.C.; Gorman, J.H. A methodology for assessing human mitral leaflet curvature using real-time 3-dimensional echocardiography. J. Thorac. Cardiovasc. Surg. 2008, 136, 726–734. [Google Scholar] [CrossRef] [PubMed][Green Version]
  94. Tamborini, G.; Mantegazza, V.; Garlaschè, A.; Muratori, M.; Fusini, L.; Ali, S.G.; Cefalù, C.; Italiano, G.; Gripari, P.; Maltagliati, A.; et al. Head to Head Comparison between Different 3-Dimensional Echocardiographic Rendering Tools in the Imaging of Percutaneous Edge-to-Edge Mitral Valve Repair. J. Cardiovasc. Dev. Dis. 2021, 8, 73. [Google Scholar] [CrossRef] [PubMed]
  95. Kaple, R.K.; Murphy, R.T.; DiPaola, L.M.; Houghtaling, P.L.; Lever, H.M.; Lytle, B.W.; Blackstone, E.H.; Smedira, N.G. Mitral Valve Abnormalities in Hypertrophic Cardiomyopathy: Echocardiographic Features and Surgical Outcomes. Ann. Thorac. Surg. 2008, 85, 1527–1535.e2. [Google Scholar] [CrossRef] [PubMed]
  96. Morris, M.F.; Pena, A.; Kalya, A.; Sawant, A.C.; Lotun, K.; Byrne, T.; Fang, H.K.; Pershad, A. Predicting paravalvular leak after transcatheter mitral valve replacement using commercially available software modeling. J. Cardiovasc. Comput. Tomogr. 2020, 14, 495–499. [Google Scholar] [CrossRef]
  97. Kim, J.H.; Kim, E.Y.; Jin, G.Y.; Choi, J.B. A Review of the Use of Cardiac Computed Tomography for Evaluating the Mitral Valve before and after Mitral Valve Repair. Korean J. Radiol. 2017, 18, 773–785. [Google Scholar] [CrossRef] [PubMed]
  98. Feldman, T.; Kar, S.; Elmariah, S.; Smart, S.C.; Trento, A.; Siegel, R.J.; Apruzzese, P.; Fail, P.; Rinaldi, M.J.; Smalling, R.W.; et al. Randomized Comparison of Percutaneous Repair and Surgery for Mitral Regurgitation. J. Am. Coll. Cardiol. 2015, 66, 2844–2854. [Google Scholar] [CrossRef] [PubMed][Green Version]
  99. Joseph, K.M.; Nyman, C. Mitral Valve Annuloplasty Failure and Percutaneous Treatment Options. Curr. Cardiol. Rep. 2021, 23, 140. [Google Scholar] [CrossRef]
  100. Van Mieghem, N.M.; Piazza, N.; Anderson, R.H.; Tzikas, A.; Nieman, K.; De Laat, L.E.; McGhie, J.S.; Geleijnse, M.L.; Feldman, T.; Serruys, P.W.; et al. Anatomy of the Mitral Valvular Complex and Its Implications for Transcatheter Interventions for Mitral Regurgitation. J. Am. Coll. Cardiol. 2010, 56, 617–626. [Google Scholar] [CrossRef][Green Version]
  101. Zamorano, J.L.; González-Gómez, A.; Lancellotti, P. Mitral valve anatomy: Implications for transcatheter mitral valve interventions. EuroIntervention 2014, 10, U106–U111. [Google Scholar] [CrossRef]
  102. Schievano, S.; Kunzelman, K.; A Nicosia, M.; Cochran, R.P.; Einstein, D.R.; Khambadkone, S.; Bonhoeffer, P. Percutaneous mitral valve dilatation: Single balloon versus double balloon. A finite element study. J. Heart Valve Dis. 2009, 18, 28–34. [Google Scholar]
  103. Ludwig, S.; Rübsamen, N.; Deuschl, F.; Schofer, N.; Kalbacher, D.; Schaefer, A.; Koell, B.; Westermann, D.; Reichenspurner, H.; Blankenberg, S.; et al. Screening for transcatheter mitral valve replacement: A decision tree algorithm. EuroIntervention 2020, 16, 251–258. [Google Scholar] [CrossRef] [PubMed]
  104. Koell, B.; Kalbacher, D.; Lubos, E. Current devices and interventions in mitral regurgitation. Herz 2021, 46, 419–428. [Google Scholar] [CrossRef] [PubMed]
  105. Arnold, M.; Haug, J.; Landendinger, M. Tricuspid Annuloplasty: Transcatheter Approaches. Curr. Cardiol. Rep. 2021, 23, 139. [Google Scholar] [CrossRef] [PubMed]
  106. Grover, R.; Ohana, M.; Arepalli, C.D.; Sellers, S.L.; Mooney, J.; Kueh, S.-H.; Kim, U.; Blanke, P.; Leipsic, J.A. Role of MDCT Imaging in Planning Mitral Valve Intervention. Curr. Cardiol. Rep. 2018, 20, 16. [Google Scholar] [CrossRef] [PubMed]
  107. Kohli, K.; Wei, Z.A.; Yoganathan, A.P.; Oshinski, J.N.; Leipsic, J.; Blanke, P. Transcatheter Mitral Valve Planning and the Neo-LVOT: Utilization of Virtual Simulation Models and 3D Printing. Curr. Treat. Options Cardiovasc. Med. 2018, 20, 99. [Google Scholar] [CrossRef]
  108. Yoon, S.-H.; Bleiziffer, S.; Latib, A.; Eschenbach, L.; Ancona, M.; Vincent, F.; Kim, W.-K.; Unbehaum, A.; Asami, M.; Dhoble, A.; et al. Predictors of Left Ventricular Outflow Tract Obstruction After Transcatheter Mitral Valve Replacement. JACC Cardiovasc. Interv. 2019, 12, 182–193. [Google Scholar] [CrossRef]
  109. Ge, L.; Jones, S.C.; Sotiropoulos, F.; Healy, T.M.; Yoganathan, A.P. Numerical Simulation of Flow in Mechanical Heart Valves: Grid Resolution and the Assumption of Flow Symmetry. J. Biomech. Eng. 2003, 125, 709–718. [Google Scholar] [CrossRef]
  110. Toma, M.; Oshima, M.; Takagi, S. Decomposition and parallelization of strongly coupled fluid-structure interaction linear subsystems based on the Q1/P0 discretization. J. Comput. Struct. 2016, 173, 84–94. [Google Scholar] [CrossRef]
  111. Gao, H.; Feng, L.; Qi, N.; Berry, C.; Griffith, B.; Luo, X. A coupled mitral valve—left ventricle model with fluid–structure interaction. Med. Eng. Phys. 2017, 47, 128–136. [Google Scholar] [CrossRef]
  112. Feng, L.; Gao, H.; Griffith, B.; Niederer, S.A.; Luo, X. Analysis of a coupled fluid-structure interaction model of the left atrium and mitral valve. Int. J. Numer. Methods Biomed. Eng. 2019, 35, e3254. [Google Scholar] [CrossRef][Green Version]
  113. Peskin, C.S. Numerical analysis of blood flow in the heart. J. Comput. Phys. 1977, 25, 220–252. [Google Scholar] [CrossRef]
  114. Tang, D.; Yang, C.; Kobayashi, S.; Zheng, J.; Vito, R.P. Effect of Stenosis Asymmetry on Blood Flow and Artery Compression: A Three-Dimensional Fluid-Structure Interaction Model. Ann. Biomed. Eng. 2003, 31, 1182–1193. [Google Scholar] [CrossRef] [PubMed]
  115. Wong, K.; Fong, F.; Wang, D. Computational evaluation of smoothed particle hydrodynamics for implementing blood flow modelling through CT reconstructed arteries. J. Ray Sci. Technol. 2017, 25, 213–232. [Google Scholar]
  116. Nasar, A. Eulerian and Lagrangian Smoothed Particle Hydrodynamics as Models for the Interaction of Fluids and Flexible Structures in Biomedical Flows. Ph.D. Thesis, The University of Manchester (United Kingdom), PQDT-UK & Ireland, Manchester, UK, 2016. [Google Scholar]
  117. Hron, J.; Mádlík, M. Fluid-structure interaction with applications in biomechanics. Nonlinear Anal. Real World Appl. 2007, 8, 1431–1458. [Google Scholar] [CrossRef]
  118. Al-Saad, M.; Suarez, C.A.; Obeidat, A.; Bordas, S.P.A.; Kulasegaram, S. Application of Smooth Particle Hydrodynamics Method for Modelling Blood Flow with Thrombus Formation. Comput. Model. Eng. Sci. 2020, 122, 831–862. [Google Scholar] [CrossRef]
  119. Le, T.B.; Usta, M.; Aidun, C.; Yoganathan, A.; Sotiropoulos, F. Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies. Fluids 2022, 7, 94. [Google Scholar] [CrossRef]
  120. Seo, J.H.; Vedula, V.; Abraham, T.; Lardo, A.C.; Dawoud, F.; Luo, H.; Mittal, R. Effect of the mitral valve on diastolic flow patterns. Phys. Fluids 2014, 26, 121901. [Google Scholar] [CrossRef]
  121. Al-Atabi, M.; Espino, D.M.; Hukins, D.W.L. Computer and Experimental Modelling of Blood Flow through the Mitral Valve of the Heart. J. Biomech. Sci. Eng. 2010, 5, 78–84. [Google Scholar] [CrossRef][Green Version]
  122. Domenichini, F.; Pedrizzetti, G. Asymptotic Model of Fluid–Tissue Interaction for Mitral Valve Dynamics. Cardiovasc. Eng. Technol. 2014, 6, 95–104. [Google Scholar] [CrossRef][Green Version]
  123. Govindarajan, V.; Mousel, J.; Udaykumar, H.S.; Vigmostad, S.C.; McPherson, D.D.; Kim, H.; Chandran, K.B. Synergy between Diastolic Mitral Valve Function and Left Ventricular Flow Aids in Valve Closure and Blood Transport during Systole. Sci. Rep. 2018, 8, 6187. [Google Scholar] [CrossRef]
  124. Bavo, A.; Pouch, A.; Degroote, J.; Vierendeels, J.; Gorman, J.; Gorman, R.; Segers, P. Patient-specific CFD models for intraventricular flow analysis from 3D ultrasound imaging: Comparison of three clinical cases. J. Biomech. 2016, 50, 144–150. [Google Scholar] [CrossRef] [PubMed][Green Version]
  125. Hassani, K.; Karimi, A.; Dehghani, A.; Golpaygani, A.T.; Abdi, H.; Espino, D.M. Development of a fluid-structure interaction model to simulate mitral valve malcoaptation. Perfusion 2018, 34, 225–230. [Google Scholar] [CrossRef] [PubMed][Green Version]
  126. Khalafvand, S.S.; Xu, F.; Westenberg, J.; Gijsen, F.; Kenjeres, S. Intraventricular blood flow with a fully dynamic mitral valve model. Comput. Biol. Med. 2018, 104, 197–204. [Google Scholar] [CrossRef] [PubMed]
  127. Rego, B.V.; Khalighi, A.H.; Drach, A.; Lai, E.K.; Pouch, A.M.; Gorman, R.C.; Gorman, J.H.; Sacks, M.S. A noninvasive method for the determination of in vivo mitral valve leaflet strains. Int. J. Numer. Methods Biomed. Eng. 2018, 34, e3142. [Google Scholar] [CrossRef]
  128. Ross, C.J.; Hsu, M.-C.; Baumwart, R.; Mir, A.; Burkhart, H.M.; Holzapfel, G.A.; Wu, Y.; Lee, C.-H. Quantification of load-dependent changes in the collagen fiber architecture for the strut chordae tendineae-leaflet insertion of porcine atrioventricular heart valves. Biomech. Model. Mechanobiol. 2020, 20, 223–241. [Google Scholar] [CrossRef]
  129. Toma, M.; Einstein, D.R.; Iv, C.H.B.; Cochran, R.P.; Yoganathan, A.P.; Kunzelman, K.S. Fluid-structure interaction and structural analyses using a comprehensive mitral valve model with 3D chordal structure. Int. J. Numer. Methods Biomed. Eng. 2016, 33, e2815. [Google Scholar] [CrossRef] [PubMed][Green Version]
  130. Mao, W.; Caballero, A.; McKay, R.; Primiano, C.; Sun, W. Fully-coupled fluid-structure interaction simulation of the aortic and mitral valves in a realistic 3D left ventricle model. PLoS ONE 2017, 12, e0184729. [Google Scholar] [CrossRef][Green Version]
  131. Caballero, A.; Mao, W.; McKay, R.; Primiano, C.; Hashim, S.; Sun, W. New insights into mitral heart valve prolapse after chordae rupture through fluid–structure interaction computational modeling. Sci. Rep. 2018, 8, 17306. [Google Scholar] [CrossRef]
  132. Mao, W.; Caballero, A.; Hahn, R.T.; Sun, W. Comparative quantification of primary mitral regurgitation by computer modeling and simulated echocardiography. Am. J. Physiol. Circ. Physiol. 2020, 318, H547–H557. [Google Scholar] [CrossRef]
  133. Biffi, B.; Gritti, M.; Grasso, A.; Milano, E.G.; Fontana, M.; Alkareef, H.; Davar, J.; Jeetley, P.; Whelan, C.; Anderson, S.; et al. A workflow for patient-specific fluid–structure interaction analysis of the mitral valve: A proof of concept on a mitral regurgitation case. Med. Eng. Phys. 2019, 74, 153–161. [Google Scholar] [CrossRef]
  134. Toma, M.; Einstein, D.R.; Iv, C.H.B.; Kohli, K.; Cochran, R.P.; Kunzelman, K.S.; Yoganathan, A.P. Fluid-Structure Interaction Analysis of Subject-Specific Mitral Valve Regurgitation Treatment with an Intra-Valvular Spacer. Prosthesis 2020, 2, 65–75. [Google Scholar] [CrossRef]
  135. Caballero, A.; Mao, W.; McKay, R.; Hahn, R.T.; Sun, W. A Comprehensive Engineering Analysis of Left Heart Dynamics After MitraClip in a Functional Mitral Regurgitation Patient. Front. Physiol. 2020, 11, 432. [Google Scholar] [CrossRef] [PubMed]
  136. Caballero, A.; Mao, W.; McKay, R.; Sun, W. Transapical mitral valve repair with neochordae implantation: FSI analysis of neochordae number and complexity of leaflet prolapse. Int. J. Numer. Methods Biomed. Eng. 2019, 36, e3297. [Google Scholar] [CrossRef] [PubMed]
  137. Mao, W.; Li, K.; Sun, W. Fluid–Structure Interaction Study of Transcatheter Aortic Valve Dynamics Using Smoothed Particle Hydrodynamics. Cardiovasc. Eng. Technol. 2016, 7, 374–388. [Google Scholar] [CrossRef][Green Version]
  138. Caballero, A.; Mao, W.; McKay, R.; Sun, W. The impact of balloon-expandable transcatheter aortic valve replacement on concomitant mitral regurgitation: A comprehensive computational analysis. J. R. Soc. Interface 2019, 16, 20190355. [Google Scholar] [CrossRef][Green Version]
  139. Caballero, A.; Mao, W.; McKay, R.; Sun, W. The Impact of Self-Expandable Transcatheter Aortic Valve Replacement on Concomitant Functional Mitral Regurgitation: A Comprehensive Engineering Analysis. Struct. Heart 2020, 4, 179–191. [Google Scholar] [CrossRef]
  140. Dabiri, Y.; Yao, J.; Sack, K.; Kassab, G.S.; Guccione, J.M. Tricuspid valve regurgitation decreases after mitraclip implantation: Fluid structure interaction simulation. Mech. Res. Commun. 2019, 97, 96–100. [Google Scholar] [CrossRef]
  141. Durrwachter, J. Hemodynamics of the Left Ventricle: Validation of a Smoothed-Particle Hydrodynamics Fluid-Structure Interaction Model. Master’s Thesis, Georgia Institute of Technology, Atlanta, GA, USA, 2016. [Google Scholar]
  142. Yuan, Q.; Ye, X. A New Way to Simulate the Fluid Structure Interaction between the Bioprosthetic Heart Valve and Blood: FE-SPH Method; Mechanical Science and Engineering IV; Trans Tech Publications Ltd.: Stafa-Zurich, Switzerland, 2014; Volume 472, pp. 125–130. [Google Scholar]
  143. Marom, G.; Mayo, R.P.; Again, N.; Raanani, E. Numerical Biomechanics Models of the Interaction Between a Novel Transcatheter Mitral Valve Device and the Subvalvular Apparatus. Innov. Technol. Tech. Cardiothorac. Vasc. Surg. 2021, 16, 327–333. [Google Scholar] [CrossRef]
  144. He, S.; Weston, M.W.; Lemmon, J.; Jensen, M.; Levine, R.A.; Yoganathan, A.P. Geometric distribution of chordae tendineae: An important anatomic feature in mitral valve function. J. Heart Valve Dis. 2000, 9, 495–501. [Google Scholar]
  145. Kunzelman, K.S.; Cochran, R.P. Mechanical properties of basal and marginal mitral valve chordae tendineae. ASAIO Trans. 1990, 36, M405–M408. [Google Scholar]
  146. Alharbi, Y.; Otton, J.; Muller, D.W.; Geelan-Small, P.; Lovell, N.H.; Al Abed, A.; Dokos, S. Predicting the outcome of transcatheter mitral valve implantation using image-based computational models. J. Cardiovasc. Comput. Tomogr. 2020, 14, 335–342. [Google Scholar] [CrossRef] [PubMed]
  147. Pasta, S.; Cannata, S.; Gentile, G.; Agnese, V.; Pilato, M.; Gandolfo, C. Simulation of left ventricular outflow tract (LVOT) obstruction in transcatheter mitral valve-in-ring replacement. Med. Eng. Phys. 2020, 82, 40–48. [Google Scholar] [CrossRef] [PubMed]
  148. Fumagalli, I.; Fedele, M.; Vergara, C.; Dede’, L.; Ippolito, S.; Nicolò, F.; Antona, C.; Scrofani, R.; Quarteroni, A. An image-based computational hemodynamics study of the Systolic Anterior Motion of the mitral valve. Comput. Biol. Med. 2020, 123, 103922. [Google Scholar] [CrossRef] [PubMed]
  149. Wang, H.; Song, H.; Yang, Y.; Wu, Z.; Hu, R.; Chen, J.; Guo, J.; Wang, Y.; Jia, D.; Cao, S.; et al. Hemodynamic testing using three-dimensional printing and computational fluid dynamics preoperatively may provide more information in mitral repair than traditional image dataset. Ann. Transl. Med. 2021, 9, 632. [Google Scholar] [CrossRef]
  150. Lantz, J.; Bäck, S.; Carlhäll, C.-J.; Bolger, A.; Persson, A.; Karlsson, M.; Ebbers, T. Impact of prosthetic mitral valve orientation on the ventricular flow field: Comparison using patient-specific computational fluid dynamics. J. Biomech. 2020, 116, 110209. [Google Scholar] [CrossRef]
  151. Galili, L.; Zeira, A.W.; Marom, G. Numerical biomechanics modelling of indirect mitral annuloplasty treatments for functional mitral regurgitation. R. Soc. Open Sci. 2022, 9, 211464. [Google Scholar] [CrossRef]
  152. Padala, M. Patient-specific computational biomechanical modeling to guide mitral valve repair strategy: Practicality and value? J. Thorac. Cardiovasc. Surg. 2018, 155, 606–607. [Google Scholar] [CrossRef][Green Version]
  153. Drach, A.; Khalighi, A.H.; Sacks, M.S. A comprehensive pipeline for multi-resolution modeling of the mitral valve: Validation, computational efficiency, and predictive capability. Int. J. Numer. Methods Biomed. Eng. 2017, 34, e2921. [Google Scholar] [CrossRef]
  154. Khalighi, A.H.; Drach, A.; Bloodworth, C.H.; Pierce, E.L.; Yoganathan, A.P.; Gorman, R.C.; Gorman, J.H.; Sacks, M.S. Mitral Valve Chordae Tendineae: Topological and Geometrical Characterization. Ann. Biomed. Eng. 2017, 45, 378–393. [Google Scholar] [CrossRef]
  155. Toma, M.; Chan-Akeley, R.; Arias, J.; Kurgansky, G.; Mao, W. Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics. Biology 2021, 10, 185. [Google Scholar] [CrossRef]
  156. Toma, M.; Chan-Akeley, R. Biofluid-Biostructure Interaction Analyses Using Comprehensive Patient-Specific Geometries. In Proceedings of the Future Technologies Conference; Springer: Cham, Switzerland, 2020; pp. 1–16. [Google Scholar] [CrossRef]
  157. Imanparast, A.; Fatouraee, N.; Sharif, F. The impact of valve simplifications on left ventricular hemodynamics in a three dimensional simulation based on in vivo MRI data. J. Biomech. 2016, 49, 1482–1489. [Google Scholar] [CrossRef] [PubMed]
  158. Villard, P.-F.; Hammer, P.E.; Perrin, D.P.; Del Nido, P.J.; Howe, R.D. Fast image-based mitral valve simulation from individualized geometry. Int. J. Med Robot. Comput. Assist. Surg. 2018, 14, e1880. [Google Scholar] [CrossRef]
  159. Daub, A.; Kriegseis, J.; Frohnapfel, B. Replication of left ventricular haemodynamics with a simple planar mitral valve model. Biomed. Eng. Biomed. Tech. 2020, 65, 595–603. [Google Scholar] [CrossRef] [PubMed]
  160. Wojtas, K.; Kozłowski, M.; Orciuch, W.; Makowski, Ł. Computational Fluid Dynamics Simulations of Mitral Paravalvular Leaks in Human Heart. Materials 2021, 14, 7354. [Google Scholar] [CrossRef] [PubMed]
  161. Zhang, F.; Kanik, J.; Mansi, T.; Voigt, I.; Sharma, P.; Ionasec, R.I.; Subrahmanyan, L.; Lin, B.A.; Sugeng, L.; Yuh, D.; et al. Towards patient-specific modeling of mitral valve repair: 3D transesophageal echocardiography-derived parameter estimation. Med Image Anal. 2017, 35, 599–609. [Google Scholar] [CrossRef]
  162. Tautz, L.; Neugebauer, M.; Hüllebrand, M.; Vellguth, K.; Degener, F.; Sündermann, S.; Wamala, I.; Goubergrits, L.; Kuehne, T.; Falk, V.; et al. Extraction of open-state mitral valve geometry from CT volumes. Int. J. Comput. Assist. Radiol. Surg. 2018, 13, 1741–1754. [Google Scholar] [CrossRef]
  163. Toma, M.; Lu, Y.; Zhou, H.; Garcia, J.D. Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries. J. Biomed. Phys. Eng. 2021, 11, 115–122. [Google Scholar] [CrossRef]
  164. Bloodworth, C.H.; Pierce, E.L.; Easley, T.F.; Drach, A.; Khalighi, A.H.; Toma, M.; Jensen, M.O.; Sacks, M.S.; Yoganathan, A.P. Ex Vivo Methods for Informing Computational Models of the Mitral Valve. Ann. Biomed. Eng. 2016, 45, 496–507. [Google Scholar] [CrossRef][Green Version]
  165. Feng, L.; Qi, N.; Gao, H.; Sun, W.; Vazquez, M.; E Griffith, B.; Luo, X. On the chordae structure and dynamic behaviour of the mitral valve. IMA J. Appl. Math. 2018, 83, 1066–1091. [Google Scholar] [CrossRef]
  166. Vellguth, K.; Brüning, J.; Tautz, L.; Degener, F.; Wamala, I.; Sündermann, S.; Kertzscher, U.; Kuehne, T.; Hennemuth, A.; Falk, V.; et al. User-dependent variability in mitral valve segmentation and its impact on CFD-computed hemodynamic parameters. Int. J. Comput. Assist. Radiol. Surg. 2019, 14, 1687–1696. [Google Scholar] [CrossRef]
  167. Gao, H.; Qi, N.; Feng, L.; Ma, X.; Danton, M.; Berry, C.; Luo, X. Modelling mitral valvular dynamics-current trend and future directions. Int. J. Numer. Methods Biomed. Eng. 2016, 33, e2858. [Google Scholar] [CrossRef] [PubMed][Green Version]
  168. Morgan, A.E.; Pantoja, J.; Weinsaft, J.; Grossi, E.; Guccione, J.M.; Ge, L.; Ratcliffe, M. Finite Element Modeling of Mitral Valve Repair. J. Biomech. Eng. 2016, 138, 021009. [Google Scholar] [CrossRef][Green Version]
  169. Nath, J.; Foster, E.; Heidenreich, P.A. Impact of tricuspid regurgitation on long-term survival. J. Am. Coll. Cardiol. 2004, 43, 405–409. [Google Scholar] [CrossRef][Green Version]
  170. Utsunomiya, H.; Itabashi, Y.; Mihara, H.; Berdejo, J.; Kobayashi, S.; Siegel, R.J.; Shiota, T. Functional Tricuspid Regurgitation Caused by Chronic Atrial Fibrillation. Circ. Cardiovasc. Imaging 2017, 10, e004897. [Google Scholar] [CrossRef] [PubMed][Green Version]
  171. van Rosendael, P.J.; Kamperidis, V.; Kong, W.K.; van Rosendael, A.R.; van der Kley, F.; Marsan, N.A.; Delgado, V.; Bax, J.J. Computed tomography for planning transcatheter tricuspid valve therapy. Eur. Heart J. 2016, 38, 665–674. [Google Scholar] [CrossRef] [PubMed][Green Version]
  172. Baumgartner, H.; Falk, V.; Bax, J.J.; De Bonis, M.; Hamm, C.; Holm, P.J.; Iung, B.; Lancellotti, P.; Lansac, E.; Rodriguez Muñoz, D.; et al. 2017 ESC/EACTS Guidelines for the management of valvular heart disease. Eur. J. Cardio-Thorac. Surg. 2017, 52, 616–664, Correction: Eur. J. Cardio-Thorac. Surg. 2017, 52, 832. [Google Scholar] [CrossRef]
  173. Otto, C.M.; Nishimura, R.A.; Bonow, R.O.; Carabello, B.A.; Erwin, J.P.; Gentile, F.; Jneid, H.; Krieger, E.V.; Mack, M.; McLeod, C.; et al. 2020 ACC/AHA Guideline for the Management of Patients With Valvular Heart Disease: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2021, 143, e35–e71. [Google Scholar] [CrossRef]
  174. Topilsky, Y.; Khanna, A.D.; Oh, J.K.; Nishimura, R.A.; Enriquez-Sarano, M.; Jeon, Y.B.; Sundt, T.M.; Schaff, H.; Park, S.J. Preoperative Factors Associated With Adverse Outcome After Tricuspid Valve Replacement. Circulation 2011, 123, 1929–1939. [Google Scholar] [CrossRef][Green Version]
  175. Zack, C.J.; Fender, E.A.; Chandrashekar, P.; Reddy, Y.N.; Bennett, C.E.; Stulak, J.M.; Miller, V.M.; Nishimura, R.A. National Trends and Outcomes in Isolated Tricuspid Valve Surgery. J. Am. Coll. Cardiol. 2017, 70, 2953–2960. [Google Scholar] [CrossRef]
  176. Pant, A.D.; Thomas, V.S.; Black, A.L.; Verba, T.; Lesicko, J.G.; Amini, R. Pressure-induced microstructural changes in porcine tricuspid valve leaflets. Acta Biomater. 2018, 67, 248–258. [Google Scholar] [CrossRef]
  177. Thomas, V.S.; Lai, V.; Amini, R. A computational multi-scale approach to investigate mechanically-induced changes in tricuspid valve anterior leaflet microstructure. Acta Biomater. 2019, 94, 524–535. [Google Scholar] [CrossRef] [PubMed]
  178. Kong, F.; Pham, T.; Martin, C.; McKay, R.; Primiano, C.; Hashim, S.; Kodali, S.; Sun, W. Finite Element Analysis of Tricuspid Valve Deformation from Multi-slice Computed Tomography Images. Ann. Biomed. Eng. 2018, 46, 1112–1127. [Google Scholar] [CrossRef] [PubMed]
  179. Laurence, D.W.; Johnson, E.L.; Hsu, M.; Baumwart, R.; Mir, A.; Burkhart, H.M.; Holzapfel, G.A.; Wu, Y.; Lee, C. A pilot in silico modeling-based study of the pathological effects on the biomechanical function of tricuspid valves. Int. J. Numer. Methods Biomed. Eng. 2020, 36, e3346. [Google Scholar] [CrossRef] [PubMed]
  180. Taramasso, M.; Pozzoli, A.; Basso, C.; Thiene, G.; Denti, P.; Kuwata, S.; Nietlispach, F.; Alfieri, O.; Hahn, R.T.; Nickenig, G.; et al. Compare and contrast tricuspid and mitral valve anatomy: Interventional perspectives for transcatheter tricuspid valve therapies. EuroIntervention 2018, 13, 1889–1898. [Google Scholar] [CrossRef]
  181. Jett, S.; Laurence, D.; Kunkel, R.; Babu, A.R.; Kramer, K.; Baumwart, R.; Towner, R.; Wu, Y.; Lee, C.-H. An investigation of the anisotropic mechanical properties and anatomical structure of porcine atrioventricular heart valves. J. Mech. Behav. Biomed. Mater. 2018, 87, 155–171. [Google Scholar] [CrossRef]
  182. Morganti, S.; Auricchio, F.; Benson, D.; Gambarin, F.I.; Hartmann, S.; Hughes, T.J.R.; Reali, A. Patient-specific isogeometric structural analysis of aortic valve closure. Comput. Methods Appl. Mech. Eng. 2015, 284, 508–520. [Google Scholar] [CrossRef]
  183. Marom, G. Numerical Methods for Fluid–Structure Interaction Models of Aortic Valves. Arch. Comput. Methods Eng. 2014, 22, 595–620. [Google Scholar] [CrossRef]
  184. Zakerzadeh, R.; Hsu, M.-C.; Sacks, M.S. Computational methods for the aortic heart valve and its replacements. Expert Rev. Med. Devices 2017, 14, 849–866. [Google Scholar] [CrossRef]
  185. Jhun, C.-S.; Newswanger, R.; Cysyk, J.P.; Ponnaluri, S.; Good, B.; Manning, K.B.; Rosenberg, G. Dynamics of Blood Flows in Aortic Stenosis: Mild, Moderate, and Severe. ASAIO J. 2020, 67, 666–674. [Google Scholar] [CrossRef]
  186. Xu, L.; Yin, L.; Liu, Y.; Liang, F. A computational study on the influence of aortic valve disease on hemodynamics in dilated aorta. Math. Biosci. Eng. 2020, 17, 606–626. [Google Scholar] [CrossRef]
  187. Xu, F.; Johnson, E.L.; Wang, C.; Jafari, A.; Yang, C.-H.; Sacks, M.S.; Krishnamurthy, A.; Hsu, M.-C. Computational investigation of left ventricular hemodynamics following bioprosthetic aortic and mitral valve replacement. Mech. Res. Commun. 2020, 112, 103604. [Google Scholar] [CrossRef] [PubMed]
  188. Su, B.; Zhong, L.; Wang, X.-K.; Zhang, J.-M.; Tan, R.S.; Allen, J.C.; Tan, S.K.; Kim, S.; Leo, H.L. Numerical simulation of patient-specific left ventricular model with both mitral and aortic valves by FSI approach. Comput. Methods Programs Biomed. 2014, 113, 474–482. [Google Scholar] [CrossRef] [PubMed]
  189. Zhong, L.; Su, B.; Zhang, J.-M.; Leo, H.L.; Tan, R.S. FSI simulation of intra-ventricular flow in patient-specific ventricular model with both mitral and aortic valves. In Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; Volume 2013, pp. 703–706. [Google Scholar] [CrossRef]
  190. Maragiannis, D.; Jackson, M.S.; Igo, S.R.; Schutt, R.C.; Connell, P.; Grande-Allen, J.; Barker, C.M.; Chang, S.M.; Reardon, M.J.; Zoghbi, W.A.; et al. Replicating Patient-Specific Severe Aortic Valve Stenosis With Functional 3D Modeling. Circ. Cardiovasc. Imaging 2015, 8, e003626. [Google Scholar] [CrossRef][Green Version]
  191. de Oliveira, D.M.C.; Abdullah, N.; Green, N.C.; Espino, D.M. Biomechanical Assessment of Bicuspid Aortic Valve Phenotypes: A Fluid–Structure Interaction Modelling Approach. Cardiovasc. Eng. Technol. 2020, 11, 431–447. [Google Scholar] [CrossRef] [PubMed]
  192. Yan, W.; Li, J.; Wang, W.; Wei, L.; Wang, S. A Fluid–Structure Interaction Study of Different Bicuspid Aortic Valve Phenotypes Throughout the Cardiac Cycle. Front. Physiol. 2021, 12, 6015. [Google Scholar] [CrossRef] [PubMed]
  193. Youssefi, P.; Gomez, A.; He, T.; Anderson, L.; Bunce, N.; Sharma, R.; Figueroa, C.A.; Jahangiri, M. Patient-specific computational fluid dynamics—Assessment of aortic hemodynamics in a spectrum of aortic valve pathologies. J. Thorac. Cardiovasc. Surg. 2016, 153, 8–20.e3. [Google Scholar] [CrossRef][Green Version]
  194. Shen, X.; Bai, L.; Cai, L.; Cao, X. A geometric model for the human pulmonary valve in its fully open case. PLoS ONE 2018, 13, e0199390. [Google Scholar] [CrossRef]
  195. Loke, Y.-H.; Capuano, F.; Balaras, E.; Olivieri, L.J. Computational Modeling of Right Ventricular Motion and Intracardiac Flow in Repaired Tetralogy of Fallot. Cardiovasc. Eng. Technol. 2021, 13, 41–54. [Google Scholar] [CrossRef]
  196. Caiazzo, A.; Guibert, R.; Boudjemline, Y.; Vignon-Clementel, I.E. Blood Flow Simulations for the Design of Stented Valve Reducer in Enlarged Ventricular Outflow Tracts. Cardiovasc. Eng. Technol. 2015, 6, 485–500. [Google Scholar] [CrossRef][Green Version]
  197. Caiazzo, A.; Guibert, R.; Vignon-Clementel, I.E. A reduced-order modeling for efficient design study of artificial valve in enlarged ventricular outflow tracts. Comput. Methods Biomech. Biomed. Eng. 2016, 19, 1314–1318. [Google Scholar] [CrossRef][Green Version]
  198. Berdajs, D.; Mosbahi, S.; Vos, J.; Charbonnier, D.; Hullin, R.; Von Segesser, L.K. Fluid dynamics simulation of right ventricular outflow tract oversizing. Interact. Cardiovasc. Thorac. Surg. 2015, 21, 176–182. [Google Scholar] [CrossRef] [PubMed][Green Version]
  199. Sonntag, S.J.; Kütting, M.; Ghalati, P.F.; Kaufmann, T.; Vazquez-Jimenez, J.; Steinseifer, U.; Vodiskar, J. Effect of Pulmonary Conduit Oversizing on Hemodynamics in Children. Int. J. Artif. Organs 2015, 38, 548–556. [Google Scholar] [CrossRef] [PubMed]
  200. Savarese, G.; Lund, L.H. Global Public Health Burden of Heart Failure. Card. Fail. Rev. 2017, 3, 7–11. [Google Scholar] [CrossRef]
  201. Oveissi, F.; Naficy, S.; Lee, A.; Winlaw, D.; Dehghani, F. Materials and manufacturing perspectives in engineering heart valves: A review. Mater. Today Bio 2019, 5, 100038. [Google Scholar] [CrossRef] [PubMed]
  202. Arthurs, C.J.; Khlebnikov, R.; Melville, A.; Marčan, M.; Gomez, A.; Dillon-Murphy, D.; Cuomo, F.; Vieira, M.S.; Schollenberger, J.; Lynch, S.R.; et al. CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation. PLOS Comput. Biol. 2021, 17, e1008881. [Google Scholar] [CrossRef] [PubMed]
  203. Erdemir, A.; Guess, T.M.; Halloran, J.P.; Modenese, L.; Reinbolt, J.A.; Thelen, D.G.; Umberger, B. Commentary on the Integration of Model Sharing and Reproducibility Analysis to Scholarly Publishing Workflow in Computational Biomechanics. IEEE Trans. Biomed. Eng. 2016, 63, 2080–2085. [Google Scholar] [CrossRef]
  204. Toma, M.; Guru, S.; Wu, W.; Ali, M.; Ong, C. Addressing Discrepancies between Experimental and Computational Procedures. Biology 2021, 10, 536. [Google Scholar] [CrossRef]
  205. Campbell, K.S.; Yengo, C.M.; Lee, L.-C.; Kotter, J.; Sorrell, V.L.; Guglin, M.; Wenk, J.F. Closing the therapeutic loop. Arch. Biochem. Biophys. 2019, 663, 129–131. [Google Scholar] [CrossRef]
  206. Atangana, A.; Araz, S.I. Rhythmic behaviors of the human heart with piecewise derivative. Math. Biosci. Eng. 2022, 19, 3091–3109. [Google Scholar] [CrossRef]
  207. Tiwary, B.K. Computational medicine: Quantitative modeling of complex diseases. Brief. Bioinform. 2019, 21, 429–440. [Google Scholar] [CrossRef]
  208. Olivier, B.G.; Swat, M.J.; Moné, M.J. Modeling and Simulation Tools: From Systems Biology to Systems Medicine. Syst. Med. 2016, 1386, 441–463. [Google Scholar] [CrossRef]
  209. Winslow, R.L.; Trayanova, N.; Geman, D.; Miller, M.I. Computational Medicine: Translating Models to Clinical Care. Sci. Transl. Med. 2012, 4, 158rv11. [Google Scholar] [CrossRef][Green Version]
  210. Sakellaropoulos, T.; Hur, J.; Melas, I.N.; Guo, E.Y.; Alexopoulos, L.; Bohlooly, M.; Bai, J.P. Computational Approaches to Accelerating Novel Medicine and Better Patient Care from Bedside to Benchtop. Adv. Protein Chem. Struct. Biol. 2016, 102, 147–179. [Google Scholar] [CrossRef] [PubMed]
  211. Miller, D.D.; Brown, E.W. Artificial Intelligence in Medical Practice: The Question to the Answer? Am. J. Med. 2018, 131, 129–133. [Google Scholar] [CrossRef] [PubMed]
  212. Johnson, K.B.; Wei, W.; Weeraratne, D.; Frisse, M.E.; Misulis, K.; Rhee, K.; Zhao, J.; Snowdon, J.L. Precision Medicine, AI, and the Future of Personalized Health Care. Clin. Transl. Sci. 2020, 14, 86–93. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Flowcharts of the FSI solution algorithms with (a) arbitrary Lagrangian–Eulerian (ALE) and (b) smoothed-particle hydrodynamics (SPH) methods.
Figure 1. Flowcharts of the FSI solution algorithms with (a) arbitrary Lagrangian–Eulerian (ALE) and (b) smoothed-particle hydrodynamics (SPH) methods.
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Figure 2. Organization of this review paper. The references are further divided according to which of the four heart valves is their main focus.
Figure 2. Organization of this review paper. The references are further divided according to which of the four heart valves is their main focus.
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Toma, M.; Singh-Gryzbon, S.; Frankini, E.; Wei, Z.; Yoganathan, A.P. Clinical Impact of Computational Heart Valve Models. Materials 2022, 15, 3302. https://doi.org/10.3390/ma15093302

AMA Style

Toma M, Singh-Gryzbon S, Frankini E, Wei Z, Yoganathan AP. Clinical Impact of Computational Heart Valve Models. Materials. 2022; 15(9):3302. https://doi.org/10.3390/ma15093302

Chicago/Turabian Style

Toma, Milan, Shelly Singh-Gryzbon, Elisabeth Frankini, Zhenglun (Alan) Wei, and Ajit P. Yoganathan. 2022. "Clinical Impact of Computational Heart Valve Models" Materials 15, no. 9: 3302. https://doi.org/10.3390/ma15093302

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