Tricuspid valve regurgitation decreases after mitraclip implantation: Fluid structure interaction simulation

Severe mitral regurgitation (MR) is a cardiac disease that can lead to fatal consequences. MitraClip (MC) intervention is a percutaneous procedure whereby the mitral valve (MV) leaflets are connected along the edge using MCs. The outcomes of the MC intervention are not known in advance, i.e., the outcomes are quite variable. Artificial intelligence (AI) can be used to guide the cardiologist in selecting optimal MC scenarios. In this study, we describe an atlas of shapes as well as different scenarios for MC implantation for such an AI analysis. We generated the MV geometrical data from three different sources. First, the patients' 3-dimensional echo images were used. The pixel data from six key points were obtained from three views of the echo images. Using PyGem, an open-source morphing library in Python, these coordinates were used to create the geometry by morphing a template geometry. Second, the dimensions of the MV, from the literature were used to create data. Third, we used machine learning methods, principal component analysis, and generative adversarial networks to generate more shapes. We used the finite element (FE) software ABAQUS to simulate smoothed particle hydrodynamics in different scenarios for MC intervention. The MR and stresses in the leaflets were post-processed. Our physics-based FE models simulated the outcomes of MC intervention for different scenarios. The MR and stresses in the leaflets were computed by the FE models for a single clip at different locations as well as two and three clips. Results from FE simulations showed that the location and number of MCs affect subsequent residual MR, and that leaflet stresses do not follow a simple pattern. Furthermore, FE models need several hours to provide the results, and they are not applicable for clinical usage where the predicted outcomes of MC therapy are needed in real-time. In this study, we generated the required dataset for the AI models which can provide the results in a matter of seconds.

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“…The MV annulus motions were adopted from the literature. The SPH methodology is similar to previous publications (24,26).…”

Section: Computational Set Upmentioning

confidence: 88%

“…The MC was simulated by connecting respective points on the MV leaflet edges (Figure 3). The leaflet materials were modeled using hyperplastic fiberreinforced material, as below (24,25):…”

Section: Computational Set Upmentioning

confidence: 99%

Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Dabiri

Jiang

Mahadevan

et al. 2021

Front. Cardiovasc. Med.

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“…To incorporate the backward flow of regurgitation, the personalized hemodynamic model accounts for the individual dynamics of the regurgitant orifice area and the shape of the mitral valve ( Cohen and Gorlin 1972 ). Each personalized model undergoes a virtual edge-to-edge repair with the virtual device, under the guidance of a clinical advisory board to replicate the judgement used in a real clinical trial ( Dabiri et al 2019 ). For each virtual repair process, the ENRICHMENT trial evaluates the relevant clinical readouts including the coaptation height, the residual regurgitation, the spreading force on the clipping device, and the left atrial pressure.…”

Section: Regulatory Perspective—medical Device Innovationmentioning

confidence: 99%

Precision medicine in human heart modeling

Peirlinck

Costabal

Jiang

et al. 2021

Biomech Model Mechanobiol

136

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Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

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“…Subsequently, it was used to assess several diseased mitral valve states [ 43 , 44 , 45 , 46 ] and applications of medical devices designed to correct them [ 47 , 48 , 49 , 50 ]. Besides the mitral valve, other valves have been studied using the same methods [ 42 , 51 , 52 , 53 , 54 , 55 ]. The SPH method has been validated to study the hemodynamics of the left ventricle [ 29 ].…”

Section: Applicationsmentioning

confidence: 99%

Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics

Toma

Chan-Akeley

Arias

et al. 2021

Biology

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Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid–structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. The functions of biological structures usually include their interactions with the surrounding fluids. Hence, we contend that the use of fluid–structure interaction models in computational studies of biological systems is practical, if not necessary. The ultimate goal is to develop computational models to predict human biological processes. These models are meant to guide us through the multitude of possible diseases affecting our organs and lead to more effective methods for disease diagnosis, risk stratification, and therapy. This review paper summarizes computational models that use smoothed-particle hydrodynamics to simulate the fluid–structure interactions in complex biological systems.

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Tricuspid valve regurgitation decreases after mitraclip implantation: Fluid structure interaction simulation

Cited by 19 publications

References 30 publications

Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Precision medicine in human heart modeling

Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics

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