Left atrial appendage occlusion device: Development and validation of a finite element model

Zaccaria, Alissa; Danielli, Francesca; Gasparotti, Emanuele; Fanni, Benigno Marco; Celi, Simona; Pennati, Giancarlo; Petrini, Lorenza

doi:10.1016/j.medengphy.2020.05.019

Cited by 12 publications

(12 citation statements)

References 31 publications

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“…However, to integrate in silico fluid simulations in device-related decision-making, enough credibility still needs to be built following verification, validation, and uncertainty quantification standards such as the V&V40 guidelines[ 41 ], including sensitivity analysis to identify the boundary conditions to provide more the realistic fluid simulations[ 39 ]. Moreover, the employed fluid solver allowed user-interaction for estimating changes in blood flow patterns with different device positions, but at the expense of simplifications (e.g., absence of wall motion) that could be relevant to better mimicking of the interaction between the device and the anatomy[ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Domain expert evaluation of advanced visual computing solutions and 3D printing for the planning of the left atrial appendage occluder interventions

Mill¹,

Montoliu²,

Moustafa³

et al. 2022

IJB

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Advanced visual computing solutions and three-dimensional (3D) printing are moving from engineering to clinical pipelines for training, planning, and guidance of complex interventions. 3D imaging and rendering, virtual reality (VR), and in-silico simulations, as well as 3D printing technologies provide complementary information to better understand the structure and function of the organs, thereby improving and personalizing clinical decisions. In this study, we evaluated several advanced visual computing solutions, such as web-based 3D imaging visualization, VR, and computational fluid simulations, together with 3D printing, for the planning of the left atrial appendage occluder (LAAO) device implantations. Six cardiologists tested different technologies in pre-operative data of five patients to identify the usability, limitations, and requirements for the clinical translation of each technology through a qualitative questionnaire. The obtained results demonstrate the potential impact of advanced visual computing solutions and 3D printing to improve the planning of LAAO interventions as well as the need for their integration into a single workflow to be used in a clinical environment.

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Section: Discussionmentioning

confidence: 99%

Domain expert evaluation of advanced visual computing solutions and 3D printing for the planning of the left atrial appendage occluder interventions

Mill¹,

Montoliu²,

Moustafa³

et al. 2022

IJB

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“…33 The centerline of the device was reconstructed using SolidWorks (Dassault Systèmes, Vélizy-Villacoublay, France) and Matlab (MathWorks, Natick, MA, USA), and discretized with monodimensional elements using HyperMesh (Altair Engineering, Troy, MI, USA). Finally, the material properties characterized in the authors' previous work 45 were assigned to the developed FE model. The hooks are part of the frame; hence, they were modeled with the same material and discretization strategy.…”

Section: Fe Model Of the Devicementioning

confidence: 99%

Left atrial appendage occlusion: On the need of a numerical model to simulate the implant procedure

Danielli,

Berti,

Fanni

et al. 2024

Numer Methods Biomed Eng

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Left atrial appendage occlusion (LAAO) is a percutaneous procedure to prevent thromboembolism in patients affected by atrial fibrillation. Despite its demonstrated efficacy, the LAA morphological complexity hinders the procedure, resulting in postprocedural drawbacks (device‐related thrombus and peri‐device leakage). Local anatomical features may cause difficulties in the device's positioning and affect the effectiveness of the device's implant. The current work proposes a detailed FE model of the LAAO useful to investigate implant scenarios and derive clinical indications. A high‐fidelity model of the Watchman FLX device and simplified parametric conduits mimicking the zone of the LAA where the device is deployed were developed. Device‐conduit interactions were evaluated by looking at clinical indicators such as device‐wall gap, possible cause of leakage, and device protrusion. As expected, the positioning of the crimped device before the deployment was found to significantly affect the implant outcomes: clinician's choices can be improved if FE models are used to optimize the pre‐operative planning. Remarkably, also the wall mechanical stiffness plays an important role. However, this parameter value is unknown for a specific LAA, a crucial point that must be correctly defined for developing an accurate FE model. Finally, numerical simulations outlined how the device's configuration on which the clinician relies to assess the implant success (i.e., the deployed configuration with the device still attached to the catheter) may differ from the actual final device's configuration, relevant for achieving a safe intervention.

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“…In the last decade, the interest in patient-specific numerical modeling has kept spreading in the context of the decision-making process of cardiovascular interventions [ 1 , 2 , 3 ]. In this view, it has been demonstrated that in silico models can be used to simulate the preoperative scenario at a patient-specific level, representing a valuable tool to stratify risk [ 4 , 5 ], increase diagnostic power [ 6 , 7 ], and inform the planning of interventions [ 8 ]. Furthermore, the Food and Drug Administration is strongly supporting the usage of computational simulations in the context of clinical in silico trials for the development of novel devices and treatment strategies, according to the V&V40 statement [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, on the other hand, the mechanical behavior of in vivo vessels is the main source of uncertainty. The response of the vascular wall to blood flow [ 19 , 20 , 21 ] or device interaction [ 8 , 22 ] is not only contingent on the intrinsic material properties of the vessel, but it is also related to the properties of the surrounding structures and tissues [ 23 ]. In this context, several studies have focused on the extraction of in vivo mechanical properties, mainly coupling inverse computational techniques and imaging [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls

Fanni

Antonuccio

Pizzuto

et al. 2023

JCDD

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Introduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (E) on a Fluid–Structure Interaction (FSI) model of a patient-specific aorta. Methods: The image-based χ-method was used to compute the initial E value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the E value was assumed. Results: The influence of the uncertain E parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of E in the ascending aorta while an insignificant effect was observed in the descending tract. Conclusions: This study demonstrated the importance of the image-based methodology for inferring E, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice.

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Left atrial appendage occlusion device: Development and validation of a finite element model

Cited by 12 publications

References 31 publications

Domain expert evaluation of advanced visual computing solutions and 3D printing for the planning of the left atrial appendage occluder interventions

Domain expert evaluation of advanced visual computing solutions and 3D printing for the planning of the left atrial appendage occluder interventions

Left atrial appendage occlusion: On the need of a numerical model to simulate the implant procedure

Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls

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