Finite element analysis of transcatheter aortic valve implantation: Insights on the modelling of self-expandable devices

Finotello, Alice; Gorla, Riccardo; Brambilla, Nedy; Bedogni, Francesco; Auricchio, Ferdinando; Morganti, Simone

doi:10.1016/j.jmbbm.2021.104772

Cited by 19 publications

(8 citation statements)

References 23 publications

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“…The curves show the typical hysteretic behavior of Nitinol throughout a loading–unloading cycle, with higher force exerted by the device in correspondence with the imposed crimping diameter. Results show similarities to those found in literature for the same commercial product, 12 with discrepancies in terms of maximum force related to the adoption of different modeling and meshing techniques, finite element solvers, and slight differences in the simulated SEV geometry and material. In addition to International Organization for Standardization (ISO) standard requirements, the tool allows to inspect strain values within the valve geometry through color maps of maximum principal strains.…”

Section: Resultssupporting

confidence: 75%

“…We applied SEV Radial Force Test to a 3D model of a SEV resembling the Corevalve 29 mm (Medtronic, Minneapolis, MN, USA). 5 Nitinol material parameters were adopted from literature 12 and a minimum crimping diameter of 10 mm was considered. Radial Resistive Force and Chronic Outward Force in function of the valve diameter were predicted (Fig.…”

Section: Resultsmentioning

confidence: 99%

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Accelerating Digitalization in Healthcare with the InSilicoTrials Cloud-Based Platform: Four Use Cases

Nicolò¹,

Sips²,

Vaghi³

et al. 2022

Ann Biomed Eng

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The use of in silico trials is expected to play an increasingly important role in the development and regulatory evaluation of new medical products. Among the advantages that in silico approaches offer, is that they permit testing of drug candidates and new medical devices using virtual patients or computational emulations of preclinical experiments, allowing to refine, reduce or even replace time-consuming and costly benchtop/in vitro/ex vivo experiments as well as the involvement of animals and humans in in vivo studies. To facilitate and widen the adoption of in silico trials, InSilicoTrials Technologies has developed a cloud-based platform, hosting healthcare simulation tools for different bench, preclinical and clinical evaluations, and for diverse disease areas. This paper discusses four use cases of in silico trials performed using the InSilicoTrials.com platform. The first application illustrates how in silico approaches can improve the early preclinical assessment of drug-induced cardiotoxicity risks. The second use case is a virtual reproduction of a bench test for the safety assessment of transcatheter heart valve substitutes. The third and fourth use cases are examples of virtual patients generation to evaluate treatment effects in multiple sclerosis and prostate cancer patients, respectively.

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Section: Resultssupporting

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Accelerating Digitalization in Healthcare with the InSilicoTrials Cloud-Based Platform: Four Use Cases

Nicolò¹,

Sips²,

Vaghi³

et al. 2022

Ann Biomed Eng

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“…It is worth noting that Portico and Evolut-R THVs were grouped together within the high-frame SE group: despite a similar stent height, the Evolut-R stent frame is more rigid due the smaller cell design and has higher radial force as compared to the Portico 19 . Also, the EnVeo-R (Medtronic, Minneapolis, MN, USA) delivery system used for Evolut-R/Pro THV is made of a double spine technology, allowing steering only in two directions, while the FlexNav (Abbott, Minneapolis, MN, USA) delivery system used for Portico THV is made of a single spine technology and it is provided with a stabilization layer, which improves stability during valve release.…”

Section: Discussionmentioning

confidence: 99%

Angulation and curvature of aortic landing zone affect implantation depth in transcatheter aortic valve implantation

Gorla,

Oliva,

Arzuffi

et al. 2024

Sci Rep

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In transcatheter aortic valve implantation (TAVI), final device position may be affected by device interaction with the whole aortic landing zone (LZ) extending to ascending aorta. We investigated the impact of aortic LZ curvature and angulation on TAVI implantation depth, comparing short-frame balloon-expanding (BE) and long-frame self-expanding (SE) devices. Patients (n = 202) treated with BE or SE devices were matched based on one-to-one propensity score. Primary endpoint was the mismatch between the intended (HPre) and the final (HPost) implantation depth. LZ curvature and angulation were calculated based on the aortic centerline trajectory available from pre-TAVI computed tomography. Total LZ curvature ($${k}_{LZ,tot}$$ k L Z , t o t ) and LZ angulation distal to aortic annulus ($${\alpha }_{LZ,Distal}$$ α L Z , D i s t a l ) were greater in the SE compared to the BE group (P < 0.001 for both). In the BE group, HPost was significantly higher than HPre at both cusps (P < 0.001). In the SE group, HPost was significantly deeper than HPre only at the left coronary cusp (P = 0.013). At multivariate analysis, $${\alpha }_{LZ,Distal}$$ α L Z , D i s t a l was the only independent predictor (OR = 1.11, P = 0.002) of deeper final implantation depth with a cut-off value of 17.8°. Aortic LZ curvature and angulation significantly affected final TAVI implantation depth, especially in high stent-frame SE devices reporting, upon complete release, deeper implantation depth with respect to the intended one.

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“…However, before this is possible, the behavior of the solid structures must be determined and successfully simulated. A variety of applications exist for the computational models mentioned, including within the medical device industry, where the mathematical models can be used to aid in the design of devices that interact with the human body, such as stents and endoscopy devices [25,81]. The use of the models in a finite element implementation can give greater insight into the devices' mechanisms, highlight any potential risks for the patient, and conserve a range of resources such as time and manufacturing materials.…”

Section: Introductionmentioning

confidence: 99%

An in silico-based review on anisotropic hyperelastic constitutive models for soft biological tissues

Dal¹,

Açan²,

Durcan³

et al. 2022

Preprint

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We review nine invariant and dispersion-type anisotropic hyperelastic constitutive models for soft biological tissues based on their fitting performance to experimental data from three different human tissues. For this, we used a hybrid multi-objective optimization procedure. A genetic algorithm is devised to generate the initial guesses followed by a gradient-based search algorithm. The constitutive models are then fit to a set of uniaxial and biaxial tension experiments conducted on tissues with differing fiber orientations. For the in silico investigation, experiments conducted on aneurysmatic abdominal aorta, linea alba, and rectus sheath tissues are utilized. Accordingly, the models are ranked with respect to an objective normalized quality of fit metric. Finally, a detailed discussion is carried out on the fitting performance of each model. This work provides a valuable quantitative comparison of various anisotropic hyperelastic models, the findings of which can aid those modeling the behavior of soft tissues in selecting the best constitutive model for their particular application.

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Finite element analysis of transcatheter aortic valve implantation: Insights on the modelling of self-expandable devices

Cited by 19 publications

References 23 publications

Accelerating Digitalization in Healthcare with the InSilicoTrials Cloud-Based Platform: Four Use Cases

Accelerating Digitalization in Healthcare with the InSilicoTrials Cloud-Based Platform: Four Use Cases

Angulation and curvature of aortic landing zone affect implantation depth in transcatheter aortic valve implantation

An in silico-based review on anisotropic hyperelastic constitutive models for soft biological tissues

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