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- W2961844105 abstract "Transcatheter aortic valve replacement (TAVR) is a minimally invasive treatment for high-risk patients with aortic diseases. Despite its increasing use, many influential factors are still to be understood and require continuous investigation. The best numerical approach capable of reproducing both the valves mechanics and the hemodynamics is the fluid-structure interaction (FSI) modeling. The aim of this work is the development of a patient-specific FSI methodology able to model the implantation phase as well as the valve working conditions during cardiac cycles.The patient-specific domain, which included the aortic root, native valve and calcifications, was reconstructed from CT images, while the CAD model of the device, metallic frame and pericardium, was drawn from literature data. Ventricular and aortic pressure waveforms, derived from the patient's data, were used as boundary conditions. The proposed method was applied to two real clinical cases, which presented different outcomes in terms of paravalvular leakage (PVL), the main complication after TAVR.The results confirmed the clinical prognosis of mild and moderate PVL with coherent values of regurgitant volume and effective regurgitant orifice area. Moreover, the final release configuration of the device and the velocity field were compared with postoperative CT scans and Doppler traces showing a good qualitative and quantitative matching.In conclusion, the development of realistic and accurate FSI patient-specific models can be used as a support for clinical decisions before the implantation." @default.
- W2961844105 created "2019-07-23" @default.
- W2961844105 creator A5003432010 @default.
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- W2961844105 date "2019-07-15" @default.
- W2961844105 modified "2023-10-16" @default.
- W2961844105 title "On the Modeling of Patient-Specific Transcatheter Aortic Valve Replacement: A Fluid–Structure Interaction Approach" @default.
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- W2961844105 doi "https://doi.org/10.1007/s13239-019-00427-0" @default.
- W2961844105 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31309527" @default.
- W2961844105 hasPublicationYear "2019" @default.
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