Right ventricular outflow tract (RVOT) calcific obstruction is frequent after homograft conduit implantation to treat congenital heart disease. Stenting and percutaneous pulmonary valve implantation (PPVI) can relieve the obstruction and prolong the conduit lifespan, but require accurate pre-procedural evaluation to minimize the risk of coronary artery (CA) compression, stent fracture, conduit injury or arterial distortion.Herein, we test patient-specific finite element (FE) modeling as a tool to assess stenting feasibility and investigate clinically relevant risks associated to the percutaneous intervention.Three patients undergoing attempted PPVI due to calcific RVOT conduit failure were enrolled; the calcific RVOT, the aortic root and the proximal CA were segmented on CT scans for each patient. We numerically reproduced RVOT balloon angioplasty to test procedure feasibility and the subsequent RVOT prestenting expanding the stent through a balloon-in-balloon delivery system.Our FE framework predicted the occurrence of CA compression in the patient excluded from the real procedure. In the two patients undergoing RVOT stenting, numerical results were consistent with intraprocedural in-vivo fluoroscopic evidences. Furthermore, it quantified the stresses on the stent and on the relevant native structures, highlighting their marked dependence on the extent, shape and location of the calcific deposits. Stent deployment induced displacement and mechanical loading of the calcific deposits, also impacting on the adjacent anatomical structures.This novel workflow has the potential to tackle the analysis of complex RVOT clinical scenarios, pinpointing the procedure impact on the dysfunctional anatomy and elucidating potential periprocedural complications. (C) 2018 Published by Elsevier Ltd.

Prediction of stenting related adverse events through patient-specific finite element modelling

Caimi A.;Sturla F.;Pluchinotta F. R.;Votta E.;Redaelli A.
2018-01-01

Abstract

Right ventricular outflow tract (RVOT) calcific obstruction is frequent after homograft conduit implantation to treat congenital heart disease. Stenting and percutaneous pulmonary valve implantation (PPVI) can relieve the obstruction and prolong the conduit lifespan, but require accurate pre-procedural evaluation to minimize the risk of coronary artery (CA) compression, stent fracture, conduit injury or arterial distortion.Herein, we test patient-specific finite element (FE) modeling as a tool to assess stenting feasibility and investigate clinically relevant risks associated to the percutaneous intervention.Three patients undergoing attempted PPVI due to calcific RVOT conduit failure were enrolled; the calcific RVOT, the aortic root and the proximal CA were segmented on CT scans for each patient. We numerically reproduced RVOT balloon angioplasty to test procedure feasibility and the subsequent RVOT prestenting expanding the stent through a balloon-in-balloon delivery system.Our FE framework predicted the occurrence of CA compression in the patient excluded from the real procedure. In the two patients undergoing RVOT stenting, numerical results were consistent with intraprocedural in-vivo fluoroscopic evidences. Furthermore, it quantified the stresses on the stent and on the relevant native structures, highlighting their marked dependence on the extent, shape and location of the calcific deposits. Stent deployment induced displacement and mechanical loading of the calcific deposits, also impacting on the adjacent anatomical structures.This novel workflow has the potential to tackle the analysis of complex RVOT clinical scenarios, pinpointing the procedure impact on the dysfunctional anatomy and elucidating potential periprocedural complications. (C) 2018 Published by Elsevier Ltd.
2018
Finite Element modeling; Patient-specific simulations; Patient-tailored planning; Right ventricle outflow tract (RVOT) obstruction; RVOT stenting; Adult; Coronary Vessels; Humans; Male; Mechanical Phenomena; Prosthesis Failure; Stents; Treatment Outcome; Young Adult; Finite Element Analysis; Patient-Specific Modeling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1120501
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