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Mitral valve function depends on its complex geometry and tissue health, with alterations in shape and tissue response affecting the long-term restorarion of function. Previous computational frameworks for biomechanical assessment are mostly based on patient-specific geometries; however, these are not flexible enough to yield a variety of models and assess mitral closure for individually tuned morphological parameters or material property representations. This study details the finite element approach implemented in our previously developed toolbox to assess mitral valve biomechanics and showcases its flexibility through the generation and biomechanical evaluation of different models. A healthy valve geometry was generated and its computational predictions for biomechanics validated against data in the literature. Moreover, two mitral valve models including geometric alterations associated with disease were generated and analysed. The healthy mitral valve model yielded biomechanical predictions in terms of valve closure dynamics, leaflet stresses and papillary muscle and chordae forces comparable to previous computational and experimental studies. Mitral valve function was compromised in geometries representing disease, expressed by the presence of regurgitating areas, elevated stress on the leaflets and unbalanced subvalvular apparatus forces. This showcases the flexibility of the toolbox concerning the generation of a range of mitral valve models with varying geometric definitions and material properties and the evaluation of their biomechanics.
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http://dx.doi.org/10.1016/j.medengphy.2023.104067 | DOI Listing |
Heart Rhythm
September 2025
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China. Electronic address:
Background: The effectiveness of ethanol infusion of the vein of Marshall (EIVOM) for persistent atrial fibrillation (AF) in patients with mitral valve replacement (MVR) remains to be determined.
Objectives: This study investigated the effectiveness and safety of EIVOM in catheter ablation of persistent AF in patients with MVR.
Methods: This is a retrospective case-control study.
Rev Esp Cardiol (Engl Ed)
September 2025
Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, España; Servicio de Cardiología, Hospital Clínico de Santiago de Compostela, Santiago de Compostela, España.
Introduction And Objectives: This report presents the 2024 activity data from the Interventional Cardiology Association of the Spanish Society of Cardiology (ACI-SEC).
Methods: All interventional cardiology laboratories in Spain were invited to complete an online survey. Data analysis was conducted by an external company and then reviewed and presented by the ACI-SEC board.
Ann Thorac Surg
September 2025
Department of Cardiac Surgery, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI 48109. Electronic address:
Innovations (Phila)
September 2025
Section of Cardiac Surgery, Department of Surgery, University of Chicago, IL, USA.
Objective: Port sites are a common source of perioperative bleeding in robotic cardiac surgery, which can be exacerbated by patient anatomy and anticoagulation. We present results from the liberal usage of a balloon-tipped coudé catheter for tamponade of robotic port sites during robotic mitral surgery.
Methods: All patients who underwent robotic mitral valve surgery at our institution from August 2016 to July 2022 were studied ( = 320).
Sci Rep
September 2025
Department of Transfusion, The Third Xiangya Hospital of Central South University, 138 Tongzipo Road, Changsha, 410013, Hunan, China.
This study aimed to identify the optimal prediction method and key preoperative variables for red blood cell (RBC) transfusion risk in patients undergoing mitral valve surgery. We conducted a retrospective study involving 1477 patients from eight large tertiary hospitals in China who underwent mitral valve surgery with cardiopulmonary bypass. From thirty collected preoperative variables, the Max-Relevance and Min-Redundancy (mRMR) method was used for feature selection, and various machine learning models were evaluated.
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