Optimisation of pre- and post-procedural patient assessment for transcatheter heart valve replacement using personalised computer modelling

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Mitral regurgitation (MR) is more common in patients over the age of 65, however they are often deemed too high risk for mitral replacement via open-heart surgery. The current therapy for patients unfit for surgery is transcatheter mitral valve replacement (TMVR). Unlike its aortic valve counterpart, TMVR is a challenging procedure often associated with unstable anchoring; the left ventricular outflow tract obstruction and thrombus formation. In this complex scenario, an effective preprocedural patient assessment is crucial for success, however clinical metrics like ventricular afterload and preload (pressure gradient analysis) and fluid-dynamic forces acting on the implanted device (stability analysis) are invasive to measure. There is therefore an opportunity for this project to develop a novel, time-efficient method for creating personalised computer models able to predict the ventricular haemodynamics of a patient post-TMVR which, once validated in-vivo, have the potential to significantly enhance current patient selection. This project will focus on the creation of an efficient workflow to create the personalised ventricular models. These models will tailored to the anatomical, kinematic and heamodynamic characteristics of the patient. The commercial software package StarCCM+ (Siemens Software PLM) proposed for the haemodynamics simulations will be verified for this type of problems via available benchmark tests. Medtronic will provide complete datasets including multi-phase CT and transesophageal echocardiography (TOE) and Doppler from patients who participated to the Intrepid Global Trial. Simulations will be performed in a pre-TMVR scenario, and on the LV models combined with a CAD model of a Medtronic Intrepid Device in a post-TMVR scenario that will provide a prediction of the haemodynamic response of the LV to implantation. These predictions will be validated against post-TMVR imaging data for each patient, and the uncertainty in the outcomes quantified