Predicting post-TEER hemodynamics in mitral regurgitation using patient-specific modelling

Transcatheter edge-to-edge repair (TEER) is a minimally invasive treatment option for patients with mitral regurgitation (MR) who are at high surgical risk. Currently, there are only limited pre-procedural planning tools available, such that decision-making for optimal device placement often occurs intra-procedurally. This can prolong intervention times and increase the risk of suboptimal outcomes.

The aim of this study is to develop and evaluate a patient-specific computational model for fast, scenario-based prediction of post-TEER hemodynamics to support treatment planning and risk assessment prior to device placement.

A lumped parameter model of the cardiovascular system was implemented that simulates clinically relevant hemodynamics, including MR volume, left ventricular stroke volume, left atrial pressure and mitral valve pressure gradients. We personalized the model parameters to the pre-TEER state of 10 patients using intra-procedural data from transoesophageal echocardiography and invasive pressure recordings. A post-TEER model scenario was created for each patient, where mitral valve model parameters were adjusted to reflect the clinically observed changes in geometry, while arterial model parameters were modified to maintain arterial pressures at pre-TEER levels. Model predictions of post-TEER hemodynamics were compared to intra-procedural measurements following device implantation. A proof-of-concept risk assessment for post-interventional mitral valve stenosis was conducted for one patient by simulating 16 different post-TEER scenarios with varying degrees of remaining mitral valve area and MR reduction, and evaluating their impact on mitral valve pressure gradients.

The developed model reproduced key pre-TEER hemodynamic features across all patients. Post-TEER model predictions reflected typical hemodynamic trends reported after TEER, such as reduced left atrial v-wave pressure and increased mitral valve pressure gradients. However, a direct comparison with individual patient measurements was limited by the physiological variability in the patients’ cardiovascular state at the time of measurement. In the proof-of-concept case, the 16 post-TEER simulations were completed in a matter of minutes, demonstrating the model’s potential for a fast exploration of different treatment scenarios. Mean mitral valve pressure gradients reached values >5mmHg in scenarios with severe reduction in mitral valve area, highlighting the potential use for identifying stenosis risk.

The proposed patient-specific modelling approach enables the rapid simulation of post-TEER hemodynamic trends and may support identification of high-risk configurations prior to device placement. With further development – such as enabling predictions based solely on non-invasive data and integration of virtual device placement models – this approach holds promise as a decision-support tool for TEER planning.