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Aims: Our aim was to validate patient-specific software integrating baseline anatomy and biomechanical properties of both the aortic root and valve for the prediction of valve morphology and aortic leaflet calcium displacement after TAVI.Methods and results: Finite element computer modelling was performed in 39 patients treated with a Medtronic CoreValve System (MCS; n=33) or an Edwards SAPIEN XT (ESV; n=6). Quantitative axial frame morphology at inflow (MCS, ESV) and nadir, coaptation and commissures (MCS) was compared between multislice computed tomography (MSCT) post TAVI and a computer model as well as displacement of the aortic leaflet calcifications, quantified by the distance between the coronary ostium and the closest calcium nodule. Bland-Altman analysis revealed a strong correlation between the observed (MSCT) and predicted frame dimensions, although small differences were detected for, e.g., Dmin at the inflow (mean±SD MSCT vs. model: 21.6±2.4 mm vs. 22.0±2.4 mm; difference±SD: -0.4±1.3 mm, p<0.05) and Dmax (25.6±2.7 mm vs. 26.2±2.7 mm; difference±SD: -0.6±1.0 mm, p<0.01). The observed and predicted calcium displacements were highly correlated for the left and right coronary ostia (R 2 =0.67 and R 2 =0.71, respectively p<0.001).Conclusions: Dedicated software allows accurate prediction of frame morphology and calcium displacement after valve implantation, which may help to improve outcome. KEYWORDS• aortic stenosis • computer modelling • transcatheter aortic valve implantation (TAVI)

Leakage of blood alongside the implant is a relatively frequent and life-limiting complication after transcatheter aortic valve implantation. The aim of this study is to develop and validate a workflow to simulate the implantation prior to the intervention. Based on the simulation outcome, the amount of leakage is estimated in order to evaluate the risk of a severe complication. A finite element model of the stent implantation in 10 patients was created based on a pre-operative computed tomography scan. All 10 patients also received a follow-up computed tomography scan, after the implantation. This scan was used to extract the deformed geometry of the stent and the position of the calcifications for validation of the simulation results. The maximal average perimeter difference between the simulated stent and the post-operative stent is 2.9±2.1mm, and occurs at the bottom of the device. The sensitivity of the simulation to the soft tissue material parameters and aortic root wall thickness was tested. The maximal diameter deviation of 6% occurred when the thickness of the aortic root was doubled. The result of the leakage analysis based on the distance between the simulated stent and the surrounding aortic root corresponded well when no regurgitation was observed. The developed tools have the potential to reduce the occurrence and severity of leakage by providing the clinician with additional information prior to the intervention. The simulated geometry and estimated leakage can help decide on the best implant type, size and position before treatment.

The new transcatheter technique to implant synthetic aortic valves offers a treatment to patients previously considered untreatable. However, the majority of patients suffer from leakage alongside the implant. Using a statistical shape model of the anatomy, a correlation was discovered between leakage and the shape of the sinuses of Valsalva.

During a transcatheter aortic valve implantation, an axisymmetric implant is placed in an irregularly shaped aortic root. Implanting an incorrect size can cause complications such as leakage of blood alongside or through the implant. The aim of this study was to construct a method that determines the optimal size of the implant based on the 3-dimensional shape of the aortic root. Based on the pre-interventional computed tomography scan of 89 patients, a statistical shape model (SSM) of their aortic root was constructed. The weights associated with the principal components of the SSM served as a parametric description of each aortic root. These weights and the volume of calcification in the aortic valve were used as parameters in a generalized linear model and a random forest classifier. Both classification algorithms were trained using the patients with no or mild leakage after their intervention. Subsequently, the algorithms were applied to the patients with moderate to severe leakage. The random forest classifier was accurate in 96% of the training cases. 55% of the patients with moderate to severe leakage were assigned a different size implant, 11 out of those 20 got one size smaller. The proposed method was capable of accurately and semi-automatically determining an implant size, using a CT scan of the aortic root. Further research is required to assess whether the different size implants would improve the outcome of those patients.

Transcatheter aortic valve implantation (TAVI) is a relatively new technique offering a treatment option to patients for whom an open-heart surgery represents a high risk of fatality. Due to the percutaneous delivery method of this treatment, there are challenges associated. In this technique the native aortic valve is not resected, making it difficult to judge the required size of the implant and making the sealing unpredictable. After implantation, 50% of the patients suffer from at least a mild degree of leakage alongside the implant, also known as paravalvular regurgitation [1].

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