Non-invasive cardiovascular magnetic resonance assessment of pressure recovery distance after aortic valve stenosis.

BACKGROUND: Decisions in the management of aortic stenosis are based on the peak pressure drop, captured by Doppler echocardiography, whereas gold standard catheterization measurements assess the net pressure drop but are limited by associated risks. The relationship between these two measurements, peak and net pressure drop, is dictated by the pressure recovery along the ascending aorta which is mainly caused by turbulence energy dissipation. Currently, pressure recovery is considered to occur within the first 40-50 mm distally from the aortic valve, albeit there is inconsistency across interventionist centers on where/how to position the catheter to capture the net pressure drop. METHODS: We developed a non-invasive method to assess the pressure recovery distance based on blood flow momentum via 4D Flow cardiovascular magnetic resonance (CMR). Multi-center acquisitions included physical flow phantoms with different stenotic valve configurations to validate this method, first against reference measurements and then against turbulent energy dissipation (respectively n = 8 and n = 28 acquisitions) and to investigate the relationship between peak and net pressure drops. Finally, we explored the potential errors of cardiac catheterisation pressure recordings as a result of neglecting the pressure recovery distance in a clinical bicuspid aortic valve (BAV) cohort of n = 32 patients. RESULTS: In-vitro assessment of pressure recovery distance based on flow momentum achieved an average error of 1.8 ± 8.4 mm when compared to reference pressure sensors in the first phantom workbench. The momentum pressure recovery distance and the turbulent energy dissipation distance showed no statistical difference (mean difference of 2.8 ± 5.4 mm, R2 = 0.93) in the second phantom workbench. A linear correlation was observed between peak and net pressure drops, however, with strong dependences on the valvular morphology. Finally, in the BAV cohort the pressure recovery distance was 78.8 ± 34.3 mm from vena contracta, which is significantly longer than currently accepted in clinical practise (40-50 mm), and 37.5% of patients displayed a pressure recovery distance beyond the end of the ascending aorta. CONCLUSION: The non-invasive assessment of the distance to pressure recovery is possible by tracking momentum via 4D Flow CMR. Recovery is not always complete at the ascending aorta, and catheterised recordings will overestimate the net pressure drop in those situations. There is a need to re-evaluate the methods that characterise the haemodynamic burden caused by aortic stenosis as currently clinically accepted pressure recovery distance is an underestimation.

Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence.

Background: The number of multislice computed tomography (MSCT) analyses performed for planning structural heart interventions is rapidly increasing. Further automation is required to save time, increase standardization, and reduce the learning curve. Objective: The purpose of this study was to investigate the feasibility of a fully automated artificial intelligence (AI)-based MSCT analysis for planning structural heart interventions, focusing on left atrial appendage occlusion (LAAO) as the selected use case. Methods: Different deep learning models were trained, validated, and tested using a cohort of 583 patients for which manually annotated data were available. These models were used independently or in combination to detect the anatomical ostium, the landing zone, the mitral valve annulus, and the fossa ovalis and to segment the left atrium (LA) and left atrial appendage (LAA). The accuracy of the models was evaluated through comparison with the manually annotated data. Results: The automated analysis was performed on 25 randomly selected patients of the test cohort. The results were compared to the manually identified landmarks. The predicted segmentation of the LA(A) was similar to the manual segmentation (dice score of 0.94 ± 0.02). The difference between the automatically predicted and manually measured perimeter-based diameter was -0.8 ± 1.3 mm (anatomical ostium), -1.0 ± 1.5 mm (Amulet landing zone), and -0.1 ± 1.3 mm (Watchman FLX landing zone), which is similar to the operator variability on these measurements. Finally, the detected mitral valve annulus and fossa ovalis were close to the manual detection of these landmarks, as shown by the Hausdorff distance (3.9 ± 1.2 mm and 4.8 ± 1.8 mm, respectively). The average runtime of the complete workflow, including data pre- and postprocessing, was 57.5 ± 34.5 seconds. Conclusions: A fast and accurate AI-based workflow is proposed to automatically analyze MSCT images for planning LAAO. The approach, which can be easily extended toward other structural heart interventions, may help to handle the rapidly increasing volumes of patients.

The 'Digital Twin' to enable the vision of precision cardiology.

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

Automatic Detection of the Aortic Annular Plane and Coronary Ostia from Multidetector Computed Tomography.

Anatomic landmark detection is crucial during preoperative planning of transcatheter aortic valve implantation (TAVI) to select the proper device size and assess the risk of complications. The detection is currently a time-consuming manual process influenced by the image quality and subject to operator variability. In this work, we propose a novel automatic method to detect the relevant aortic landmarks from MDCT images using deep learning techniques. We trained three convolutional neural networks (CNNs) with 344 multidetector computed tomography (MDCT) acquisitions to detect five anatomical landmarks relevant for TAVI planning: the three basal attachment points of the aortic valve leaflets and the left and right coronary ostia. The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. These segmentation volumes were averaged into one final segmentation volume, and the final predicted landmarks were obtained during a postprocessing step. Finally, we constructed the aortic annular plane, defined by the three predicted hinge points, and measured the distances from this plane to the predicted coronary ostia (i.e., coronary height). The methodology was validated on 100 patients. The automatic landmark detection was able to detect all the landmarks and showed high accuracy as the median distance between the ground truth and predictions is lower than the interobserver variations (1.5 mm [1.1-2.1], 2.0 mm [1.3-2.8] with a paired difference -0.5 ± 1.3 mm and p value <0.001). Furthermore, a high correlation is observed between predicted and manually measured coronary heights (for both R 2 = 0.8). The image analysis time per patient was below one second. The proposed method is accurate, fast, and reproducible. Embedding this tool based on deep learning in the preoperative planning routine may have an impact in the TAVI environments by reducing the time and cost and improving accuracy.

Enabling Automated Device Size Selection for Transcatheter Aortic Valve Implantation.

The number of transcatheter aortic valve implantation (TAVI) procedures is expected to increase significantly in the coming years. Improving efficiency will become essential for experienced operators performing large TAVI volumes, while new operators will require training and may benefit from accurate support. In this work, we present a fast deep learning method that can predict aortic annulus perimeter and area automatically from aortic annular plane images. We propose a method combining two deep convolutional neural networks followed by a postprocessing step. The models were trained with 355 patients using modern deep learning techniques, and the method was evaluated on another 118 patients. The method was validated against an interoperator variability study of the same 118 patients. The differences between the manually obtained aortic annulus measurements and the automatic predictions were similar to the differences between two independent observers (paired diff. of 3.3 ± 16.8 mm2 vs. 1.3 ± 21.1 mm2 for the area and a paired diff. of 0.6 ± 1.7 mm vs. 0.2 ± 2.5 mm for the perimeter). The area and perimeter were used to retrieve the suggested prosthesis sizes for the Edwards Sapien 3 and the Medtronic Evolut device retrospectively. The automatically obtained device size selections accorded well with the device sizes selected by operator 1. The total analysis time from aortic annular plane to prosthesis size was below one second. This study showed that automated TAVI device size selection using the proposed method is fast, accurate, and reproducible. Comparison with the interobserver variability has shown the reliability of the strategy, and embedding this tool based on deep learning in the preoperative planning routine has the potential to increase the efficiency while ensuring accuracy.

A computational study of the hemodynamic impact of open- versus closed-cell stent design in carotid artery stenting.

The aim of this study is to analyze the shape and flow changes of a patient-specific carotid artery after carotid artery stenting (CAS) performed using an open-cell (stent-O) or a closed-cell (stent-C) stent design. First, a stent reconstructed from micro-computed tomography (microCT) is virtually implanted in a left carotid artery reconstructed from CT angiography. Second, an objective analysis of the stent-to-vessel apposition is used to quantify the lumen cross-sectional area and the incomplete stent apposition (ISA). Third, the carotid artery lumen is virtually perfused in order to quantify its resistance to flow and its exposure to atherogenic or thrombogenic hemodynamic conditions. After CAS, the minimum cross-sectional area of the internal carotid artery (ICA) (external carotid artery [ECA]) changes by +54% (-12%) with stent-O and +78% (-17%) with stent-C; the resistance to flow of the ICA (ECA) changes by -21% (+13%) with stent-O and -26% (+18%) with stent-C. Both stent designs suffer from ISA but the malapposed stent area is larger with stent-O than stent-C (29.5 vs. 14.8 mm(2) ). The untreated vessel is not exposed to atherogenic flow conditions whereas an area of 67.6 mm(2) (104.9) occurs with stent-O (stent-C). The area of the stent surface exposed to thrombogenic risk is 5.42 mm(2) (7.7) with stent-O (stent-C). The computer simulations of stenting in a patient's carotid artery reveal a trade-off between cross-sectional size and flow resistance of the ICA (enlarged and circularized) and the ECA (narrowed and ovalized). Such a trade-off, together with malapposition, atherogenic risk, and thrombogenic risk is stent-design dependent.

Artificial Intelligence, Computational Simulations, and Extended Reality in Cardiovascular Interventions.

Artificial intelligence, computational simulations, and extended reality, among other 21st century computational technologies, are changing the health care system. To collectively highlight the most recent advances and benefits of artificial intelligence, computational simulations, and extended reality in cardiovascular therapies, we coined the abbreviation AISER. The review particularly focuses on the following applications of AISER: 1) preprocedural planning and clinical decision making; 2) virtual clinical trials, and cardiovascular device research, development, and regulatory approval; and 3) education and training of interventional health care professionals and medical technology innovators. We also discuss the obstacles and constraints associated with the application of AISER technologies, as well as the proposed solutions. Interventional health care professionals, computer scientists, biomedical engineers, experts in bioinformatics and visualization, the device industry, ethics committees, and regulatory agencies are expected to streamline the use of AISER technologies in cardiovascular interventions and medicine in general.

Artificial Intelligence and Transcatheter Interventions for Structural Heart Disease: A glance at the (near) future.

With innovations in therapeutic technologies and changes in population demographics, transcatheter interventions for structural heart disease have become the preferred treatment and will keep growing. Yet, a thorough clinical selection and efficient pathway from diagnosis to treatment and follow-up are mandatory. In this review we reflect on how artificial intelligence may help to improve patient selection, pre-procedural planning, procedure execution and follow-up so to establish efficient and high quality health care in an increasing number of patients.

Sealing Behavior in Transcatheter Bicuspid and Tricuspid Aortic Valves Replacement Through Patient-Specific Computational Modeling.

Background: Patient-specific computer simulation of transcatheter aortic valve replacement (TAVR) can provide unique insights in device-patient interaction. Aims: This study was to compare transcatheter aortic valve sealing behavior in patients with bicuspid aortic valves (BAV) and tricuspid aortic valves (TAV) through patient-specific computational modeling. Methods: Patient-specific computer simulation was retrospectively performed with FEops HEARTguide for TAVR patients. Simulation output was compared with postprocedural computed tomography and echocardiography to validate the accuracy. Skirt malapposition was defined by a distance larger than 1 mm based on the predicted device-patient interaction by quantifying the distance between the transcatheter heart valve (THV) skirt and the surrounding anatomical regions. Results: In total, 43 patients were included in the study. Predicted and observed THV frame deformation showed good correlation (R 2 ≥ 0.90) for all analyzed measurements (maximum diameter, minimum diameter, area, and perimeter). The amount of predicted THV skirt malapposition was strongly linked with the echocardiographic grading of paravalvular leakage (PVL). More THV skirt malapposition was observed for BAV cases when compared to TAV cases (22.7 vs. 15.5%, p < 0.05). A detailed analysis of skirt malapposition showed a higher degree of malapposition in the interleaflet triangles section for BAV cases as compared to TAV patients (11.1 vs. 5.8%, p < 0.05). Conclusions: Patient-specific computer simulation of TAVR can accurately predict the behavior of the Venus A-valve. BAV patients are associated with more malapposition of the THV skirt as compared to TAV patients, and this is mainly driven by more malapposition in the interleaflet triangle region.

Towards patient-specific prediction of conduction abnormalities induced by transcatheter aortic valve implantation: a combined mechanistic modelling and machine learning approach.

Aims: Post-procedure conduction abnormalities (CA) remain a common complication of transcatheter aortic valve implantation (TAVI), highlighting the need for personalized prediction models. We used machine learning (ML), integrating statistical and mechanistic modelling to provide a patient-specific estimation of the probability of developing CA after TAVI. Methods and results: The cohort consisted of 151 patients with normal conduction and no pacemaker at baseline who underwent TAVI in nine European centres. Devices included CoreValve, Evolut R, Evolut PRO, and Lotus. Preoperative multi-slice computed tomography was performed. Virtual valve implantation with patient-specific computer modelling and simulation (CM&S) allowed calculation of valve-induced contact pressure on the anatomy. The primary composite outcome was new onset left or right bundle branch block or permanent pacemaker implantation (PPI) before discharge. A supervised ML approach was applied with eight models predicting CA based on anatomical, procedural and mechanistic data. CA occurred in 59% of patients (n = 89), more often after mechanical than first or second generation self-expanding valves (68% vs. 60% vs. 41%). CM&S revealed significantly higher contact pressure and contact pressure index in patients with CA. The best model achieved 83% accuracy (area under the curve 0.84) and sensitivity, specificity, positive predictive value, negative predictive value, and F1-score of 100%, 62%, 76%, 100%, and 82%. Conclusion: ML, integrating statistical and mechanistic modelling, achieved an accurate prediction of CA after TAVI. This study demonstrates the potential of a synergetic approach for personalizing procedure planning, allowing selection of the optimal device and implantation strategy, avoiding new CA and/or PPI.

Differences in clinical valve size selection and valve size selection for patient-specific computer simulation in transcatheter aortic valve replacement (TAVR): a retrospective multicenter analysis.

Valve size selection for transcatheter aortic valve replacement (TAVR) is currently based on cardiac CT-scan. At variance with patient-specific computer simulation, this does not allow the assessment of the valve-host interaction. We aimed to compare clinical valve size selection and valve size selection by an independent expert for computer simulation. A multicenter retrospective analysis of valve size selection by the physician and the independent expert in 141 patients who underwent TAVR with the self-expanding CoreValve or Evolut R. Baseline CT-scan was used for clinical valve size selection and for patient-specific computer simulation. Simulation results were not available for clinical use. Overall true concordance between clinical and simulated valve size selection was observed in 47 patients (33%), true discordance in 15 (11%) and ambiguity in 79 (56%). In 62 (44%, cohort A) one valve size was simulated whereas two valve sizes were simulated in 79 (56%, cohort B). In cohort A, concordance was 76% and discordance was 24%; a smaller valve size was selected for simulation in 10 patients and a larger in 5. In cohort B, a different valve size was selected for simulation in all patients in addition to the valve size that was used for TAVR. The different valve size concerned a smaller valve in 45 patients (57%) and a larger in 34 (43%). Selection of the valve size differs between the physician and the independent computer simulation expert who used the same source of information. These findings indicate that valve sizing in TAVR is still more intricate than generally assumed.

Optimization of a Transcatheter Heart Valve Frame Using Patient-Specific Computer Simulation.

PURPOSE: This study proposes a new framework to optimize the design of a transcatheter aortic valve through patient-specific finite element and fluid dynamics simulation. METHODS: Two geometrical parameters of the frame, the diameter at ventricular inflow and the height of the first row of cells, were examined using the central composite design. The effect of those parameters on postoperative complications was investigated by response surface methodology, and a Nonlinear Programming by Quadratic Lagrangian algorithm was used in the optimization. Optimal and initial devices were then compared in 12 patients. The comparison was made in terms of device performance [i.e., reduced contact pressure on the atrioventricular conduction system and paravalvular aortic regurgitation (AR)]. RESULTS: Results suggest that large diameters and high cells favor higher anchoring of the device within the aortic root reducing the contact pressure and favor a better apposition of the device to the aortic root preventing AR. Compared to the initial device, the optimal device resulted in almost threefold lower predicted contact pressure and limited AR in all patients. CONCLUSIONS: In conclusion, patient-specific modelling and simulation could help to evaluate device performance prior to the actual first-in-human clinical study and, combined with device optimization, could help to develop better devices in a shorter period.

The Impact of Size and Position of a Mechanical Expandable Transcatheter Aortic Valve: Novel Insights Through Computational Modelling and Simulation.

Transcatheter aortic valve implantation has become an established procedure to treat severe aortic stenosis. Correct device sizing/positioning is crucial for optimal outcome. Lotus valve sizing is based upon multiple aortic root dimensions. Hence, it often occurs that two valve sizes can be selected. In this study, patient-specific computer simulation is adopted to evaluate the influence of Lotus size/position on paravalvular aortic regurgitation (AR) and conduction abnormalities, in patients with equivocal aortic root dimensions. First, simulation was performed in 62 patients to validate the model in terms of predicted AR and conduction abnormalities using postoperative echocardiographic, angiographic and ECG-based data. Then, two Lotus sizes were simulated at two positions in patients with equivocal aortic root dimensions. Large valve size and deep position were associated with higher contact pressure, while only large size, not position, significantly reduced the predicted AR. Despite general trends, simulations revealed that optimal device size/position is patient-specific.

Patient-Specific Computer Simulation of Transcatheter Aortic Valve Replacement in Bicuspid Aortic Valve Morphology.

BACKGROUND: A patient-specific computer simulation of transcatheter aortic valve replacement (TAVR) in tricuspid aortic valve has been developed, which can predict paravalvular regurgitation and conduction disturbance. We wished to validate a patient-specific computer simulation of TAVR in bicuspid aortic valve and to determine whether patient-specific transcatheter heart valve (THV) sizing and positioning might improve clinical outcomes. METHODS: A retrospective study was performed on TAVR in bicuspid aortic valve patients that had both pre- and postprocedural computed tomography imaging. Preprocedural computed tomography imaging was used to create finite element models of the aortic root. Finite element analysis and computational fluid dynamics was performed. The simulation output was compared with postprocedural computed tomography imaging, cineangiography, echocardiography, and electrocardiograms. For each patient, multiple simulations were performed, to identify an optimal THV size and position for the patient's specific anatomic characteristics. RESULTS: A total of 37 patients were included in the study. The simulations accurately predicted the THV frame deformation (minimum-diameter intraclass correlation coefficient, 0.84; maximum-diameter intraclass correlation coefficient, 0.88; perimeter intraclass correlation coefficient, 0.91; area intraclass correlation coefficient, 0.91), more than mild paravalvular regurgitation (area under the receiver operating characteristic curve, 0.86) and major conduction abnormalities (new left bundle branch block or high-degree atrioventricular block; area under the receiver operating characteristic curve, 0.88). When compared with the implanted THV size and implant depth, optimal patient-specific THV sizing and positioning reduced simulation-predicted paravalvular regurgitation and markers of conduction disturbance. CONCLUSIONS: Patient-specific computer simulation of TAVR in bicuspid aortic valve may predict the development of important clinical outcomes, such as paravalvular regurgitation and conduction abnormalities. Patient-specific THV sizing and positioning may improve clinical outcomes of TAVR in bicuspid aortic valve.

Realistic finite element-based stent design: the impact of balloon folding.

At present, the deployment of an intravascular stent has become a common and widely used minimally invasive treatment for coronary heart disease. To improve these coronary revascularization procedures (e.g. reduce in-stent restenosis rates) the optimal strategy lies in the further development of stent design, material and coatings. In the context of optimizing the stent design, computational models can provide an excellent research tool. In this study, the hypothesis that the free expansion of a stent is determined by the unfolding and expansion of the balloon is examined. Different expansion modeling strategies are studied and compared for a new generation balloon-expandable coronary stent. The trifolded balloon methodology presented in this paper shows very good qualitative and quantitative agreement with both manufacturer's data and experiments. Therefore, the proposed numerical expansion strategy appears to be a very promising optimization methodology in stent design.

Patient-Specific Computer Simulation to Elucidate the Role of Contact Pressure in the Development of New Conduction Abnormalities After Catheter-Based Implantation of a Self-Expanding Aortic Valve.

BACKGROUND: The extent to which pressure generated by the valve on the aortic root plays a role in the genesis of conduction abnormalities after transcatheter aortic valve replacement (TAVR) is unknown. This study elucidates the role of contact pressure and contact pressure area in the development of conduction abnormalities after TAVR using patient-specific computer simulations. METHODS AND RESULTS: Finite-element computer simulations were performed to simulate TAVR of 112 patients who had undergone TAVR with the self-expanding CoreValve/Evolut R valve. On the basis of preoperative multi-slice computed tomography, a patient-specific region of the aortic root containing the atrioventricular conduction system was determined by identifying the membranous septum. Contact pressure and contact pressure index (percentage of area subjected to pressure) were quantified and compared in patients with and without new conduction abnormalities. Sixty-two patients (55%) developed a new left bundle branch block or a high-degree atrioventricular block after TAVR. Maximum contact pressure and contact pressure index (median [interquartile range]) were significantly higher in patients with compared with those without new conduction abnormalities (0.51 MPa [0.43-0.70 MPa] and 33% [22%-44%], respectively, versus 0.29 MPa [0.06-0.50 MPa] and 12% [1%-28%]). By multivariable regression analysis, only maximum contact pressure (odds ratio, 1.35; confidence interval, 1.1-1.7; P=0.01) and contact pressure index (odds ratio, 1.52; confidence interval, 1.1-2.1; P=0.01) were identified as independent predictors for conduction abnormalities, but not implantation depth. CONCLUSIONS: Patient-specific computer simulations revealed that maximum contact pressure and contact pressure index are both associated with new conduction abnormalities after CoreValve/Evolut R implantation and can predict which patient will have conduction abnormalities.

Impact of plaque type and side branch geometry on side branch compromise after provisional stent implantation: a simulation study.

AIMS: Mechanisms of lumen compromise after provisional side branch (SB) stenting are poorly understood. In this study we aimed to investigate the impact of bifurcation angle, plaque composition, and procedural strategy on SB compromise. METHODS AND RESULTS: Computer simulations of stent implantation were performed in Medina (1,1,1) bifurcation models. Provisional SB stenting was replicated including post-dilation after main branch stenting. Two bifurcation angles (45°, 70°) and four plaque types (fully lipid, fully fibrous, lipid with half and fully calcified ring distal to the carina) were tested. Two post-dilation balloons of different lengths (15 mm and 9 mm) were also investigated. Provisional stenting caused an ovalisation of the SB ostium (i.e., increase of ellipticity from 0.27 to 0.58±0.21, p<0.05) that might appear as a significant stenosis on two-dimensional angiography, although SB ostium area was preserved (-3.3±10.3%) in the absence of calcifications. However, in the presence of calcifications, SB lumen volume compromise was evident (-0.89±0.15 mm3). Plaque type had a higher impact than bifurcation angle on SB ostium shape. A shorter balloon (9 mm) for proximal optimisation reduced SB lumen volume compromise from -1.11 mm3 to -0.72 mm3. CONCLUSIONS: Simulations showed ovalisation of the SB ostium, generally without significant lumen compromise. Provisional stenting in the presence of calcifications resulted in a more severe outcome for the SB ostium.

Patient-specific image-based computer simulation for theprediction of valve morphology and calcium displacement after TAVI with the Medtronic CoreValve and the Edwards SAPIEN valve.

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 (R2=0.67 and R2=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.

Technical aspects of the provisional side branch stenting strategy.

Provisional side branch (SB) stenting is the recommended treatment strategy in the vast majority of bifurcation lesions. Over the past 10 years, advances in fundamental knowledge have led to a better understanding and to improvements of this technical approach. This strategy has reached maturity, and long-term clinical results are now comparable to those of non-bifurcation lesions. This paper describes in detail simple rules and tips and tricks which may help physicians in daily practice to use provisional side branch (SB) stenting as the gold standard treatment for the majority of bifurcation lesions.

Patient-specific computer modelling of coronary bifurcation stenting: the John Doe programme.

John Doe, an 81-year-old patient with a significant distal left main (LM) stenosis, was treated using a provisional stenting approach. As part of an European Bifurcation Club (EBC) project, the complete stenting procedure was repeated using computational modelling. First, a tailored three-dimensional (3D) reconstruction of the bifurcation anatomy was created by fusion of multislice computed tomography (CT) imaging and intravascular ultrasound. Second, finite element analysis was employed to deploy and post-dilate the stent virtually within the generated patient-specific anatomical bifurcation model. Finally, blood flow was modelled using computational fluid dynamics. This proof-of-concept study demonstrated the feasibility of such patient-specific simulations for bifurcation stenting and has provided unique insights into the bifurcation anatomy, the technical aspects of LM bifurcation stenting, and the positive impact of adequate post-dilatation on blood flow patterns. Potential clinical applications such as virtual trials and preoperative planning seem feasible but require a thorough clinical validation of the predictive power of these computer simulations.