Endovascular aneurysm repair (EVAR) can involve some unfavorable complications such as endoleaks or stent-graft (SG) migration. Such complications, resulting from the complex mechanical interaction of vascular tissue, SG and blood flow or incompatibility of SG design and vessel geometry, are difficult to predict. Computational vascular mechanics models can be a predictive tool for the selection, sizing and placement process of SGs depending on the patient-specific vessel geometry and hence reduce the risk of potential complications after EVAR. In this contribution, we present a new in silico EVAR methodology to predict the final state of the deployed SG after intervention and evaluate the mechanical state of vessel and SG, such as contact forces and wall stresses. A novel method to account for residual strains and stresses in SGs, resulting from the precompression of stents during the assembly process of SGs, is presented. We suggest a parameter continuation approach to model various different sizes of SGs within one in silico EVAR simulation which can be a valuable tool when investigating the issue of SG oversizing. The applicability and robustness of the proposed methods are demonstrated on the example of a synthetic abdominal aortic aneurysm geometry.

Non-negligible postinterventional complication rates after endovascular aneurysm repair (EVAR) leave room for further improvements. Since the potential success of EVAR depends on various patientspecific factors, such as the complexity of the vessel geometry and the physiological state of the vessel, in silico models can be a valuable tool in the preinterventional planning phase. A suitable in silico EVAR methodology applied to patient-specific cases can be used to predict stent-graft (SG) related complications, such as SG migration, endoleaks or tissue remodeling-induced aortic neck dilatation, and to improve the selection and sizing process of SGs. In this contribution, we apply an in silico EVAR methodology that predicts the final state of the deployed SG after intervention to three clinical cases. A novel qualitative and quantitative validation methodology, that is based on a comparison between in silico results and postinterventional CT data, is presented. The validation methodology compares average stent diameters pseudo-continuously along the total length of the deployed SG. The validation of the in silico results shows very good agreement proving the potential of using in silico approaches in the preinterven

This work is devoted to the development of a mathematical model of the early stages of atherosclerosis incorporating processes of all time scales of the disease and to show their interactions. The cardiovascular mechanics is modeled by a fluid-structure interaction approach coupling a non-Newtonian fluid to a hyperelastic solid undergoing anisotropic growth and a change of its constitutive equation. Additionally, the transport of low-density lipoproteins and its penetration through the endothelium is considered by a coupled set of advection-diffusion-reaction equations. Thereby, the permeability of the endothelium is wall-shear stress modulated resulting in a locally varying accumulation of foam cells triggering a novel growth and remodeling formulation. The model is calibrated and applied to an murine-specific case study, and a qualitative validation of the computational results is performed. The model is utilized to further investigate the influence of the pulsatile blood flow and the compliance of the artery wall to the atherosclerotic process. The computational results imply that the pulsatile blood flow is crucial, whereas the compliance of the aorta has only a minor influence on atherosclerosis. Further, it is shown that the novel model is capable to produce a narrowing of the vessel lumen inducing an adaption of the endothelial permeability pattern.

The variety of stent‐graft (SG) design variables (eg, SG type and degree of SG oversizing) and the complexity of decision making whether a patient is suitable for endovascular aneurysm repair (EVAR) raise the need for the development of predictive tools to assist clinicians in the preinterventional planning phase. Recently, some in silico EVAR methods have been developed to predict the deployed SG configuration. However, only few studies investigated how to assess the in silico EVAR outcome with respect to EVAR complication likelihoods (eg, endoleaks and SG migration). Based on a large literature study, in this contribution, 20 mechanical and geometrical parameters (eg, SG drag force and SG fixation force) are defined to evaluate the quality of the in silico EVAR outcome. For a cohort of n = 146 realizations of parameterized vessel and SG geometries, the in silico EVAR results are studied with respect to these mechanical and geometrical parameters. All degrees of SG oversizing in the range between 5% and 40% are investigated continuously by a computationally efficient parameter continuation approach. The in silico investigations have shown that the mechanical and geometrical parameters are able to indicate candidates at high risk of postinterventional complications. Hence, this study provides the basis for the development of a simulation‐based metric to assess the potential success of EVAR based on engineering parameters.

Endovascular aortic repair (EVAR) has become the preferred intervention option for aortic aneurysms and dissections. This is because EVAR is much less invasive than the alternative open surgery repair. While in‐hospital mortality rates are smaller for EVAR than open repair (1%–2% vs. 3%–5%), the early benefits of EVAR are lost after 3 years due to larger rates of complications in the EVAR group. Clinicians follow instructions for use (IFU) when possible, but are left with personal experience on how to best proceed and what choices to make with respect to stent‐graft (SG) model choice, sizing, procedural options, and their implications on long‐term outcomes. Computational modeling of SG deployment in EVAR and tissue remodeling after intervention offers an alternative way of testing SG designs in silico, in a personalized way before intervention, to ultimately select the strategies leading to better outcomes. Further, computational modeling can be used in the optimal design of SGs in cases of complex geometries. In this review, we address some of the difficulties and successes associated with computational modeling of EVAR procedures. There is still work to be done in all areas of EVAR in silico modeling, including model validation, before models can be applied in the clinic, but much progress has already been made. Critical to clinical implementation are current efforts focusing on developing fast algorithms that can achieve (near) real‐time solutions, as well as ways of dealing with inherent uncertainties related to patient aortic wall degradation on an individualized basis. We are optimistic that EVAR modeling in the clinic will soon become a reality to help clinicians optimize EVAR interventions and ultimately reduce EVAR‐associated complications.

ZusammenfassungHintergrund: Die endovaskuläre Behandlung (EVAR) von abdominalen Aortenaneurysmen (AAA) erfordert einen komplexen und bisher stark erfahrungsbasierten präoperativen Planungsprozess z. B. bzgl. Stentgrafttypen Auswahl und Oversizing, welcher an die individuellen patientenspezifischen Gegebenheiten angepasst sein muss. Fragestellung: Ob die Verwendung eines virtuellen digitalen Zwillings potentiell hilfreich sein kann diesen erfahrungsbasierten Planungsprozess zu objektivieren und zu optimieren, um damit das methodenassoziierte Komplikationsrisiko zu senken, soll in der vorliegenden Arbeit untersucht werden. Methoden: Basierend auf präoperativen patientenspezifischen Daten werden wirklichkeitsgetreue AAA sowie wirklichkeitsgetreue Stentgraft Simulationsmodelle gängiger kommerzieller Stentgrafts erzeugt. Anschließend wird eine virtuelle endovaskuläre AAA Reparatur zur Vorhersage der postoperativen Konfiguration von Stentgraft und AAA verwendet. Unterschiedliche Anwendungsbeispiele dieser Prozesskette sollen den potentiellen Nutzen eines digitalen Zwillings in der endovaskulären Therapie aufzeigen. Ergebnisse: Die potentielle Anwendbarkeit und der Nutzen eines digitalen Zwillings zur Optimierung der präoperativen Stentgraft Auswahl und Größenbestimmung sowie zur prädiktiven Einschätzung der Komplikationswahrscheinlichkeit basierend auf mechanischen und geometrischen Kenngrößen konnten exemplarisch demonstriert und validifiziert werden. Schlussfolgerungen: Die gute Vorhersagegüte macht den digitalen Zwilling zu einem vielversprechenden Planungswerkzeug in der präoperativen Planungsphase der endovaskulären AAA Versorgung mit denkbar vielseitigen Anwendungsmöglichkeiten. Schlüsselwörter abdominales Aortenaneurysma, endovaskuläre Aneurysmareparatur, Stentgraft, personalisierte Medizin, digitaler ZwillingThe digital twin in the endovascular repair Abstract Background: Endovascular aortic repair (EVAR) of abdominal aortic aneurysms (AAA) requires a very complex preoperative planning process, e.g. with respect to stent--graft selection and stent--graft oversizing, which must be individually adapted to the patient-specific case and which is strongly based on the interventionalist's experience. Objectives: In this study, it is investigated whether the use of a digital twin could increase objectivity and optimize the experience based preoperative planning process and hence reduce the method--associated complication rate. Methods: Based on preoperative patient--specific data, realistic AAA and realistic stent-graft simulation models of common commercial stent--grafts are generated. Subsequently, virtual endovascular AAA repair is used to predict the postoperative configuration of stent--graft and AAA. Different application examples of this process chain are intended to demonstrate the potential benefits of the use of a digital twin in EVAR. Results: The potential applicability and the benefit of a digital twin for the optimization of the preoperative stent--graft selection and sizing as well as for the predictive...

Endovascular aortic repair (EVAR) is a challenging intervention whose longterm success strongly depends on the appropriate stent-graft (SG) selection and sizing. Most off-the-shelf SGs are straight and cylindrical. Especially in challenging vessel morphologies, the morphology of off-the-shelf SGs is not able to meet the patient-specific demands. Advanced manufacturing technologies facilitate the development of highly customized SGs. Customized SGs that have the same morphology as the luminal vessel surface could considerably improve the quality of the EVAR outcome with reduced likelihoods of EVAR related complications such as endoleaks type I and SG migration. In this contribution, we use an in silico EVAR methodology that approximates the deployed state of the elastically deformable SG in a hyperelastic, anisotropic vessel. The in silico EVAR results of off-the-shelf SGs and customized SGs are compared qualitatively and quantitatively in terms of mechanical and geometrical parameters such as stent stresses, contact tractions, SG fixation forces and the SG-vessel attachment. In a numerical proof of concept, eight different vessel morphologies, such as a conical vessel, a barrel shaped vessel and a curved vessel, are used to demonstrate the added value of customized SGs compared to off-the-shelf SGs. The numerical investigation has shown large benefits of the highly customized SGs compared to off-the-shelf SGs with respect to a better SG-vessel attachment and a considerable increase in SG fixation forces of up to 50% which indicate decreased likelihoods of EVAR related complications. Hence, this numerical proof of concept motivates further research and development of highly customized SGs for the use in challenging vessel morphologies. K E Y W O R D Sabdominal aortic aneurysm, customized stent-graft, endovascular repair, personalized medicine

The cover image is based on the Original Article In silico study of vessel and stent‐graft parameters on the potential success of endovascular aneurysm repair by Michael W. Gee, Andre Hemmler, Brigitta Lutz et al., https://doi.org/10.1002/cnm.3237