Terumo Aortic (United Kingdom)

Aortic dissection is a life-threatening disease with high mortality rates. The degradation of the layers of the aorta wall causes tears, which then propagate further due to high-pressure blood penetrating the vessel wall, creating a false lumen. The intimal flap separating the true and false lumen can either bulge inwards constricting the true lumen's blood flow or bulge outwards leading to catastrophic rupture and internal bleeding. Therefore, to understand the role of critical pressure on tear propagation, a computational study of the initiation and propagation of tears of various sizes and at multiple depths and locations in three-dimensional aortas was conducted. Tears were modelled using the extended finite element method, and the wall of the aortas is an anisotropic hyperelastic material. Blood-pressure-loaded aorta geometries were obtained from the corresponding unloaded geometries using an iterative procedure to match the in vivo geometries. Pressure-driven tear initiation and propagation were studied. Our results show that when the tear surface's normal is perpendicular to the blood flow, the critical pressure required to cause further propagation is higher for the shorter and deeper tears and reduces when the initial tear size increases. When the normal is parallel to the blood flow, the difference in critical pressure with an increase in tear depth is small and is more likely to propagate transversely. Also, the critical pressure decreases with an increase in the diameter of the aorta for all the tear orientations. This study concludes that tear size, depth inside the medial layer and the diameter of the aorta near the tear location are critical parameters in assessing the risk of further propagation.

BackgroundAsthma affects 235 million people worldwide. Supported self-management, including an action plan agreed with clinicians, improves asthma outcomes. Internet-of-things (IoT) systems with artificial intelligence (AI) can provide customized support for a range of self-management functions, but trust is vital to encourage patients’ adoption of such systems. Many models for understanding trust exist, some explicitly designed for eHealth, but no studies have used these models to explore trust in the context of using IoT systems to support asthma self-management.ObjectiveIn this study, we aim to use the McKnight model to explore the functionality, helpfulness, and reliability domains of patients’ and clinicians’ trust in IoT systems to deliver the 14 components of self-management support defined by the PRISMS (Practical Reviews in Self-Management Support) taxonomy.MethodsWe used think-aloud techniques in semistructured interviews to explore the views of patients and clinicians. Patients were recruited from research registers and social media and purposively sampled to include a range of ages, genders, action plan ownership, asthma duration, hospital admissions, and experience with mobile apps. Clinicians (primary, secondary, and community-based) were recruited from professional networks. Interviews were transcribed verbatim, and thematic analysis was used to explore perceptions of the functionality, helpfulness, and reliability of IoT features to support components of supported self-management.ResultsA total of 12 patients and 12 clinicians were interviewed. Regarding perceived functionality, most patients considered that an IoT system had functionality that could support a broad range of self-management tasks. They wanted a system to provide customized advice involving AI. With regard to perceived helpfulness, they considered that IoT systems could usefully provide integrated support for a number of recognized components of self-management support. In terms of perceived reliability, they believed they could rely on the system to log their asthma condition and provide preset action plan advice triggered by their logs. However, they were less confident that the system could operate continuously and without errors in providing advice. They were not confident that AI could generate new advice or reach diagnostic conclusions without the interpretation of their trusted clinicians. Clinicians wanted clinical evidence before trusting the system.ConclusionsIoT systems including AI were regarded as offering potentially helpful functionality in mediating the action plans developed with a trusted clinician, although our technologically adept participants were not yet ready to trust AI to generate novel advice. Research is needed to ensure that technological capability does not outstrip the trust of individuals using it.

<b>Aim:</b> The Internet-of-Thing (IoT) can support self-management with a broad range of applications. We explored the 'connected' features that patients and clinicians want in order to support asthma self-management. <b>Methods:</b> We interviewed a maximum variation sample of patients and clinicians recruited via social media and professional contacts and explored opinions about preferred IoT features. Thematic analysis used the Practical Reviews in Self-Management Support (PRISMS) taxonomy. Descriptive analysis of an online questionnaire enabled triangulation of findings. <b>Results:</b> We interviewed 12 patients, 12 (primary/secondary/tertiary care) clinicians. 140 patients completed questionnaires online. Patients preferred 'passive' observation of their status, and wanted real-time advice to support their self-management decisions. Peak flows (24%), pollen, humidity, air temperature (24%), and asthma symptoms(18%) were the data that patients most wanted the system to capture. Detecting incorrect inhaler technique, monitoring medication stock and advice on medication adjustment were the self-management advice wanted by most patients. Clinicians and patients wanted a shift to more flexible consultations and responsive step up/down strategies to maintain control and avoid unnecessary side-effects. <b>Conclusion:</b> We identified a number of features to inform future IoT development to support asthma self-management. We propose a paradigm shift in digitised self-management–from standardised monitoring to a personalised, more fluid approach to support individuals over time.

Numerical models of endografts for the simulation of endovascular aneurysm repair are increasingly important in the improvement of device designs and patient outcomes. Nevertheless, current finite element analysis (FEA) models of complete endograft devices come at a high computational cost, requiring days of runtime, therefore restricting their applicability. In the current study, an efficient FEA model of the Anaconda™ endograft (Terumo Aortic, UK) was developed, able to yield results in just over 4 h, an order of magnitude less than similar models found in the literature. The model was used to replicate a physical device that was deployed in a 3D printed aorta and comparison of the two shapes illustrated a less than 5 mm placement error of the model in the regions of interest, consistent with other more computationally intensive models in the literature. Furthermore, the final goal of the study was to utilize the deployed fabric model in a hemodynamic analysis that would incorporate realistic fabric folds, a feature that is almost always omitted in similar simulations. By successfully exporting the deployed graft geometry into a flow analysis, it was illustrated that the inclusion of fabric wrinkles enabled clinically significant flow patterns such as flow stagnation and recirculation to be detected, paving the way for this modelling methodology to be used in future for stent design optimisation.

Arterial replacement using synthetic grafts is currently the standard of care for patients with arterial aneurysm and/or dissection. These disease states can result in death due to arterial rupture if left untreated. However, the treatment itself, using knitted or woven polyester grafts, can lead to longer term health problems for the patient, including: high blood pressure and heart strain due to the mismatch in compliance between the synthetic graft and the native vessel; problems related to poor bio-integration of the graft prosthesis, including chronic infection; and restricted blood supply to organs due to mismatched geometry of the synthetic graft. The use of synthetic grafts for revascularisation of smaller diameter arteries is limited by very low patency rates, meaning that revascularisation of the coronary circulation still requires the use of autologous grafts. Alternative vascular graft technologies are therefore required. In this study, we have evaluated the potential of alginate as a novel graft material and characterised a rapid manufacturing method for the production of multi-layered synthetic vessels. Alginate hydrogel formation can be easily achieved in the presence of divalent cations, such as calcium or barium, in a process known as ionic crosslinking. To assess the effect of this crosslinking method on the mechanical properties of the gel and whether the chosen material mimics the biomechanical properties of native vessels, alginate gels (4, 5% (w/v)) in disc form using calcium and barium cross-linkers at different concentrations (0.1, 0.2, 0.3M) were mechanically characterised via compression testing. Results showed that ionically crosslinked alginate exhibits a viscoelastic behaviour similar to soft tissue, rendering it a potentially suitable material candidate for blood vessel substitution. A dip-coating method involving the immersion of a stainless-steel mandrel (4 mm diameter) within alginate and cross-linker solutions, yielded multi-layer tubular structures. The resultant tubular vessel constructs were found to be stable within phosphate buffered saline (0.01M, 37 °C) over a period of two weeks. The formulations used in this study are compatible with vascular cells and future work will therefore focus on the development of cell-laden constructs, with the use of 3D printing explored as a means of better matching the vessels produced to the patient’s anatomy. It is envisaged that this novel approach, will help accelerate progress towards the development of personalised vascular grafts with optimised biological and mechanical properties, thereby providing improved outcomes for patients. The authors acknowledge the funding support from Terumo Aortic, SRPe NMIS-IDP (L. Asciak) and EPSRC (J. Dow, Grant No.: EP/L015595/1).

Superelastic Ni-Ti (Nitinol) is a member of the shape memory alloy (SMA) family of metals. The physical properties of Nitinol are highly dependant on a number of factors, including manufacturing method, subsequent processing, operating temperature, and strain rate. These factors complicate the prescription of material constitutive models, leading to complexities in the computational analysis of Nitinol components. The current work explores the limitations in the Nitinol material model available in existing commercial finite element (FE) software using a series of specially design experimental tests and representative FE models.