Morphological and hemodynamical alterations in brachial artery and cephalic vein. An image‐based study for preoperative assessment for vascular access creation

The current study aims to computationally evaluate the effect of right upper arm position on the geometric and hemodynamic characteristics of the brachial artery (BA) and cephalic vein (CV) and, furthermore, to present in detail the methodology to characterise morphological and hemodynamical healthy vessels. Ten healthy volunteers were analysed in two configurations, the supine (S) and the prone (P) position. Lumen 3D surface models were constructed from images acquired from a non-contrast MRI sequence. Then, the models were used to numerically compute the physiological range of geometric (n = 10) and hemodynamic (n = 3) parameters in the BA and CV. Geometric parameters such as curvature and tortuosity, and hemodynamic parameters based on wall shear stress (WSS) metrics were calculated with the use of computational fluid dynamics. Our results highlight that changes in arm position had a greater impact on WSS metrics of the BA by altering the mean and maximum blood flow rate of the vessel. Whereas, curvature and tortuosity were found not to be significantly different between positions. Inter-variability was associated with antegrade and retrograde flow in BA, and antegrade flow in CV. Shear stress was low and oscillatory shear forces were negligible. This data suggests that deviations from this state may contribute to the risk of accelerated intimal hyperplasia of the vein in arteriovenous fistulas. Therefore, preoperative conditions coupled with post-operative longitudinal data will aid the identification of such relationships

Multi-scale computational modeling of coronary blood flow: application to fractional flow reserve.

Introduction. Fractional flow reserve (FFR) is presently an invasive coronary clinical index. Non-invasive CT imaging combined with computational coronary flow modelling may reduce the patient’s burden of undergoing invasive testing. Research statement. The ability to obtain information of the hemodynamic significance of detected lesions would streamline decision making in escalation to invasive angiography. Methods. A reduced order (lumped parameter) model of the coronary vasculature was further developed. The model was used in the assessment of the roles of structure and function on the FFR. Sophisticated methods were used to elicit numerical solutions. Further, CT imaging (n = 10) provided multiple porcine geometries based upon algorithms encoded within an existing scientific platform. Results. It was found that the length of large vessel stenosis and presence of microvascular disease are primary regulators of FFR. Further, the CT data provided a basis to investigate relationships between coronary geometry (structure) and blood flow (function) attributes. Discussion. The presented model, upon personalization, may compliment and streamline ongoing imaging efforts by guiding FFR assessment. It is likely to assist in preliminary data generation for future projects. The computational geometries will contribute to an open source service that will be made available to our University’s researchers

Cardiovascular models for personalised medicine: Where now and where next?

The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the digital twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Developments in physiological modelling, model personalisation, model outcome uncertainty, and the role of models in clinical decision support are addressed and ‘where-next’ steps and challenges discussed

Computational Modelling in the Management of Patients with Aortic Valve Stenosis

Background Stenosis of the aortic valve causes increased left ventricular pressure leading to adverse clinical outcomes. The selection and timing of intervention (surgical replacement or transcatheter implantation) is often unclear and is based upon limited data. Hypothesis A comprehensive and integrated personalised approach, including recognition of cardiac energetics parameters extracted from a personalised mathematical model, mapped to patient activity, has the potential to improve diagnosis and the planning and timing of interventions. Aims This project seeks to implement a simple, personalised, mathematical model of patients with aortic stenosis (AS), which can ‘measure’ cardiac work and power parameters that provide an effective characterisation of the demand on the heart in both rest and exercise conditions and can predict the changes of these parameters following an intervention. The specific aims of this project are: • to critically review current diagnostic methods • to evaluate the potential role of pre- and post-procedural measured patient activity • to implement a simple, personalised, mathematical model of patients with AS • to evaluate the potential role of a clinical decision support system Methods Twenty-two patients with severe AS according to ESC criteria were recruited. Relevant clinical, imaging, activity monitoring, six-minute walk test, and patient reported data were collected, before and early and after treatment. Novel imaging techniques were developed to help in the diagnosis of AS. A computational model was developed and executed using the data collected to create non-invasive pressure volume loops and study the global haemodynamic burden on the left ventricle. Simulations were run to predict the haemodynamic parameters both during exercise and following intervention. Modelled parameters were validated against clinically measured values. This information was then correlated with symptoms and activity data. A clinical decision support tool was created and populated with data obtained and its clinical utility evaluated. Outcomes The results of this project suggest that the combination of imaging and activity data with computational modelling provides a novel, patient-specific insight into patients’ haemodynamics and may help guide clinical decision making in patients with AS

Introducing new dimensions in pediatric oncologic surgery

In the Netherlands, approximately 30 children per year are diagnosed with kidney cancer. For most patients, the entire kidney along with the tumor is surgically removed (Total Nephrectomy, TN). For the other patients, the tumor is removed from the kidney (Nephron-Sparing Surgery, NSS). NSS is performed on children with bilateral tumors or children with specific overgrowth syndromes. NSS is only performed under very strict conditions in children who have a unilateral tumor and no overgrowth syndromes. This is because NSS is very challenging, and the tumor is not completely removed in 13.3% to 36.4% of the surgeries. Despite this, NSS is expected to have benefits compared to TN. For instance, the likelihood of chronic kidney disease later in life could be reduced. In this dissertation, we conducted research to make NSS more feasible for this specific patient group. First, we examined which patients in this group could be eligible for NSS surgically. There are oncological guidelines for this decision, but no surgical guidelines exist yet. Therefore, we conducted an international Delphi consensus study among experts in the field of NSS. The other part of our study focused on technical improvements in NSS. For this, we developed and implemented 3D technology in our surgical care. Using 3D models, a surgeon can prepare for an operation and gain a better understanding of the anatomical relationships of a patient's tumor. Additionally, we worked on surgical navigation using a holographic 3D model. The 3D model is projected holographically into a patient. This should make it easier to locate the tumor and improve the surgeon's depth perception. Following these two research directions, I looked at the impact of 3D models on surgical outcomes for patients who underwent NSS at the Princess Máxima Center. We observed a trend in decreased unexpected incomplete tumor removal in surgeries where the surgeon used a 3D model during preparation. In summary, we have taken steps to improve surgical decision-making and techniques for children with kidney tumors where nephron-sparing surgery could be possible