Assessment of Haemodynamic Remodeling in Fetal Aortic Coarctation Using a Lumped Model of the Circulation

Introduction: Aortic coarctation is one of the most difficult cardiac defects to diagnose before birth, and it accounts for 8% of congenital heart diseases. Antenatal diagnosis is crucial for early treatment of the neonate and to decrease the risk of morbidity and mortality; however the fetal hemodynamic changes are not fully understood and current imaging methods are limited to accurately diagnosis this congenital defect. Objective: We propose to use a lumped model of the fetal circulation to provide insights into the hemodynamic changes in fetuses with aortic coarctation, and thus helping to improve its diagnosis. Methods: To achieve this goal a patient-specific lumped model of the fetal circulation was implemented in OpenCOR, including the modeling of different types and degrees of aortic coarctation. A parametric study of degree and type of coarctation was performed, where blood flow distribution, cerebroplacental ratio, pressure drop over the coarctation and left ventricular pressure were quantified. Results: Obvious changes in the fetal hemodynamics were observed only from 80% of coarctation, corresponding to the clinically used cut-off for pressure drop of 20 mmHg. Furthermore, the observed hemodynamic changes were different depending on the location and degree of the coarctation

Computational model of the fetal heart with Coarctation of the Aorta

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2021-2022. Directors: Patricia Garcia Cañadilla & Bart Bijnens. Tutora: Fátima Crispi.It is thought that altered intrauterine hemodynamics may lead to congenital heart defects, such as aortic arch abnormalities. Coarctation of the aorta (CoA) is one of the most difficult cardiac defects to diagnose before birth, because of the patency of the ductus arteriosus (DA). It consists of a narrowing in the aortic isthmus (AoI) causing a decrease of blood flow. Prenatal diagnosis is important to reduce mortality and morbidity. Nonetheless, prenatal diagnosis has a high rate of false-positive and false-negatives and local hemodynamics in the CoA is not fully understood. The aim of this project was to improve our understanding of the underlying cause of CoA using computational fluid dynamics (CFD) tools. We have implemented a computational model with an idealized geometry of the fetal aorta to investigate the relationship between flow unbalance and wall shear stress (WSS) at the isthmus-ductus. An imbalanced flow was imposed in the ascending aorta (AscAo) and ductus to study if a progressive aortic flow reduction suggests the “flowdependency” of the fetal aortic arch development. As a result, when aortic flow diminished from 50% to 10% progressively, velocity and WSS decreased in the aortic arch and increased in the distal arch. A redistribution of flow could be observed in the model and a “zero flow zone” could be noticed between the brachiocephalic artery and left carotid when the flow decreased to from 50% to 10%. Additionally, another “zero flow zone” could be observed in the AoI when the aortic flow decreased from 50% to 30%

Tuning of boundary conditions parameters for hemodynamics simulation using patient data

This thesis describes an engineering workflow, which allows specification of boundary conditions and 3D simulation based on clinically available patient-specific data. A review of numerical models used to describe the cardiovascular system is provided, with a particular focus on the clinical target disease chosen for the toolkit, aortic coarctation. Aorta coarctation is the fifth most common congenital heart disease, characterized by a localized stenosis of the descending thoracic aorta. Current diagnosis uses invasive pressure measurement with rare but potential complications. The principal objective of this work was to develop a tool that can be translated into the clinic, requiring minimum operator input and time, capable of returning meaningful results from data typically acquired in clinical practice. Linear and nonlinear 1D modelling approaches are described, tested against full 3D solutions derived for idealized geometries of increasing complexityand for a patient-specific aortic coarctation. The 1D linear implementation is able to represent the fluid dynamic in simple idealized benchmarks with a limited effort in terms of computational time, but in a more complex case, such as a mild aortic coarctation, it is unable to predict well 3D fluid dynamic features. On the other side, the 1D nonlinear implementation showed a good agreement when compared to 3D pressure and flow waveforms, making it suitable to estimate outflow boundary conditions for subject-specific models. A cohort of 11 coarctation patients was initially used for a preliminary analysis using 0D models of increasing complexity to examine parameters derived when tuning models of the peripheral circulation. The first circuit represents the aortic coarctation as a nonlinear resistance, using the Bernoulli pressure drop equation, without considering the effect of downstream circulation. The second circuit include a peripheral resistance and compliance, and separate ascending and descending aortic pressure responses. In the third circuit a supra-aortic Windkessel model was added in order to include the supra-aortic circulation. The analysis detailed represents a first attempt to assess the interaction between local aortic haemodynamics and subject-specific parameterization of windkessel representations of the peripheral and supra-aortic circulation using clinically measured data. From the analysis of these 0D models, it is clear that the significance of the coarctation becomes less from the simple two resistance model to the inclusion of both the peripheral and supra-aortic circulation. These results provide a context within which to interpret outcomes of the tuning process reported for a more complex model of aortic haemodynamics using 1D and 3D model approaches. Earlier developments are combined to enable a multi-scale modelling approach to simulate fluid-dynamics. This includes non-linear 1D models to derive patient-specific parameters for the peripheral and supra-aortic circulation followed by transient analysis of a coupled 3D/0D system to estimate the coarctation pressure augmentation. These predictions are compared with invasively measured catheter data and the influence of uncertainty in measured data on the tuning process is discussed. This study has demonstrated the feasibility of constructing a workflow using non-invasive routinely collected clinical data to predict the pressure gradient in coarctation patients using patient specific CFD simulation, with relatively low levels of user interaction required. The results showed that the model is not suitable for the clinical use at this stage, thus further work is required to enhance the tuning process to improve agreement with measured catheter data. Finally, a preliminary approach for the assessment of change in haemodynamics following coarctation repair, where the coarctation region is enlarged through a virtual intervention process. The CFD approach reported can be expanded to explore the sensitivity of the peak ascending aortic pressure and descending aortic flow to the aortic diameter achieved following intervention, such an analysis would provide guidance for surgical intervention to target the optimal diameter to restore peripheral perfusion and reduce cerebral hypertension

Machine Learning in Fetal Cardiology: What to Expect

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities

Role of Computational Fluid Dynamics in the Analysis of Haemodynamic and Morphological Characteristics of Intracranial Aneurysms

Aneurysmal subarachnoid hemorrhage (SAH) carries a high morbidity and mortality. The current protocols used to treat the unruptured Intracranial Aneurysms (IAs) are inadequate underscoring the need of finding new descriptors. As demonstrated by the studies performed in this manuscript, haemodynamics plays an important role in the aetiopathogenesis of IAs. An evaluation of haemodynamic indices can provide a useful alternative to predict the behavior of an unruptured IA at an early stage. Studies performed by me demonstrate that Computational Fluid Dynamics (CFD) can be used successfully to predict haemodynamic indices where detailed in vivo measurement of haemodynamic flow variables is not possible owing to technical limitations. European Commission funded Project @neurIST was the first project of it’s kind that brought together a number of multidisciplinary professionals from 32 European institutions and made possible development of state-of-the-art tools for personalised risk assessment and treatment IAs using CFD. These tools have been constantly improved and amended in the light of feedback gathered from their controlled exposures conducted world over, as described in the manuscript. However, need of a well-designed Randomized Controlled Trial in this context cannot be overemphasized, before these tools can be accepted by clinicians and patients. In my study on the validation of different concepts used in CFD, I demonstrated that there is no added advantage of complex Womersley-flow-profile over the much simpler plug-flow profile. One of my studies on initiation and rupture of IAs showed that the haemodynamic patterns of IAs during these two phases are significantly different with values of supra-physiological Wall Shear Stress (WSS) being higher in initiation while lower in rupture phase. I also investigated the effects of pharmacological agents on the aetiopathogenesis of IAs and found that heparin induces significant derangements in the haemodynamics of both, pre-aneurysmal as well as ruptured IA. I propose that heparin (and its derivatives) can, on the one hand may facilitate the rupture of existing IAs, on the other hand they may suppress the formation of new IAs. I have also found significant differences in the results using patient-specific vs. Modeled Boundary Conditions and showed that the 1D circulation model adopted by @neurIST performs better than other approaches found in the literature. I also proposed a novel mechanism of increase in Blood Viscosity leading to high WSS as one of the important underlying mechanisms responsible for the increased incidence of IA formation in smokers and hypertensive patients. In my study on patients with pre-existing Coarctation of Aorta (CoA) and Intracranial Aneurysms, I demonstrated that the cerebral flow-rates in CoA patients were significantly higher when compared to average flow-rates in healthy population. It was also seen that the values and the area affected by supraphysiological WSS (>15Pa) were exponentially higher in patients with CoA indicating the possible role of increased haemodynamic WSS secondary to the increased flow-rates playing an important role in the pathogenesis and rupture of IAs in CoA patients

Hypoplastic Left Heart Syndrome Current Considerations and Expectations

In the recent era, no congenital heart defect has undergone a more dramatic change in diagnostic approach, management, and outcomes than hypoplastic left heart syndrome (HLHS). During this time, survival to the age of 5 years (including Fontan) has ranged from 50% to 69%, but current expectations are that 70% of newborns born today with HLHS may reach adulthood. Although the 3-stage treatment approach to HLHS is now well founded, there is significant variation among centers. In this white paper, we present the current state of the art in our understanding and treatment of HLHS during the stages of care: 1) pre-Stage I: fetal and neonatal assessment and management; 2) Stage I: perioperative care, interstage monitoring, and management strategies; 3) Stage II: surgeries; 4) Stage III: Fontan surgery; and 5) long-term follow-up. Issues surrounding the genetics of HLHS, developmental outcomes, and quality of life are addressed in addition to the many other considerations for caring for this group of complex patients

Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie - klinische Anwendung bildbasierter in silico Modellierung

Die richtige Therapie zum richtigen Zeitpunkt, nichtinvasiv und patientenindividuell zu identifizieren, ist das Ziel der Präzisionsmedizin. Durch den stetigen Fortschritt sowohl im Bereich der Bildgebung als auch in mathematischen Modellierungstechniken sowie einer zunehmenden Verfügbarkeit von leistungsstarker Informationstechnologie, gewinnen in silico (angelehnt an das Lateinische „in silicio“, also „in silicium“ bzw. im übertragenden Sinne im Computer ablaufende) Modellierungsverfahren eine immer größere Bedeutung auch im Bereich der kardiovaskulären Medizin. Die bildbasierte in silico Modellierung von Hämodynamik und Funktion des Herzens kann dabei einerseits helfen, die diagnostische Aussagekraft unterschiedlicher Bildgebungsmodalitäten zu erweitern, andererseits aber auch, verschiedene Parameter der postinterventionellen bzw. postoperativen Funktion vorherzusagen und so das geeignetste patientenindividuelle Therapieverfahren zu identifizieren. Im Bereich der pädiatrischen Kardiologie, insbesondere bei Patient*innen mit komplexen angeborenen Herzfehlern, ist eine individualisierte Therapieplanung zudem von ganz besonderer Bedeutung. Da die Anatomie des kardiovaskulären Systems in diesem Patientenkollektiv hoch individuell ist, gibt es häufig keine für das jeweilige Krankheitsbild einheitliche Therapie. Die virtuelle Behandlungsplanung bietet hier ein großes Potential für die multimodale Therapiefindung. Die Translation solcher Modellierungsansätze in die Klinik stellt jedoch eine große Hürde dar. Einerseits muss die Genauigkeit der jeweiligen Simulationsmethode quantifiziert und die Methode selbst validiert werden. Dafür benötigt es in der Regel eine hohe Anzahl an Patientendaten, die insbesondere in der Kinderkardiologie, aber auch aufgrund zunehmend strengerer Datenschutzrichtlinien häufig nicht zur Verfügung stehen. Andererseits sind die Simulationsverfahren sehr komplex und verlangen neben einer hohen technischen Expertise auch beachtliche Rechenkapazitäten und -laufzeiten, wodurch sich ihr routinemäßiger Einsatz in der Klinik ebenfalls verkompliziert. Das Problem der hohen Komplexität könnte durch den Einsatz künstlicher Intelligenz (KI) überwunden werden. Fehlende klinische Daten wiederum könnten mittels synthetischer Patientenkohorten augmentiert werden, sodass sowohl für mögliche Validierungsstudien als auch zum Trainieren des maschinellen Algorithmus‘ ein ausreichend großer Datensatz zur Verfügung stünde. In der vorliegenden Habilitationsschrift werden die Inhalte von fünf wissenschaftlichen Arbeiten zum Thema Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie auf Grundlage bildbasierter in silico Modellierung vorgestellt. Dabei wird in Form einer Proof of Concept Studie die prinzipielle Durchführbarkeit der bildbasierten in silico Modellierung am Beispiel verschiedener Parameter der aortalen Hämodynamik gezeigt sowie die Validierung der Methodik gegen den klinischen Goldstandard des Herzkatheters präsentiert. An komplexen Patient*innen aus dem Bereich der Kinderkardiologie wird die bildbasierte in silico Modellierung für eine konkrete klinische Fragestellung angewandt. Zuletzt werden zwei Optimierungsansätze vorgestellt, die einerseits den komplexen Arbeitsablauf der bildbasierten in silico Modellierung mittels KI vereinfachen sowie andererseits das Problem der existierenden klinischen Datenlücken überwinden sollen

In-Vitro and In-Silico Investigations of Alternative Surgical Techniques for Single Ventricular Disease

Single ventricle (SV) anomalies account for one-fourth of all cases of congenital Heart disease. The conventional second and third stage i.e. Comprehensive stage II and Fontan procedure of the existing three-staged surgical approach serving as a palliative treatment for this anomaly, entails multiple complications and achieves a survival rate of 50%. Hence, to reduce the morbidity and mortality rate associated with the second and third stages of the existing palliative procedure, the novel alternative techniques called “Hybrid Comprehensive Stage II” (HCSII), and a “Self-powered Fontan circulation” have been proposed. The goal of this research is to conduct in-vitro investigations to validate computational and clinical findings on these proposed novel surgical techniques. The research involves the development of a benchtop study of HCSII and self-powered Fontan circulation

Automated deep phenotyping of the cardiovascular system using magnetic resonance imaging

Across a lifetime, the cardiovascular system must adapt to a great range of demands from the body. The individual changes in the cardiovascular system that occur in response to loading conditions are influenced by genetic susceptibility, and the pattern and extent of these changes have prognostic value. Brachial blood pressure (BP) and left ventricular ejection fraction (LVEF) are important biomarkers that capture this response, and their measurements are made at high resolution. Relatively, clinical analysis is crude, and may result in lost information and the introduction of noise. Digital information storage enables efficient extraction of information from a dataset, and this strategy may provide more precise and deeper measures to breakdown current phenotypes into their component parts. The aim of this thesis was to develop automated analysis of cardiovascular magnetic resonance (CMR) imaging for more detailed phenotyping, and apply these techniques for new biological insights into the cardiovascular response to different loading conditions. I therefore tested the feasibility and clinical utility of computational approaches for image and waveform analysis, recruiting and acquiring additional patient cohorts where necessary, and then applied these approaches prospectively to participants before and after six-months of exercise training for a first-time marathon. First, a multi-centre, multi-vendor, multi-field strength, multi-disease CMR resource of 110 patients undergoing repeat imaging in a short time-frame was assembled. The resource was used to assess whether automated analysis of LV structure and function is feasible on real-world data, and if it can improve upon human precision. This showed that clinicians can be confident in detecting a 9% change in EF or a 20g change in LV mass. This will be difficult to improve by clinicians because the greatest source of human error was attributable to the observer rather than modifiable factors. Having understood these errors, a convolutional neural network was trained on separate multi-centre data for automated analysis and was successfully generalizable to the real-world CMR data. Precision was similar to human analysis, and performance was 186 times faster. This real-world benchmarking resource has been made freely available (thevolumesresource.com). Precise automated segmentations were then used as a platform to delve further into the LV phenotype. Global LVEFs measured from CMR imaging in 116 patients with severe aortic stenosis were broken down into ~10 million regional measurements of structure and function, represented by computational three-dimensional LV models for each individual. A cardiac atlas approach was used to compile, label, segment and represent these data. Models were compared with healthy matched controls, and co-registered with follow-up one year after aortic valve replacement (AVR). This showed that there is a tendency to asymmetric septal hypertrophy in all patients with severe aortic stenosis (AS), rather than a characteristic specific to predisposed patients. This response to AS was more unfavourable in males than females (associated with higher NT-proBNP, and lower blood pressure), but was more modifiable with AVR. This was not detected using conventional analysis. Because cardiac function is coupled with the vasculature, a novel integrated assessment of the cardiovascular system was developed. Wave intensity theory was used to combine central blood pressure and CMR aortic blood flow-velocity waveforms to represent the interaction of the heart with the vessels in terms of traveling energy waves. This was performed and then validated in 206 individuals (the largest cohort to date), demonstrating inefficient ventriculo-arterial coupling in female sex and healthy ageing. CMR imaging was performed in 236 individuals before training for a first-time marathon and 138 individuals were followed-up after marathon completion. After training, systolic/diastolic blood pressure reduced by 4/3mmHg, descending aortic stiffness decreased by 16%, and ventriculo-arterial coupling improved by 14%. LV mass increased slightly, with a tendency to more symmetrical hypertrophy. The reduction in aortic stiffness was equivalent to a 4-year reduction in estimated biological aortic age, and the benefit was greater in older, male, and slower individuals. In conclusion, this thesis demonstrates that automating analysis of clinical cardiovascular phenotypes is precise with significant time-saving. Complex data that is usually discarded can be used efficiently to identify new biology. Deeper phenotypes developed in this work inform risk reduction behaviour in healthy individuals, and demonstrably deliver a more sensitive marker of LV remodelling, potentially enhancing risk prediction in severe aortic stenosis