Towards patient-specific modelling as a pre-operative planning strategy and follow up assessment for the treatment of advanced heart failure with rotary blood pumps

Background: Ventricular Assist Devices (VADs) insertion is an established treatment for patients with end-stage heart failure waiting for a heart transplant or in need for long-term circulatory support (destination therapy). Rotary blood pumps (RBP) are the most popular devices in view of their size and performance. Pre-operative planning strategy for the insertion of a left ventricular assist device (LVAD) requires a timely discussion at a Multi-Disciplinary Team Meeting (MDT). Clinical-decision making is based according to the needs of the patient and must be processed without delays. Nevertheless, thrombus formation remains a feared complication which affects outcome. VADs operate in a flow regime which is difficult to simulate: the transitional region at the boundary of laminar and turbulent flow (low Reynolds number). Different methods have been used but the best approach remains debatable. Computational Fluid Dynamics (CFD) is an attractive and invaluable tool for the study of the interactions between VADs and the cardiovascular system. The aim of this thesis is three-fold: a) to investigate the use of pressure-volume analysis in a clinical setting through the review of six heart failure patients previously discussed at a MDT meeting with a view to predict or guide further management; b) to review the theory behind modelling approaches to VADs and their interactions with the cardiovascular system for better understanding of their clinical use. Then, an overview of computational fluid dynamics (CFD) is considered as a prelude to its application to the analysis of VADs performance. Additionally, the development of a simplified model of centrifugal pump will be used in initial simulations as preliminary analysis; c) to examine an example of a proof-of-concept pilot patient-specific model of an axial flow pump (HeartMate II) as pre-operative planning strategy in a patient-specific model with a view to identify potential critical areas that may affect pump function and outcome in a clinical setting. Material and Methods: 3D reconstruction from CT-scan images of patients who underwent the insertion of rotary blood pumps, namely HeartWare HVAD and HeartMate II. Ansys Fluent has been used for CFD analysis based on the fundamental governing equations of motion. Blood has been modelled as incompressible, Newtonian fluid with density = 1060 and viscosity = 0.0035 kg/m-s. The laminar and SST models have been used for comparison purposes. The rotational motion of the impeller has been implemented using the moving reference frame (MRF) approach. The sliding mesh method has also been used to account for unsteady interaction between stationary and moving part. The no-slip condition has been applied to all walls, which were assumed to be rigid. Boundary conditions consisting of velocity inlet and pressure outlet of the pump based on different settings and constant rotational speed for the impeller. Pressure-velocity coupling has been based on the coupled scheme. Spatial discretisation consisted of the “least square cell based” gradient for velocity and “PRESTO” or second order for pressure. Second order upwind has been set for the momentum, turbulent kinetic energy and specific dissipation rate. First order implicit has been set for transient formulation. The pseudo transient algorithm (steady state), the high order relaxation term and the warped-face gradient correction have been used to add an unsteady term to the solution equations with the aim to improve stability and enhance convergence. Specific settings have been considered for comparison purposes. Results: Pressure-volume simulation analysis in six advanced heart failure patients showed that an integrated model of the cardiovascular system based on lumped-parameter representation, modified time-varying elastance and pressure-volume analysis of ventricular function seems a feasible and suitable approach yielding a sufficiently accurate quantitative analysis in real time, therefore applicable within the time-constraints of a clinical setting. Lumped-parameter models consist of simultaneous ordinary differential equations complemented by an algebraic balance equation and are suitable for examination of global distribution of pressure, flow and volume over a range of physiological conditions with inclusion of the interaction between modelled components. Higher level lumped-parameter modelling is needed to address the interaction between the circulation and other systems based on a compromise between complexity and ability to set the required parameters to personalise an integrated lumped-parameter model for a patient-specific approach. CARDIOSIM© fulfils these requirements and does address the systems interaction with its modular approach and assembly of models with varying degree of complexity although 0-D and 1-D coupling may be required for the evaluation of long-term VAD support. The challenge remains the ability to predict outcome over a longer period of time. The preliminary CFD simulations with the HeartWare HVAD centrifugal pump demonstrated that it is possible to obtain an accurate analysis in a timely manner to complement the clinical review process. The simulations with the pilot patient-specific model of the HeartMate II axial flow pump revealed that a complex 3D reconstruction is feasible in a timely manner and can be used to generate sufficiently accurate results to be used in the context of a MDT meeting for the purposes of clinical decision-making. Overall, these three studies demonstrate that the time frame of the simulations was within hours which may fit the time constraints of the clinical environment in the context of a MDT meeting. More specifically, it was shown that the laminar model may be used for an initial evaluation of the flow development within the pump. Nonetheless, the k- model offers higher accuracy if the timeline of the clinical setting allows for a longer simulation. Conclusion: This thesis aimed at the understanding of the use of computational modelling as a pre-operative planning strategy and follow up assessment for the treatment of advanced heart failure with rotary blood pumps. The novelty lays in the use of both pressure-volume simulation analysis and 3D flow dynamics studies in VADs with a view to treatment optimisation and outcome prediction within the time constraints of a clinical setting in the context of a MDT meeting. The clinical significance and the contribution to the field is a more targeted approach for different groups of patients and a more quantitative evaluation in the clinical decision process based on a pro-active co-operation between clinicians and scientists reducing the potential for “guess work”. The results of this thesis are a proof-of-concept as a prelude to a potential future implementation of patient-specific modelling within a clinical setting on a daily basis demonstrating a clear clinical significance and contribution to the field. The proposed approach does not consider modelling and simulation as a substitute for clinical experience but an additional tool to guide therapeutic intervention and complement the clinical decision process in which the clinician remains the ultimate decision-maker. Such an approach may well add a different dimension to the problem of heart failure with potential for high return in terms of patient’s outcome and long-term surveillance. The same principles would be applicable to other cardiovascular problems in line with the current concept of “Team Approach” such as the Heart Team, the Structural Heart Team or the Aortic Team. The present work has taken this concept closer to clinical delivery and has highlighted its potential but further work remains to be done in refining the technique.Background: Ventricular Assist Devices (VADs) insertion is an established treatment for patients with end-stage heart failure waiting for a heart transplant or in need for long-term circulatory support (destination therapy). Rotary blood pumps (RBP) are the most popular devices in view of their size and performance. Pre-operative planning strategy for the insertion of a left ventricular assist device (LVAD) requires a timely discussion at a Multi-Disciplinary Team Meeting (MDT). Clinical-decision making is based according to the needs of the patient and must be processed without delays. Nevertheless, thrombus formation remains a feared complication which affects outcome. VADs operate in a flow regime which is difficult to simulate: the transitional region at the boundary of laminar and turbulent flow (low Reynolds number). Different methods have been used but the best approach remains debatable. Computational Fluid Dynamics (CFD) is an attractive and invaluable tool for the study of the interactions between VADs and the cardiovascular system. The aim of this thesis is three-fold: a) to investigate the use of pressure-volume analysis in a clinical setting through the review of six heart failure patients previously discussed at a MDT meeting with a view to predict or guide further management; b) to review the theory behind modelling approaches to VADs and their interactions with the cardiovascular system for better understanding of their clinical use. Then, an overview of computational fluid dynamics (CFD) is considered as a prelude to its application to the analysis of VADs performance. Additionally, the development of a simplified model of centrifugal pump will be used in initial simulations as preliminary analysis; c) to examine an example of a proof-of-concept pilot patient-specific model of an axial flow pump (HeartMate II) as pre-operative planning strategy in a patient-specific model with a view to identify potential critical areas that may affect pump function and outcome in a clinical setting. Material and Methods: 3D reconstruction from CT-scan images of patients who underwent the insertion of rotary blood pumps, namely HeartWare HVAD and HeartMate II. Ansys Fluent has been used for CFD analysis based on the fundamental governing equations of motion. Blood has been modelled as incompressible, Newtonian fluid with density = 1060 and viscosity = 0.0035 kg/m-s. The laminar and SST models have been used for comparison purposes. The rotational motion of the impeller has been implemented using the moving reference frame (MRF) approach. The sliding mesh method has also been used to account for unsteady interaction between stationary and moving part. The no-slip condition has been applied to all walls, which were assumed to be rigid. Boundary conditions consisting of velocity inlet and pressure outlet of the pump based on different settings and constant rotational speed for the impeller. Pressure-velocity coupling has been based on the coupled scheme. Spatial discretisation consisted of the “least square cell based” gradient for velocity and “PRESTO” or second order for pressure. Second order upwind has been set for the momentum, turbulent kinetic energy and specific dissipation rate. First order implicit has been set for transient formulation. The pseudo transient algorithm (steady state), the high order relaxation term and the warped-face gradient correction have been used to add an unsteady term to the solution equations with the aim to improve stability and enhance convergence. Specific settings have been considered for comparison purposes. Results: Pressure-volume simulation analysis in six advanced heart failure patients showed that an integrated model of the cardiovascular system based on lumped-parameter representation, modified time-varying elastance and pressure-volume analysis of ventricular function seems a feasible and suitable approach yielding a sufficiently accurate quantitative analysis in real time, therefore applicable within the time-constraints of a clinical setting. Lumped-parameter models consist of simultaneous ordinary differential equations complemented by an algebraic balance equation and are suitable for examination of global distribution of pressure, flow and volume over a range of physiological conditions with inclusion of the interaction between modelled components. Higher level lumped-parameter modelling is needed to address the interaction between the circulation and other systems based on a compromise between complexity and ability to set the required parameters to personalise an integrated lumped-parameter model for a patient-specific approach. CARDIOSIM© fulfils these requirements and does address the systems interaction with its modular approach and assembly of models with varying degree of complexity although 0-D and 1-D coupling may be required for the evaluation of long-term VAD support. The challenge remains the ability to predict outcome over a longer period of time. The preliminary CFD simulations with the HeartWare HVAD centrifugal pump demonstrated that it is possible to obtain an accurate analysis in a timely manner to complement the clinical review process. The simulations with the pilot patient-specific model of the HeartMate II axial flow pump revealed that a complex 3D reconstruction is feasible in a timely manner and can be used to generate sufficiently accurate results to be used in the context of a MDT meeting for the purposes of clinical decision-making. Overall, these three studies demonstrate that the time frame of the simulations was within hours which may fit the time constraints of the clinical environment in the context of a MDT meeting. More specifically, it was shown that the laminar model may be used for an initial evaluation of the flow development within the pump. Nonetheless, the k- model offers higher accuracy if the timeline of the clinical setting allows for a longer simulation. Conclusion: This thesis aimed at the understanding of the use of computational modelling as a pre-operative planning strategy and follow up assessment for the treatment of advanced heart failure with rotary blood pumps. The novelty lays in the use of both pressure-volume simulation analysis and 3D flow dynamics studies in VADs with a view to treatment optimisation and outcome prediction within the time constraints of a clinical setting in the context of a MDT meeting. The clinical significance and the contribution to the field is a more targeted approach for different groups of patients and a more quantitative evaluation in the clinical decision process based on a pro-active co-operation between clinicians and scientists reducing the potential for “guess work”. The results of this thesis are a proof-of-concept as a prelude to a potential future implementation of patient-specific modelling within a clinical setting on a daily basis demonstrating a clear clinical significance and contribution to the field. The proposed approach does not consider modelling and simulation as a substitute for clinical experience but an additional tool to guide therapeutic intervention and complement the clinical decision process in which the clinician remains the ultimate decision-maker. Such an approach may well add a different dimension to the problem of heart failure with potential for high return in terms of patient’s outcome and long-term surveillance. The same principles would be applicable to other cardiovascular problems in line with the current concept of “Team Approach” such as the Heart Team, the Structural Heart Team or the Aortic Team. The present work has taken this concept closer to clinical delivery and has highlighted its potential but further work remains to be done in refining the technique

Automated development of clinical prediction models using genetic programming

Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Genetic programming is a general methodology, the specific implementation of which requires development of several different specific elements such as problem representation, fitness, selection and genetic variation. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this work was to develop a genetic programming approach for survival analysis and demonstrate its utility for the automatic development of clinical prediction models using cardiovascular disease as a case study. We developed a tree-based untyped steady-state genetic programming approach for censored longitudinal data, comparing its performance to the de facto statistical method—Cox regression—in the development of clinical prediction models for the prediction of future cardiovascular events in patients with symptomatic and asymptomatic cardiovascular disease, using large observational datasets. We also used genetic programming to examine the prognostic significance of different risk factors together with their non-linear combinations for the prognosis of health outcomes in cardiovascular disease. These experiments showed that Cox regression and the developed steady-state genetic programming approach produced similar results when evaluated in common validation datasets. Despite slight relative differences, both approaches demonstrated an acceptable level of discriminative and calibration at a range of times points. Whilst the application of genetic programming did not provide more accurate representations of factors that predict the risk of both symptomatic and asymptomatic cardiovascular disease when compared with existing methods, genetic programming did offer comparable performance. Despite generally comparable performance, albeit in slight favour of the Cox model, the predictors selected for representing their relationships with the outcome were quite different and, on average, the models developed using genetic programming used considerably fewer predictors. The results of the genetic programming confirm the prognostic significance of a small number of the most highly associated predictors in the Cox modelling; age, previous atherosclerosis, and albumin for secondary prevention; age, recorded diagnosis of ’other’ cardiovascular disease, and ethnicity for primary prevention in patients with type 2 diabetes. When considered as a whole, genetic programming did not produce better performing clinical prediction models, rather it utilised fewer predictors, most of which were the predictors that Cox regression estimated be most strongly associated with the outcome, whilst achieving comparable performance. This suggests that genetic programming may better represent the potentially non-linear relationship of (a smaller subset of) the strongest predictors. To our knowledge, this work is the first study to develop a genetic programming approach for censored longitudinal data and assess its value for clinical prediction in comparison with the well-known and widely applied Cox regression technique. Using empirical data this work has demonstrated that clinical prediction models developed by steady-state genetic programming have predictive ability comparable to those developed using Cox regression. The genetic programming models were more complex and thus more difficult to validate by domain experts, however these models were developed in an automated fashion, using fewer input variables, without the need for domain specific knowledge and expertise required to appropriately perform survival analysis. This work has demonstrated the strong potential of genetic programming as a methodology for automated development of clinical prediction models for diagnostic and prognostic purposes in the presence of censored data. This work compared untuned genetic programming models that were developed in an automated fashion with highly tuned Cox regression models that was developed in a very involved manner that required a certain amount of clinical and statistical expertise. Whilst the highly tuned Cox regression models performed slightly better in validation data, the performance of the automatically generated genetic programming models were generally comparable. The comparable performance demonstrates the utility of genetic programming for clinical prediction modelling and prognostic research, where the primary goal is accurate prediction. In aetiological research, where the primary goal is to examine the relative strength of association between risk factors and the outcome, then Cox regression and its variants remain as the de facto approach

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology

SOLID-SHELL FINITE ELEMENT MODELS FOR EXPLICIT SIMULATIONS OF CRACK PROPAGATION IN THIN STRUCTURES

Crack propagation in thin shell structures due to cutting is conveniently simulated using explicit finite element approaches, in view of the high nonlinearity of the problem. Solidshell elements are usually preferred for the discretization in the presence of complex material behavior and degradation phenomena such as delamination, since they allow for a correct representation of the thickness geometry. However, in solid-shell elements the small thickness leads to a very high maximum eigenfrequency, which imply very small stable time-steps. A new selective mass scaling technique is proposed to increase the time-step size without affecting accuracy. New ”directional” cohesive interface elements are used in conjunction with selective mass scaling to account for the interaction with a sharp blade in cutting processes of thin ductile shells

Pharmacovigilance Decision Support : The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology

The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms.Doctor of Philosoph

Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging

Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data

Large space structures and systems in the space station era: A bibliography with indexes (supplement 04)

Bibliographies and abstracts are listed for 1211 reports, articles, and other documents introduced into the NASA scientific and technical information system between 1 Jul. and 30 Dec. 1991. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems