Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a “virtual population” from which “virtual individuals” can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the “virtual individuals” that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models

Review of Zero-D and 1-D Models of Blood Flow in the Cardiovascular System

<p>Abstract</p> <p>Background</p> <p>Zero-dimensional (lumped parameter) and one dimensional models, based on simplified representations of the components of the cardiovascular system, can contribute strongly to our understanding of circulatory physiology. Zero-D models provide a concise way to evaluate the haemodynamic interactions among the cardiovascular organs, whilst one-D (distributed parameter) models add the facility to represent efficiently the effects of pulse wave transmission in the arterial network at greatly reduced computational expense compared to higher dimensional computational fluid dynamics studies. There is extensive literature on both types of models.</p> <p>Method and Results</p> <p>The purpose of this review article is to summarise published 0D and 1D models of the cardiovascular system, to explore their limitations and range of application, and to provide an indication of the physiological phenomena that can be included in these representations. The review on 0D models collects together in one place a description of the range of models that have been used to describe the various characteristics of cardiovascular response, together with the factors that influence it. Such models generally feature the major components of the system, such as the heart, the heart valves and the vasculature. The models are categorised in terms of the features of the system that they are able to represent, their complexity and range of application: representations of effects including pressure-dependent vessel properties, interaction between the heart chambers, neuro-regulation and auto-regulation are explored. The examination on 1D models covers various methods for the assembly, discretisation and solution of the governing equations, in conjunction with a report of the definition and treatment of boundary conditions. Increasingly, 0D and 1D models are used in multi-scale models, in which their primary role is to provide boundary conditions for sophisticate, and often patient-specific, 2D and 3D models, and this application is also addressed. As an example of 0D cardiovascular modelling, a small selection of simple models have been represented in the CellML mark-up language and uploaded to the CellML model repository <url>http://models.cellml.org/</url>. They are freely available to the research and education communities.</p> <p>Conclusion</p> <p>Each published cardiovascular model has merit for particular applications. This review categorises 0D and 1D models, highlights their advantages and disadvantages, and thus provides guidance on the selection of models to assist various cardiovascular modelling studies. It also identifies directions for further development, as well as current challenges in the wider use of these models including service to represent boundary conditions for local 3D models and translation to clinical application.</p

Blood Supply to the Brain via the Carotid Arteries: Examining Obstructive and Sclerotic Disorders using Theoretical and Experimental Models

Stroke remains one of the leading causes of death in North America. Approximately half of all ischemic episodes are a direct result of carotid artery disease, which can be categorized into either obstructive or sclerotic disease. Obstructive disease is a result of plaque development that imposes a direct limitation on the physical space available for blood flow. Sclerotic disease involves the hardening of the arteries as is often a result of aging and disease. While the impact of vessel stiffening is not as obvious, it does interfere with wave propagation. Effects of obstructive and sclerotic disease were studied using a lumped parameter model that was designed to match an experimental in vitro flow loop. Mild to moderate stenosis had minimal impact on blood supply to the brain. Both stiffness of the carotid artery and severe stenosis ( 70%) had a significant reduction on blood supply to the brain (p\u3c0.01)

Nonlinear Stochastic Dynamic Systems Approach for Personalized Prognostics of Cardiorespiratory Disorders

This research investigates an approach rooted in nonlinear stochastic dynamic systems principles for personalized prognostics of cardiorespiratory disorders in the emerging point-of-care (POC) treatment contexts. Such an approach necessitates new methods for (a) quantitative and personalized modeling of underlying cardiovascular system dynamics to serve as a virtual instrument to derive surrogate (hemodynamic) signals, (b) high-specificity diagnostics to identify and localize disorders, (c) real-time prediction to provide forecasts of impending disorder episodes, and (d) personalized prognosis of the short-term variations of the risk, necessary for effective treatment decisions, based on estimating the distribution of the times remaining till the onset of an anomaly episode. The specific contributions of the dissertation work are as follows: 1. Quantitative modeling for real-time synthesis of hemodynamic signals. Features extracted from ECG signals were used to construct atrioventricular excitation inputs to a nonlinear deterministic lumped parameter model of cardiovascular system dynamics. The model-derived hemodynamic signals, personalized to an individual's physiological and anatomical conditions, would lead to cost-effective virtual medical instruments necessary for personalized POC prognostics. 2. Random graph representation of the complex cardiac dynamics for disorder diagnostics. The quantifiers of a random walk on a network reconstructed from vectorcardiogram (VCG) were investigated for the detection and localization of cardiovascular disorders. Extensive tests with signals from PTB database of PhysioNet databank suggest that locations of myocardial infarction can be determined accurately (sensitivity of ~88% and specificity of ~92%) from tracking certain consistently estimated invariants of this random walk representation. 3. Nonparametric prediction modeling of disorder episodes. A Dirichlet process based mixture Gaussian process was utilized to track and forecast the evolution of the complex nonlinear and nonstationary cardiorespiratory dynamics underlying of the measured signal features and health states. Extensive sleep tests suggest that the method can predict an impending sleep apnea episode to accuracies (R^2) of 83% and 77% for 1 step and 3 step-ahead predictions, respectively.4. Color-coded random graph representation of the state space for personalized prognostic modeling. The prognostic model used the stochastic evolution of the transition pathways from a normal state to an anomalous state in the color-coded state space network to estimate the distribution of the remaining useful life. The prognostic model was validated using the data from ECG Apnea Database (Physionet.org). The model can predict the estimated time till a disorder (apnea episode) onset to within 15% of the observed times 1-45 min ahead of their inception.Industrial Engineering & Managemen

Heart failure syndrome and predicting response to cardiac resynchronisation therapy.

Heart failure results from the heart pumping insufficient quantities of blood to meet the body’s metabolic requirements. This condition affects around 600,000 people in the United Kingdom and carries with it a significant morbidity and mortality. Patients typically complain of reduced exercise capacity and a poor quality of life. Whilst there are various pharmaceutical options available to clinicians, none directly augment cardiac function. Cardiac resynchronisation therapy (CRT) is proven to reverse the progression of left ventricular systolic dysfunction, the most common cause of heart failure. The device resynchronises inefficient cardiac function, reducing symptoms and improving stroke volume and life expectancy. However, only two thirds of patients typically derive benefit from this pacemaker, it being unclear why. Finding a sensitive and specific predictor of response would be invaluable, preventing potential harm to patients, reducing waste and targeting the patient groups who will derive benefit. In this body of work, the heart failure syndrome is delineated; the evidence underpinning CRT discussed and the difficulties in defining response outlined. There are 2 main research themes in this body of work, measuring and predicting response to CRT. In the former, the role of patient specific three-­‐dimensional computational models and biophysical properties are investigated, and, in the latter, the influence of CRT on the heart failure syndrome using biomarkers. It is concluded that CRT response can be predicted using patient specific computational models of the left ventricle, but they are too complex for routine clinical use. Biophysical markers have more merit in the immediate future, being simper and quicker, with measures of endothelial and skeletal muscle function, demonstrating promise in a small cohort of patients. Finally, there exists a significant level of undiagnosed pathology in this patient group, such as hyperuricaemia and hyperparathyroidism, but it remains unclear what impact CRT has on this comorbidity