Doctor of Philosophy

dissertationThe task of comparing and evaluating the performance of different computer-based clinical protocols is difficult and expensive to accomplish. This dissertation explores methods to compare and evaluate computer-based insulin infusion protocols based on an in silico analytical framework iteratively developed for this study, using data from the intensive care unit (ICU). In Methods for Aim 1, we used a pairwise comparative technique to evaluate two computer-based insulin infusion protocols. Our result showed that the pairwise method can rapidly identify a promising computer-based clinical protocol but with limitations. In Methods for Aim 2, we used a ranking strategy to evaluate six computer-based insulin infusion protocols. The ranking method enabled us to overcome a key limitation in Methods for Aim 1, making it possible to compare multiple computer-based clinical protocols simultaneously. In Methods for Aim 3, we developed a more comprehensive in silico method based on multiple-criteria decision analysis that included user-defined performance evaluation criteria examining different facets of the computer-based insulin infusion protocols. The in silico method appears to be an efficient way for identifying promising computer-based clinical protocols suitable for clinical evaluation. We discuss the advantages and disadvantages for each of the presented methods. We also discuss future research work and the generalizability of the framework to other potential clinical areas

Next-generation, personalised, model-based critical care medicine : a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

© 2018 The Author(s). Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care

Mathematical Modeling in Systems Medicine: New Paradigms for Glucose Control in Critical Care

Stress hyperglycemia occurs frequently in critical care patients and many of the harmful repercussions may be mitigated by maintaining glucose within a ``healthy'' zone. While the exact range of the zone varies, glucose below 80 $mg/dl$ or above 130 $mg/dl$ increases risk of mortality. Zone glucose control (ZGC) is accomplished primarily using insulin administration to reduce hyperglycemia. Alternatively, we propose also allowing glucose administration to be used to raise blood glucose and avoid hypoglycemia. While there have been attempts to create improved paradigms for treatment of stress hyperglycemia, inconsistencies in glycemic control protocols as well as variation in outcomes for different ICU subpopulations has contributed to the mixed success of glucose control in critical care and subsequent disagreement regarding treatment protocols. Therefore, a more accurate, personalized treatment that is tailored to an individual may significantly improve patient outcome. The most promising method to achieve better control using a personalized strategy is through the use of a model-based decision support system (DSS), wherein a mathematical patient model is coupled with a controller and user interface that provides for semi-automatic control under the supervision of a clinician. Much of the error and subsequent failure to control blood glucose comes from the failure to resolve inter- and intrapatient variations in glucose dynamics following insulin administration. The observed variation arises from the many biologically pathways that affect insulin signaling for patients in the ICU. Mathematical modeling of the biological pathways of stress hyperglycemia can improve understanding and treatment. Trauma and infection lead to the development of systemic insulin resistance and elevated blood glucose levels associated with stress hyperglycemia. We develop mathematical models of the biological signaling pathways driving fluctuations in insulin sensitivity and resistance. Key metabolic mediators from the inflammatory response and counterregulatory response are mathematically represented acting on insulin-mediated effects causing increases or decreases in blood glucose concentration. Data from published human studies are used to calibrate a composite model of glucose and insulin dynamics augmented with biomarkers relevant to critical care. The resulting mathematical description of the underlying mechanisms of insulin resistance could be used in a model-based decision support system to estimate patient-specific metabolic status and provide more accurate insulin treatment and glucose control for critical care patients