AUTOMATED MEDICATION INFUSION SYSTEM DESIGN

Automated infusion of medications will be increasingly deployed in patient care as a means to deliver high-quality and continuous monitoring and therapy, and also to alleviate the excessive workload imposed on the clinicians. Therefore, a well-designed automated medication infusion system is an attractive alternative to today’s manual treatment requiring caregiver’s interventions. However, it also presents numerous challenges: 1) Significant inter- and intra-patient variability; 2) Complexity of medication infusion model; 3) Complexity of interaction of multiple medications; 4) Difficulty in coordination of medical targets. So the following approaches are proposed to address the various challenges: First, to deal with the large degree of individual patient variability, an adaptive controller was designed. This is because robust controllers which have fixed parameters might be difficult to offer decent behavior for all patients. Secondly, since classical adaptive controllers can only be applied to linearly parametrized models while even the infusion model of single drug is highly nonlinear and complex, a single-input single-output (SISO) semi-adaptive control approach which only adapt can adapt model parameters having a large impact on the model’s fidelity was introduced. Thirdly, the complicated interaction of multiple medications makes the adaptive controller for two medications even more difficult to design. So a model for two interacting dose responses was constructed and linearized at one operation point. Then the SISO semi-adaptive controller was extended to a two-input two-output case. However, this controller is only designed at one operating point. Therefore, based on two models associated with two distinct operating regimes, a two-model switching control technique was developed and combined with the semi-adaptive controller. Fourthly, we presented a coordinate mechanism to deal with the medical targets setting problem. In real clinical scenarios, the reference targets are empirically specified by caregivers, which are not always achievable in all patients. Therefore, our proposed coordinate mechanism can recursively adjusts the reference targets based on the estimated dose-response relationship of a patient. Lastly, we conducted some SISO control experiments on animals. Based on the experiments, we made some further improvements to the proposed controller

Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy

Resiliency Against Stressful Life Events and the Progression of Depression from Adolescence

Major depression is the second most impactful condition on overall health in the United States, affecting approximately 9 million commercially insured Americans. Depression rates are increasing as time goes on, especially in adolescents (12-17). From 2013, the rates of teen depression has risen 63 percent (47 percent for boys and 65 percent for girls). Depression is a complicated condition and there are many factors that play into the progression of the disease, including environmental, genetic, and psychological factors. Adolescence is a developmentally critical period of brain development for plasticity and maturation of stress systems. Studies have suggested that there may be developmental periods in which exposure to stressful life events (SLEs) cause cell sensitivity to epigenetic changes that affect gene and protein expression long term. Thus, these changes may render adolescents more vulnerable to psychiatric disorders. Variations of certain genes may allow certain people to be more susceptible to psychiatric disorders when exposed to an early SLE than others. However, there are studies that have demonstrated that children who have had SLE have no particular mental health problems in adulthood. These individuals would be considered “resilient”. Resilience is the brain’s capacity to cope with environmental stress and achieve stable psychological functioning in response to prolonged stress. Understanding the numerous neuronal mechanism in different parts of the brain is pertinent, in that it may lead to the susceptibility of depression. A literature review was done on stress’s effects in different regions of the brain and how it may contribute to the progression of depression later into adulthood

Pharmacometric modelling of haemodynamics in humans

Current pharmacokinetic-pharmacodynamic models describing changes to the haemodynamic system often do not include necessary feedback mechanisms. These models often provide an adequate empirical description of data but may fail to adequately extrapolate to additional scenarios. This study aimed to develop a minimal model to describe the short-term changes of haemodynamics that can be used as the basis for future model development. The model was used to describe the haemodynamic effects of sodium nitroprusside (SNP) in adolescents undergoing surgery. A minimal haemodynamic model was developed to describe the influence of drugs on blood pressure components. The model structure was defined based on known mechanisms and previously published models. The model parameters were calibrated to describe (without estimation) the heamodynamics of two antihypertensive drugs with data extracted from the literature. Structural identifiability analysis was done using various combinations of the observed variables. The model was applied to clinical data from patients receiving SNP infusion. The model was extended to accommodate the postulated mechanism of action of SNP in the literature. The proposed model structure included mean arterial pressure (MAP), heart rate (HR), and stroke volume and was composed of four states described by differential equations. The model evaluation showed flexibility in describing haemodyanmics at different target perturbations. Overlay plots of model predictions and literature data showed a good description without data fitting. The structural identifiability analysis revealed all model parameters and initial conditions were identifiable only when HR, MAP, and cardiac output were measured together. In addition, model evaluation using SNP data suggested its mechanistic plausibility and the flexibility to describe various response patterns elicited by SNP A minimal model of the haemodynamic system was developed and evaluated. The model accounted for short-term haemodynamic feedback processes. We propose that this model can be used as the basis for future pharmacometric analyses of drugs acting on the haemodynamic system

Regulation of Alloreactive CD8 T Cell Responses by Costimulation and Inflammation

CD8 T lymphocytes are a crucial component of the adaptive immune system and mediate control of infections and malignancy, but also autoimmunity and allograft rejection. Given their central role in the immune system, CD8 T cell responses are tightly regulated by costimulatory signals and cytokines. Strategies targeting signals that are critical for T cell activation have been employed in a transplantation setting to impede alloreactive T cell responses and prevent graft rejection. The goal of my thesis is to understand how costimulatory signals and inflammation regulate alloreactive CD8 T cell responses and how to target these pathways to develop more effective tools to prevent graft rejection. Costimulation blockade is an effective approach to prolong allograft survival in murine and non-human primate models of transplantation and is an attractive alternative to immunosuppressants. I describe a novel murine anti-CD40 monoclonal antibody that prolongs skin allograft survival across major histocompatibility barriers and attenuates alloreactive CD8 T cell responses. I find that the pro-apoptotic proteins Fas and Bim function concurrently to regulate peripheral tolerance induction to allografts. Activation of the innate immune system by endogenous moIecules released during surgery or infections in transplant recipients can modulate T cell responses. However, the direct impact of inflammation on alloreactive CD8 T cell responses is not clear. Using a T cell receptor (TCR) transgenic mouse modeI, I demonstrate that inflammatory stimuli bacterial lipopolysaccharide (LPS) and the viral dsRNA mimetic poly(I:C) differentially regulate donor-reactive CD8 T cell responses by generating distinct cytokine milieus. Finally I demonstrate the role of pro-inflammatory cytokines stem cell factor (SCF), granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin-3 (IL-3) in improving human B cell development in humanized NOD-scid IL2Rγnull (NSG) mice

Model-Based Analysis of User Behaviors in Medical Cyber-Physical Systems

Human operators play a critical role in various Cyber-Physical System (CPS) domains, for example, transportation, smart living, robotics, and medicine. The rapid advancement of automation technology is driving a trend towards deep human-automation cooperation in many safety-critical applications, making it important to explicitly consider user behaviors throughout the system development cycle. While past research has generated extensive knowledge and techniques for analyzing human-automation interaction, in many emerging applications, it remains an open challenge to develop quantitative models of user behaviors that can be directly incorporated into the system-level analysis. This dissertation describes methods for modeling different types of user behaviors in medical CPS and integrating the behavioral models into system analysis. We make three main contributions. First, we design a model-based analysis framework to evaluate, improve, and formally verify the robustness of generic (i.e., non-personalized) user behaviors that are typically driven by rule-based clinical protocols. We conceptualize a data-driven technique to predict safety-critical events at run-time in the presence of possible time-varying process disturbances. Second, we develop a methodology to systematically identify behavior variables and functional relationships in healthcare applications. We build personalized behavior models and analyze population-level behavioral patterns. Third, we propose a sequential decision filtering technique by leveraging a generic parameter-invariant test to validate behavior information that may be measured through unreliable channels, which is a practical challenge in many human-in-the-loop applications. A unique strength of this validation technique is that it achieves high inter-subject consistency despite uncertain parametric variances in the physiological processes, without needing any individual-level tuning. We validate the proposed approaches by applying them to several case studies

Multiple Sclerosis

Multiple sclerosis (MS) offers important challenges to understanding its complex genetics and molecular and immunological mechanisms, which eventually lead to relapsing and progressive clinical forms and a constellation of clinical manifestations. Along with the progress in knowledge, disease-modifying treatments and new therapeutic molecules have made an impact on the prognosis of the disease. Essential in the diagnostic identification process of MS is the differentiation of this major demyelinating disease with other inflammatory CNS disorders, including Neuromyelitis Optica and MOG antibody disease. MS is more prevalent in women; therefore, pregnancy and post-partum hormonal and immunological changes typically affect the clinical behavior of the disease. This Special Issue of Biomedicines addresses recent advances in the mechanistic genetic and immunological processes of MS, opening more options to future studies and to the consideration of further therapeutic possibilities. The issue discusses the application of modern therapies, including monoclonal antibodies, some still in the process of complete development, and the current strategies managing progressive MS. The aim of this issue is to stimulate basic and clinical research and promote observations on the ever-expanding and complex field of MS. Advancement in the understanding of the mechanisms and the clinical characterizations of this disease should result in improved therapeutic outcomes that reduce neurological and cognitive disability commonly associated with progressive disease

Navigating the Patient Room: Critical Care Nurses' Interaction with the Designed Physical Environment

abstract: The physical environment influences the physiology, psychology, and the societal interactions of those who experience it. The environment can also influence human behavior. Critical care nurses are in constant interaction with the physical environment surrounding their patients. High acuity ICU patients are vulnerable and at risk for harm, infection, and poor outcomes while the physical and cognitive workload of nurses presents a demanding and continuous challenge. The goal of this qualitative study was to explore and understand the way critical care nurses navigate within the patient room and interact with its features. The study of critical care nurses interacting with the patient room environment was conducted in five critical care units at three tertiary care institutions in the Eastern United States, along with another unit in the pilot study at a community hospital in the Southwest United States. Nurses were observed in their typical work environment as they performed normal tasks and patient care activities for entire day and night shifts. The study involved ethnographic field observations, individual semi-structured participant interviews, and examination of photographs and floor plans. The exploratory study resulted in a comprehensive model for nurse navigation that includes both cognitive and action components, along with a conceptual framework for nurse behavioral activity. Repetitive patterns of nurse movement were identified and named. The findings produced recommendations for nurses’ effective use of space and architectural design of ICU patient rooms to improve patient outcomes.Dissertation/ThesisDoctoral Dissertation Nursing and Healthcare Innovation 201