Closed-loop drug infusion for control of heart-rate trajectory in pharmacological stress tests

Intelligent automated drug administration and therapy: future of healthcare

In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will have promising opportunities, vast applications, and some challenges. Due to advancements in processing speed, the closed-loop administration of drugs has gained popularity for critically ill patients in intensive care units and routine life such as personalized drug delivery or implantable therapeutic devices. For developing a closed-loop drug delivery system, the control system works with a group of technologies like sensors, micromachining, wireless technologies, and pharmaceuticals. Recently, the integration of artificial intelligence techniques such as fuzzy logic, neural network, and reinforcement learning with the closed-loop drug delivery systems has brought their applications closer to fully intelligent automatic healthcare systems. This review's main objectives are to discuss the current developments, possibilities, and future visions in closed-loop drug delivery systems, for providing treatment to patients suffering from chronic diseases. It summarizes the present insight of closed-loop drug delivery/therapy for diabetes, gastrointestinal tract disease, cancer, anesthesia administration, cardiac ailments, and neurological disorders, from a perspective to show the research in the area of control theory.

Closed-loop hemodynamic management

As the operating room and intensive care settings become increasingly complex, the required vigilance practitioners must dedicate to a wide array of clinical systems has increased concordantly. The resulting shortage of available attention to these various clinical tasks creates a vacuum for the introduction of systems that can administer well-established goal-directed therapies without significant provider feedback. Recently, there has been an explosion of academic exploration into creating such automated systems, with a strong specific focus on hemodynamic control. Within this field, the largest focus has been on goal-directed fluid therapy as systems automating vasopressor administration have only recently become viable options. Our goal in this review article is to summarize the validity of the relevant goal-directed hemodynamic systems and explore the expanding role of automation within these systems.

Multiscale modeling of cardiac cellular energetics

Multiscale modeling is essential to integrating knowledge of human physiology starting from genomics, molecular biology, and the environment through the levels of cells, tissues, and organs all the way to integrated systems behavior. The lowest levels concern biophysical and biochemical events. The higher levels of organization in tissues, organs, and organism are complex, representing the dynamically varying behavior of billions of cells interacting together. Models integrating cellular events into tissue and organ behavior are forced to resort to simplifications to minimize computational complexity, thus reducing the model's ability to respond correctly to dynamic changes in external conditions. Adjustments at protein and gene regulatory levels shortchange the simplified higher-level representations. Our cell primitive is composed of a set of subcellular modules, each defining an intracellular function (action potential, tricarboxylic acid cycle, oxidative phosphorylation, glycolysis, calcium cycling, contraction, etc.), composing what we call the "eternal cell," which assumes that there is neither proteolysis nor protein synthesis. Within the modules are elements describing each particular component (i.e., enzymatic reactions of assorted types, transporters, ionic channels, binding sites, etc.). Cell subregions are stirred tanks, linked by diffusional or transporter-mediated exchange. The modeling uses ordinary differential equations rather than stochastic or partial differential equations. This basic model is regarded as a primitive upon which to build models encompassing gene regulation, signaling, and long-term adaptations in structure and function. During simulation, simpler forms of the model are used, when possible, to reduce computation. However, when this results in error, the more complex and detailed modules and elements need to be employed to improve model realism. The processes of error recognition and of mapping between different levels of model form complexity are challenging but are essential for successful modeling of large-scale systems in reasonable time. Currently there is to this end no established methodology from computational sciences.

A Closed-Loop Electrical Stimulation System for Cardiac Cell Cultures

An integrated electrical stimulation and recording system was designed for closed-loop control and analysis of cardiac cultures on planar microelectrode arrays. Stimulated action potentials from HL-1 clonal myocyte cultures were digitized, stimulation artifacts were removed using nulling and filtering methods, and analysis was performed to determine stimulation efficacy in real time. Results of this analysis were used to determine future stimulation waveform parameters such as polarity, amplitude, pulse duration, and rate or pattern. Algorithms were designed utilizing real-time analysis and control to maintain a desired electrophysiological response of the culture, such as an arbitrary capture fraction value. This paper presents the hardware and software design of the stimulus pulse circuitry, artifact extraction, analysis, and control components of the system. Applications of this technology include the study of cardiac cell physiology, improving the speed and accuracy of traditional open-loop stimulation protocols, pharmacological screening, and improving the performance of biosensors based on sensing electrical activity in cardiac cultures.

Very low-frequency heart rate variability wave amplitude and sympathetic stimulation--characterization and modeling

The purpose of this study was to assess the relationship between very low-frequency heart rate variability (LFHR) wave amplitude and the degree of sympathetic stimulation. We developed a computerized system for the controlled increase of heart rate (HR) by isoproterenol (ISP), with which we obtained a series of stabilized HR levels in conscious freely moving rats. We found that LFHR amplitude rises gradually as a function of the average HR for each level until it reaches a point where additional increases in average HR are associated with gradual decrease in LFHR amplitude. We successfully built and fitted a model of LFHR amplitude to the experimental results. The fact that our model fits the experimental data well may suggest a possible relationship between our LFHR amplitude findings and the basic physiologic properties of the HR-ISP system inherent in our model.

Computer-controlled heart rate increase by isoproterenol infusion: mathematical modeling of the system

The purpose of this study was mathematical modeling of the heart rate (HR) response to isoproterenol (Iso) infusion. We developed a computerized system for the controlled increase of HR by Iso, based on a modified proportional-integral controller. HR was measured in conscious, freely moving rats. We found that the steady-state HR can be described as a hyperbolic power function of the steady-state Iso flow rate. This dependence was coupled with a first-order difference equation to form a pharmacodynamic model that reliably describes the relationship between HR and Iso flow for any arbitrary form of Iso flow function. In simulation studies, we showed that the model continued to follow the HR curve from real-time experiments far beyond the initial "learning interval" from which its parameters were calculated. Our results suggest that the predictive ability and the simplicity of calculating the parameters render this pharmacodynamic model appropriate for use within future advanced, model-based, adaptive control systems and as a part of larger cardiovascular models.