Modeling and Control of Hemoglobin for Anemia Management in Chronic Kidney Disease

  • Author / Creator
    McAllister, Jayson C. W.
  • Chronic Kidney Disease (CKD) affects millions of people throughout the world today. One of the major side effects of this disease is the inability to regulate the body’s red blood cell production, and subsequently the mass of the protein called hemoglobin within the body. The health of these patients deteriorates and they become anemic. Recently, erythropoietin stimulating agents have become the standard for treating anemia in chronic kidney disease. The medication works extremely well for what it is designed to do. The problem with this scenario is the inability of the physician’s to be able to choose an appropriate dose for each patient. The dosing protocols are not standardized across hospitals, and many of the dosing regimens are poorly designed. As such, many patients’ hemoglobin levels are poorly controlled. The poor control of hemoglobin in CKD patients is well documented through peer reviewed research. The focus of this thesis is to present an individualized epoetin-alfa dosing regimen, through the use of well known model predictive control technologies. Due to the absence of a proper setpoint, zone model predictive control becomes the focus of the controller methods. The foundation of any model predictive controller is the system model. This thesis presents several different hemoglobin response modeling techniques including classical ARX, pharmacokinetic and pharmacodynamic (PKPD) delayed differential equation modeling and a novel new nonlinear constrained ARX modeling (C-ARX) method. The hemoglobin response modeling methods are compared on a clinical data set of 167 patients. It will be shown that the new modeling method offers similar modeling results to the previously developed PKPD model, with the added benefit of being linear and easily estimated through nonlinear programming. The nonlinear C-ARX method is also converted to a weighted linear C-ARX, which improves the robustness of estimation even further, without a large loss in estimation performance. ii Different model predictive controllers were tested against the current anemia management protocol (AMP) from a participating hospital. The first set of tests were performed using the identified models as the simulated patient and represent a more nominal case for controller testing. Using these results, some of the controllers were eliminated from further testing. The second set of simulations were performed on a patient simulator that was designed based on the PKPD models. The simulator uses random integrating process noise to represent a slowly changing dose over time. The designed simulator also incorporates random step and ramp disturbances to simulate blood loss, infections and other acute anomalies observed in the clinical data. The remaining controller types were tested on the designed patient simulator and represent a realistic and rigorous test scenario for the modeling and control methods. The final controller recommended for use is a weighted recursive least squares zone model predictive controller that uses a funnel shaped control zone.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Chemical and Materials Engineering
  • Specialization
    • Process Control
  • Supervisor / co-supervisor and their department(s)
    • Liu, Jinfeng (Chemical and Materials Engineering)
    • Zukui, Li (Chemical and Materials Engineering)
  • Examining committee members and their departments
    • Dubljevic, Stevan (Chemical and Materials Engineering)
    • Liu, Jinfeng (Chemical and Materials Engineering)
    • Li, Zukui (Chemical and Materials Engineering)