Modeling and control of hemoglobin response for anemia management

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Anemia is a state characterized by a reduced mass of red blood cells (RBCs) or hemoglobin (Hgb), resulting in reduced oxygencarrying capacity and delivery to the body's tissues and organs. Anemia of chronic renal disease patients is primarily due to insufficient endogenous production of erythropoietin, as well as iron deficiency. Those patients with anemia are treated by the administration of recombinant human erythropoietin (rHuEPO) and iron. It has been recognized by the clinical community that while low Hgb level leads to anemia, too large Hgb can increase the risk of mortality for the patient. Hence, effective methods are needed to determine the appropriate dose of rHuEPO and iron to maintain the target Hgb level. Many conventional methods for guiding rHuEPO and iron dosing used by clinicians rely on a set of rules based on past experiences or retrospective studies. Those methods for rHuEPO administration are generally imprecise, and as a result, the patient's Hgb level may move through the target range with big oscillations and overshoots. Effective anemia management method is needed to avoid the adverse effects associated with increased Hgb levels while minimizing the effects of anemia. In this project, we work with Cybernius Medical Ltd to develop algorithms that can be automated for efficient anemia management. Specifically, we will develop (1) modeling methods that can give us reliable predictions of hemoglobin over a relatively long (e.g., two-month) period, (2) robust automated control algorithms that can be used to maintain hemoglobin at desired range, and (3) a simulator that can be used to evaluate the modeling and control performance before its clinical tests. The large amount of renal disease patients (estimated 2.6 million in Canada) suffering from anemia will benefit from the research with reduced risk of high hemoglobin level variability.