Modeling and control of hemoglobin response for anemia management

贫血管理中血红蛋白反应的建模和控制

• 批准号: 490513-2015
• 负责人: Liu, Jinfeng
• 金额: $ 7.89万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=601441
• 关键词: Modeling; control; hemoglobin; response; anemia

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.

贫血是一种状态，其特征在于红细胞（RBC）或血红蛋白（Hgb）的质量减少，导致携氧能力和向身体组织和器官的输送减少。慢性肾病患者的贫血主要是由于内源性红细胞生成素的产生不足以及铁缺乏。贫血患者可以通过给予重组人红细胞生成素（rHuEPO）和铁来治疗。临床界已经认识到，虽然低Hgb水平会导致贫血，但过高的Hgb会增加患者死亡的风险。因此，需要有效的方法来确定合适的剂量的红细胞生成素和铁，以维持目标血红蛋白水平。临床医生使用的许多常规的指导rHuEPO和铁剂量的方法依赖于基于过去经验或回顾性研究的一组规则。用于rHuEPO给药的那些方法通常是不精确的，并且因此，患者的Hgb水平可能以大的振荡和过冲通过目标范围。需要有效的贫血管理方法来避免与Hgb水平增加相关的不良反应，同时最大限度地减少贫血的影响。在这个项目中，我们与Cybernius Medical Ltd合作开发可以自动化的算法，以实现有效的贫血管理。具体来说，我们将开发（1）建模方法，可以为我们提供相对长时间（例如，两个月）期间，（2）可用于将血红蛋白维持在所需范围的鲁棒自动控制算法，以及（3）可用于在其临床测试之前评估建模和控制性能的模拟器。大量患有贫血的肾病患者（估计加拿大有260万）将从研究中受益，降低高血红蛋白水平变异性的风险。