Computational Approach to Personalized Anemia Management

个性化贫血管理的计算方法

• 批准号: 8526456
• 负责人: ADAM E GAWEDA
• 金额: $ 52.04万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8526456
• 关键词: Address; Affect; Algorithms; Anemia; Area; Biomedical Computing; Blood Vessels; Cardiovascular system; Caring; Chronic; Chronic Kidney Failure; Clinical; Complex; Complication; Computer Simulation; Computing Methodologies; Data; Dialysis procedure; Dose; Elements; End stage renal failure; Erythropoiesis; Erythropoietin; Exposure to; Goals; Health; Hemodialysis; Hemorrhage; Intestines; Iron; Lead; Machine Learning; Medicare; Medication Management; Medicine; Mission; Modeling; Nature; Outcome; Patient Care; Patients; Pharmaceutical Preparations; Population; Prevention; Protocols documentation; Public Health; Quality of life; Research; Risk; Risk Factors; Simulate; Therapeutic; Therapeutic Agents; United States National Institutes of Health; Validation; Variant; Work; absorption; base; clinically relevant; cost; cost effectiveness; design; efficacy testing; gastrointestinal; improved; mathematical model; mortality; response; theories; vigilance

DESCRIPTION (provided by applicant): Effective anemia management in End-Stage Renal Disease (ESRD) is a complex task due to large variation in erythropoietic response among the ESRD patients. Evidence shows that excessive doses of Erythropoiesis Stimulating Agents (ESA), used in hypo-responsive patients, coincide with increased cardiovascular complications. In addition to health risks, reimbursement restrictions imposed by Medicare demand additional vigilance in ESA administration. Iron is one of the most important elements affecting the erythropoietic response. The ongoing blood losses associated with hemodialysis treatments, increased risk of gastrointestinal bleeding, and access complications, as well as poor iron absorption lead to iron depletion in ESRD patients. Standard anemia management protocols do not fully address the synergistic nature of interactions between erythropoietin and iron. Personalized strategies for coordinated ESA and iron dosing are therefore necessary to achieve the desired clinical outcome, reduce patient risk exposure, and improve the cost-effectiveness of treatment. Our long-term goal is the advancement of biomedical computing to personalize medicine and pharmacologic management of chronic conditions. The objective of this project is to address the challenge of multiple drug dosing personalization with a specific application to ESA and iron management in anemia of ESRD. The central hypothesis is that a computational approach can be developed to perform coordinated personalized dosing of erythropoiesis stimulating agents and iron in order to achieve desired clinical outcomes and minimize patient exposure resulting in optimal care. The rationale behind the proposed research is that computational models quantifying the interaction between iron and erythropoietin can be applied through the principles of control theory and machine learning, to derive personalized dosing strategies for both agents. The main objective will be accomplished through three specific aims: 1) quantification of the erythropoietin-iron interaction through clinically relevant computational models, 2) application of such models to derive algorithms for coordinated personalized ESA and iron dosing, 3) validation of these algorithms through an "in sillico" trial for a wide range o simulated subjects and clinical conditions. The expected outcome of this project is a new systematic approach to the management of ESRD anemia using multiple agents resulting in improved patient care and decreased cost which will be broadly applicable to other therapeutic areas.

描述（由申请人提供）：由于终末期肾病（ESRD）患者的红细胞生成反应存在很大差异，因此有效管理终末期肾病（ESRD）的贫血是一项复杂的任务。有证据表明，在低反应患者中使用过量的红细胞生成刺激剂（ESA）会导致心血管并发症增加。除了健康风险之外，医疗保险施加的报销限制要求 ESA 管理部门提高警惕。铁是影响红细胞生成反应的最重要元素之一。与血液透析治疗相关的持续失血、胃肠道出血风险增加、通路并发症以及铁吸收不良导致 ESRD 患者铁耗竭。标准贫血管理方案并未完全解决促红细胞生成素和铁之间相互作用的协同性质。因此，需要协调 ESA 和铁剂量的个性化策略，以实现预期的临床结果、降低患者风险暴露并提高治疗的成本效益。我们的长期目标是推进生物医学计算，以实现慢性病的个性化医疗和药理学管理。该项目的目标是解决多种药物剂量个性化的挑战，具体应用于ESA和ESRD贫血的铁管理。中心假设是，可以开发一种计算方法来执行红细胞生成刺激剂和铁的协调个性化剂量，以实现所需的临床结果并最大限度地减少患者暴露，从而获得最佳护理。拟议研究背后的基本原理是，可以通过控制理论和机器学习的原理应用量化铁和促红细胞生成素之间相互作用的计算模型，从而得出两种药物的个性化剂量策略。主要目标将通过三个具体目标来实现：1）通过临床相关的计算模型对促红细胞生成素-铁相互作用进行量化，2）应用此类模型来导出协调个性化ESA和铁剂量的算法，3）通过针对广泛的模拟受试者和临床条件的“计算机模拟”试验来验证这些算法。该项目的预期成果是使用多种药物来管理 ESRD 贫血的新系统方法，从而改善患者护理并降低成本，这将广泛适用于其他治疗领域。