首页> 外文会议>ASME annual dynamic systems and control conference >DESIGNING ROBUST ULTRA FILTRATION RATE PROFILES BASED ON IDENTIFYING FLUID VOLUME MODEL PARAMETERS DURING HEMODIALYSIS
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DESIGNING ROBUST ULTRA FILTRATION RATE PROFILES BASED ON IDENTIFYING FLUID VOLUME MODEL PARAMETERS DURING HEMODIALYSIS

机译:基于热解过程中流体体积模型参数的确定性强超滤速率分布图

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Chronic dialysis is a necessary treatment for end-stage kidney disease (ESKD) patients in order to increase life span, with hemodialysis (HD) being the dominant modality. Despite significant advances in HD technology, only half of ESKD patients treated with this modality survive more than 3 years. Fluid management remains one of the most challenging aspects of HD care, with serious implications for morbidity and mortality. Ultrafil-tration has been associated with intradialytic hypotension, also associated with adverse outcomes. Therefore, removing a specified fluid volume to achieve an adequate balance without negative outcomes remains a critical challenge to improving patient outcomes. Therefore, it has been suggested that in addition to blood pressure information, routine HD treatments should include blood volume monitoring. Sensors integrated in dialysis machines are able to track the concentration of various blood components, such as hematocrit, with high accuracy and resolution and to derive a relative blood volume (RBV) changes. In this paper, we propose a novel algorithm to design an optimal, robust ultrafiltration rate profile based on identifying the parameters of a fluid volume model of an individual patient during HD and RBV sensor. Our design achieves, if exists, an optimal ultrafiltration profile for the identified nominal model under maximum ultrafiltration and hematocrit constraints, and guarantees that these constraints are satisfied over a pre-defined set of parameter uncertainty. We demonstrate the performance of our algorithm through a combination of clinical data and simulations.
机译:慢性透析是终末期肾脏病(ESKD)患者的必需治疗,以延长寿命,其中血液透析(HD)是主要方式。尽管高清技术取得了重大进步,但只有一半的ESKD患者通过这种方式治疗后可以存活3年以上。液体管理仍然是高清护理中最具挑战性的方面之一,对发病率和死亡率具有严重影响。超滤与透析内低血压相关,也与不良结局相关。因此,去除指定的体液量以达到足够的平衡而又不会产生不良结果,仍然是改善患者预后的关键挑战。因此,已经建议,除了血压信息外,常规的HD治疗还应包括血容量监测。集成在透析机中的传感器能​​够以高精度和分辨率跟踪各种血液成分(例如血细胞比容)的浓度,并得出相对血容量(RBV)的变化。在本文中,我们提出了一种新颖的算法,可基于识别HD和RBV传感器中每个患者的体液模型的参数来设计最佳,鲁棒的超滤速率曲线。如果存在,我们的设计可以在最大超滤和血细胞比容约束下,为所识别的标称模型实现最佳的超滤特性,并确保在预定义的参数不确定性条件下满足这些约束。我们通过结合临床数据和模拟来证明我们算法的性能。

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