Precision Volume Management During Maintenance Hemodialysis

项目摘要 目前，美国有超过40万人接受维持性血液透析。虽然这件事 治疗是延长生命的，临床结果和生活质量都很差。当前用于去除液体的策略 血液透析是不精确的，在血液透析期间和之后都会发生事件(例如，透析中的低血压) (例如，恢复时间延长)是常见的并发症，与显著的发病率和死亡率有关。 不幸的是，用血流动力学监测的标准方法来预测这些事件是具有挑战性的。全 这突出表明，需要更精确地监测和改进血液透析的液体清除。 连续的红细胞压积监测仪已经被开发出来，以非侵入性的方式监测 血液透析过程中的血管内容量。然而，将这项技术应用于流体的决策 管理层的情况好坏参半。这在很大程度上是因为缺乏数据来表明与Intra-Rate的一致关联。 透析性低血压或改善临床结果。然而，研究主要集中在血浆体积上， 但血容量不足并不是导致低血压的唯一原因。为了充分利用这项技术的潜力， 我们计划将血液透析过程中血浆容量的变化与液体清除率结合起来，以产生 间质组织血管再充盈率的半连续测量--称为血浆再充盈率 作为患者韧性的动态衡量标准。研究血浆再充盈率的模式提供了一个独特的机会 评估患者在血液透析治疗过程中韧性的变化，该治疗尚未得到充分的探索。 此外，一项新技术的临床影响需要了解其集成到 工作流程。因此，对于这项工作，我们将使用两种互补的方法。使用大型数据集，我们将 通过以下方法检查血浆再充盈率与透析中低血压的关系：(1)确定是否低血压 血浆再充盈率与透析中低血压的血液透析次数和 (2)用时间更新变量建立模型来预测透析液的发展时间。 低血压。同时，我们将收集主要数据以(3)评估血浆再充盈之间的关联 速度和恢复时间，以及(4)探讨提供商对该技术可用性的看法。通过这些 并行项目我们将提高我们对血液动力学细微变化的理解，这将为未来提供信息 研究和燃料致力于维持性血液透析的精确容量管理。而且，如果我们 发现血浆再充盈率在预测透析中低血压或恢复时间中的关键作用，我们可以 利用这项技术的半连续特性来改变用户界面并改进护理交付。 结合临床流行病学硕士课程的正式课程，建议的 申请NRSA奖学金将为王博士提供高级 生物统计、统计建模、初级数据收集方面的专门知识，并促进建立 研究透析技术和终末期肾脏疾病的利基。

Project Summary More than 400,000 people currently receive maintenance hemodialysis in the United States. While this therapy is life-extending, clinical outcomes and quality of life are poor. Current strategies to remove fluid with hemodialysis are imprecise, and events both during hemodialysis (e.g., intra-dialytic hypotension) and after (e.g., prolonged recovery time) are common complications associated with significant morbidity and mortality. Unfortunately, these events are challenging to predict with standard methods for hemodynamic monitoring. All of this highlights a need for greater precision in monitoring and improvement in fluid removal on hemodialysis. Continuous hematocrit monitors have been developed to non-invasively monitor relative changes in intravascular volume during a hemodialysis session. Yet, adoption of this technology into decisions on fluid management has been mixed. This is largely due to a lack of data to show a consistent association with intra- dialytic hypotension or improved clinical outcomes. However, studies have largely focused on plasma volume, but hypovolemia is not the only cause of hypotension. In order to leverage the full potential of this technology, we plan to incorporate changes in plasma volume with the rate of fluid removal during hemodialysis to yield a semi-continuous measure of the rate of vascular refilling from the interstitial tissue - termed plasma refill rate - as a dynamic measure of patient resilience. Examining patterns of plasma refill rate offers a unique opportunity to evaluate changes in patient resilience throughout a hemodialysis treatment that has yet to be fully explored. Additionally, the clinical impact of a new technology requires understanding of its ease of integration into the workflow. As such, for this work we will use two complementary approaches. Using a large dataset, we will examine the relationship between plasma refill rate and intra-dialytic hypotension by (1) determining if low plasma refill rates are associated with the number of hemodialysis sessions with intra-dialytic hypotension and (2) to develop a model using time-updated variables to predict the time to development of intra-dialytic hypotension. Concurrently, we will collect primary data to (3) evaluate the association between plasma refill rate and recovery time and (4) explore provider perspectives on the usability of this technology. Through these parallel projects we will improve our understanding of subtle hemodynamic changes that will inform future studies and fuel work towards precision volume management in maintenance hemodialysis. Moreover, if we find a critical role of plasma refill rate in the prediction of intra-dialytic hypotension or recovery time, we can leverage the semi-continuous nature of this technology to alter the user interface and improve delivery of care. In conjunction with formal coursework in a master's program for clinical epidemiology, the proposed application for the NRSA fellowship award will provide Dr. Wang with intensive training in advanced biostatistics, expertise in statistical modeling, primary data collection, and facilitate the establishment of a research niche in dialysis technology and end-stage kidney disease.