Development and Validation of a Predictive Energy Equation in Hemodialysis

描述(由申请人提供)：描述(由申请人提供)：尽管医学和技术取得了进步，但被诊断为慢性肾脏病(CKD)5期患者进行维持性血液透析(MHD)的临床结果仍然不佳。在美国，所有接受透析的慢性肾脏病(CKD)患者中，有20%将在年底前死亡。这是一个重要的问题，因为美国有50多万人受到影响，人类痛苦和损失的成本很高，医疗保险和私人保险的费用也很高。造成这一高死亡率的原因是多因素和相互依存的，但蛋白质-能量营养不良正在成为这一人群中的一个独立风险因素。肾功能的下降会导致食欲和口服摄入量的自发下降，并伴随着营养和能量代谢的生理变化。最佳营养护理需要一个适当的预测能量方程式，基于对临床因素、机制的了解，并准确确定能量消耗(EE)。目前，对于这一患者群体还没有这样的理解或方程式。因此，这项拟议的研究旨在描述相关参数，有助于了解这一重要的临床状况，并生成专门用于CKD患者的预测性能量方程，从而改善临床实践，降低患者的发病率和死亡率。我们建议进行的研究的目标是：1)确定哪些临床因素可以预测MHD的5期CKD患者的EE；2)开发和验证一个预测能量方程，其中包含了在MHD的5期CKD患者中测量的所有影响临床因素；3)为了进一步测试临床实用性，我们将把新开发和验证的预测能量方程与几个经常应用于这一人群的公式进行比较。我们将通过为期两年的多地点研究来实现这些目标，美国东北部地区的三个著名研究和医疗保健机构将包括在当前的研究中。涉及多个站点确保了项目各个方面的适当人口统计和临床表现以及内容方面的专业知识。每个研究地点将计划招募大约75名不同种族和民族的参与者，他们代表了地理区域和更大的MHD患者群体(1)。预计登记将分两个阶段进行：1)开发阶段(即开发预测能量方程)和2)验证阶段(即验证预测能量方程)。根据稍后在赠款提案中报告的功率估计，预计将有大约75名参与者参加第一阶段：开发，其余150名参与者将参加第二阶段：验证。一旦登记，参与者将使用代谢车测量他们的EE。参与者还将测量他们的身体成分，并绘制关键的实验室参数(例如iPTH、CRP、A1C)，以确定是否有影响EE的因素。

DESCRIPTION (provided by applicant): DESCRIPTION (provided by applicant): Despite advances in medicine and technology, clinical outcomes among patients diagnosed with stage 5 chronic kidney disease (CKD) on maintenance hemodialysis (MHD) have remained suboptimal. In the United States, 20% of all chronic kidney disease (CKD) patients receiving dialysis will die by years' end. This is an important problem, given the over half million individuals in US who are affected, the high cost in human suffering and loss, and the expense to Medicare and private insurance. Causes for this high mortality rate are multifactorial and interdependent, but protein-energy malnutrition is emerging as an independent risk factor in this population. The decline in kidney function leads to a spontaneous decline in appetite and oral intake, coupled with physiological alterations in nutrient and energy metabolism. Optimal nutritional care requires an appropriate predictive energy equation, based on an understanding of clinical factors, mechanisms, and accurate determination of energy expenditure (EE). Currently no such understanding or equation exists for this patient population. Therefore, this proposed study seeks to characterize the relevant parameters, contribute to the understanding of this significant clinical condition, and generate a predictive energy equation specifically for use in patients with CKD, thereby improving clinical practice and reducing patient morbidity and mortality. The goals of our proposed study are 1) to determine what clinical factors predict EE in patients diagnosed with stage 5 CKD on MHD; 2) to develop and validate a predictive energy equation that incorporates all of the influencing clinical factors measured in stage 5 CKD patients on MHD; and 3) to further test the clinical utility, we will compare the newly developed and validated predictive energy equation to several formulas often applied to this population. We will accomplish these aims using a 2-year, multi-site, Three prominent research and health care institutions within the Northeastern region of the United States will be included in the current study. Involving multiple sites assures appropriate demographic and clinical representation and content expertise in all aspects of the project. Each research site will plan to enroll approximately 75 participants of diverse races and ethnicities, representative of the geographic regions and the larger population of MHD patients (1). It is anticipated that enrollment will occur in a two-phase process: 1) Development Phase (i.e., development of the predictive energy equation and 2) Validation Phase (i.e., validation of the predictive energy equation). As per power estimations reported later in the grant proposal, it is anticipated that approximately 75 participants will be included in Phase 1: Development and the remaining 150 participants will be enrolled for Phase 2: Validation. Once enrolled, the participants will have their EE measured using a metabolic cart. The participants will also have their body composition measured as well as key laboratory parameters drawn (e.g., iPTH, CRP, A1C) in order to determine what, if any, factors impact EE.