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，但诊断为5期慢性肾脏病（CKD）并接受维持性血液透析（MHD）的患者的临床结局仍不理想。在美国，20%接受透析的慢性肾病（CKD）患者将在年底前死亡。这是一个重要的问题，因为美国有超过50万人受到影响，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的一个独立风险因素。肾功能的下降导致食欲和口服摄入量的自发下降，伴随着营养和能量代谢的生理变化。最佳的营养护理需要一个适当的预测能量方程，基于对临床因素，机制和能量消耗（EE）的准确测定的理解。目前对该患者人群没有这样的理解或等式。因此，这项拟议的研究旨在表征相关参数，有助于理解这种重要的临床状况，并生成专门用于CKD患者的预测能量方程，从而改善临床实践并降低患者发病率和死亡率。我们提出的研究的目标是：1）确定哪些临床因素可预测接受MHD治疗的5期CKD患者的EE; 2）开发并验证一个预测能量方程，该方程包含接受MHD治疗的5期CKD患者中测量的所有影响临床因素;以及3）为了进一步测试临床实用性，我们将比较新开发和验证的预测能量方程，几个公式经常适用于这个群体。我们将通过一项为期2年的多中心研究来实现这些目标，目前的研究将包括美国东北部地区的三家著名研究和医疗保健机构。涉及多个研究中心可确保在项目的各个方面具有适当的人口统计学和临床代表性以及内容专业知识。每个研究中心将计划招募约75名不同种族和民族的参与者，代表地理区域和更大的MHD患者人群（1）。预计招募将分两个阶段进行：1）开发阶段（即，预测能量方程的开发和2）验证阶段（即，预测能量方程的验证）。根据资助提案中稍后报告的功率估计，预计约75名参与者将被纳入第1阶段：开发，其余150名参与者将被纳入第2阶段：验证。一旦入组，受试者将使用代谢推车测量其EE。参与者还将测量他们的身体成分以及关键的实验室参数（例如，iPTH、CRP、A1 C），以确定影响EE的因素（如有）。

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.