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.
The survival rates for patients diagnosed with kidney disease on dialysis for life maintenance are similar to individuals diagnosed with lung cancer;and they have not greatly improved despite advances in medicine and technology. Partly responsible for the poor outcomes is the malnourished condition often experienced by patients on dialysis. The malnutrition results from a high metabolic rate coupled with a poor appetite. Quality nutrition care for dialysis patients can only be provided when practitioners have appropriate methods to evaluate the metabolic rate accurately. This proposed study will plan to develop a formula that will precisely determine the number of calories a dialysis patient requires in order to thwart further compromise.
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|Byham-Gray, Laura; Parrott, J Scott; Ho, Wai Yin et al. (2014) Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study. J Ren Nutr 24:32-41|