Dataset Information

Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

ABSTRACT: BACKGROUND:Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting. MATERIALS AND METHODS:For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed with chronic kidney disease and were receiving MHD for at least 3 months. Predictors for the model included weight, sex, age, C-reactive protein (CRP), glycosylated hemoglobin, and serum creatinine. The outcome variable was measured resting energy expenditure (mREE). Regression modeling was used to generate predictive formulas and Bland-Altman analyses to evaluate accuracy. RESULTS:The majority were male (60.3%), black (81.0%), and non-Hispanic (76.7%), and 23% were ?65 years old. After screening for multicollinearity, the best predictive model of mREE (R2 = 0.67) included weight, age, sex, and CRP. Two alternative models with acceptable predictability (R2 = 0.66) were derived with glycosylated hemoglobin or serum creatinine. Based on Bland-Altman analyses, the maintenance hemodialysis equation that included CRP had the best precision, with the highest proportion of participants' predicted energy expenditure classified as accurate (61.2%) and with the lowest number of individuals with underestimation or overestimation. CONCLUSIONS:This study confirms disease-specific factors as key determinants of mREE in patients on MHD and provides a preliminary predictive energy equation. Further prospective research is necessary to test the reliability and validity of this equation across diverse populations of patients who are receiving MHD.

SUBMITTER: Byham-Gray LD

PROVIDER: S-EPMC5711615 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

Byham-Gray Laura D LD Parrott J Scott JS Peters Emily N EN Fogerite Susan Gould SG Hand Rosa K RK Ahrens Sean S Marcus Andrea Fleisch AF Fiutem Justin J JJ

JPEN. Journal of parenteral and enteral nutrition 20171219 3

<h4>Background</h4>Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting.<h4>Materials and methods</h4>For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed w ...[more]

PMID: 29187037

Similar Datasets

Project description:BackgroundCardiac valve calcification (CVC) is associated with adverse cardiovascular events. We studied the risk factors of CVC in maintenance hemodialysis (MHD) patients and the value of serum β2-microglobulin (β2-MG) levels in predicting the incidence of CVC. β2-MG is a middle molecular weight toxin. In recent years, researchers found that elevated blood β2-MG was associated with coronary, thoracic, and abdominal aortic calcifications with significant correlations. β2-MG has been emerging as a strong biomarker for cardiovascular mortality in uremic patients but its role in CVC is not well studied. This study looked specifically at CVC occurrence in relation to β2-MG for MHD patients.MethodsPatients who underwent MHD for more than 3 months in the First People's Hospital of Nantong City from November 2012 to November 2019 with complete available data were included in the study. The patients were divided into the CVC group and the non-CVC group. The general information and clinical laboratory indicators of the patients were collected in a retrospective manner. We analyzed the risk factors for developing CVC in MHD patients using binary logistic regression method. Receiver operating characteristic (ROC) curves were used to calculate the cut-off value of β2-MG for predicting CVC. The decision tree (DT) method was used to classify and explore the probability of CVC in patients with MHD.ResultsThe β2-MG in the CVC group was significantly higher than that in the non-CVC group (t=6.750, P<0.001). Multivariate binary logistic regression analysis showed that gender, age, serum β2-MG, and hemodialysis (HD) adequacy (Kt/V urea) were independent risk factors for CVC in MHD patients. ROC analysis showed that a β2-MG value of 25 µg/L was the best cut-off point for predicting CVC in MHD patients. According to binary logistic regression analysis, the β2-MG ≥25 µg/L group was 3.39 times more likely to develop CVC than the β2-MG <25 µg/L group [odds ratio (OR), 3.39; 95% confidence interval (CI), 1.63-7.06; P=0.001]. The DT model determined that serum β2-MG ≥25 µg/L and age >69 years were important determinants for predicting CVC in MHD patients.ConclusionsSerum β2-MG in MHD patients has a positive correlation with the severity and occurrence of CVC.

Dataset Information

Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

Publications

Modeling a Predictive Energy Equation Specific for Maintenance Hemodialysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets