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Constructing a prediction model for physiological parameters for malnutrition in hemodialysis patients.

ABSTRACT: A retrospective analysis of the improvement in the health condition of patients undergoing hemodialysis was done to understand the important factors that can affect malnutrition in these patients. In this study, data from patients who underwent hemodialysis between 2010 and 2015 in a regional hospital in Yunlin County were collected from the Taiwan Society of Nephrology-Kidney Transplantation database. A total of 1049 medical records from 300 patients with age over 20 and underwent hemodialysis were collected for this study. A decision tree C5.0 and logistic regression were used to identify 40 independent variables, as well as the association of the dependent variable albumin. Then, the C5.0 decision tree, logistic regression, and support vector machine (SVM) methods were applied to find a combination of factors that contributed to malnutrition in patients undergoing hemodialysis. Predictive models were established. Finally, a receiver operating characteristic curve and confusion matrix was used to evaluate the standard of performance of these models. All analytical methods indicated that "age" was an important factor. In particular, the best predictive model was the SVM-model 4, with a training accuracy rate of 98.95% and test accuracy rate of 66.89%, identified that "age" and 15 other important factors were the most related to hemodialysis. The findings of this study can be used as a reference for clinical applications.

SUBMITTER: Tsai YT

PROVIDER: S-EPMC6656719 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Constructing a prediction model for physiological parameters for malnutrition in hemodialysis patients.

Tsai Yu-Tsung YT Yang Feng-Jung FJ Lin Hong-Mau HM Yeh Jiang-Chou JC Cheng Bor-Wen BW

Scientific reports 20190724 1

A retrospective analysis of the improvement in the health condition of patients undergoing hemodialysis was done to understand the important factors that can affect malnutrition in these patients. In this study, data from patients who underwent hemodialysis between 2010 and 2015 in a regional hospital in Yunlin County were collected from the Taiwan Society of Nephrology-Kidney Transplantation database. A total of 1049 medical records from 300 patients with age over 20 and underwent hemodialysis ...[more]

PMID: 31341234

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Project description:Background and aimProtein-energy wasting (PEW) is critically associated with the reduced quality of life and poor prognosis of hemodialysis patients. However, the diagnosis criteria of PEW are complex, characterized by difficulty in estimating dietary intake and assessing muscle mass loss objectively. We performed a cross-sectional study in hemodialysis patients to propose a novel PEW prediction model.Materials and methodsA total of 380 patients who underwent maintenance hemodialysis were enrolled in this cross-sectional study. The data were analyzed with univariate and multivariable logistic regression to identify influencing factors of PEW. The PEW prediction model was presented as a nomogram by using the results of logistic regression. Furthermore, receiver operating characteristic (ROC) and decision curve analysis (DCA) were used to test the prediction and discrimination ability of the novel model.ResultsBinary logistic regression was used to identify four independent influencing factors, namely, sex (P = 0.03), triglycerides (P = 0.009), vitamin D (P = 0.029), and NT-proBNP (P = 0.029). The nomogram was applied to display the value of each influencing factor contributed to PEW. Then, we built a novel prediction model of PEW (model 3) by combining these four independent variables with part of the International Society of Renal Nutrition and Metabolism (ISRNM) diagnostic criteria including albumin, total cholesterol, and BMI, while the ISRNM diagnostic criteria served as model 1 and model 2. ROC analysis of model 3 showed that the area under the curve was 0.851 (95%CI: 0.799-0.904), and there was no significant difference between model 3 and model 1 or model 2 (all P > 0.05). DCA revealed that the novel prediction model resulted in clinical net benefit as well as the other two models.ConclusionIn this research, we proposed a novel PEW prediction model, which could effectively identify PEW in hemodialysis patients and was more convenient and objective than traditional diagnostic criteria.

Project description:Background & aimsPatients undergoing dialysis are less likely to develop immune responses to SARS-CoV-2 vaccine. Malnutrition is common in the dialysis population. However, whether malnutrition contributes to the impaired immunogenicity remains unknown. The aim of this study was to assess the association between nutritional status and SARS-CoV-2 vaccine response in patients receiving maintenance hemodialysis.MethodsA total of 206 hemodialysis patients (mean age, 67 ± 13 years) without prior SARS-CoV-2 infection were examined for the primary outcome of seroconversion, defined as the detection of IgG antibodies (≥50 AU/mL) to the receptor-binding domain of the S1 spike protein of SARS-CoV-2 one month after a priming dose of ChAdOx1 nCoV-19, an adenovirus-vectored vaccine. Nutritional status was assessed by using the Controlling Nutritional Status (CONUT) score, an objective indicator of nutrition incorporating serum albumin, total cholesterol, and total lymphocyte count, as well as the subjective global assessment (SGA).ResultsOverall, 16.5% of patients were classified as malnourished, and 64.1% of patients were at risk for malnutrition based on the CONUT score. Anti-SARS-CoV-2 IgG were the highest in patients with normal nutrition. In multivariate logistic regression analyses adjusted for age, sex, comorbidities, and use of immunosuppressants, patients with malnutrition remained less likely to develop an antibody response than those with normal nutrition (odds ratio 0.23, 95% CI, 0.07-0.76). SGA was a significant predictor of anti-SARS-CoV-2 IgG seroconversion in univariate but not multivariate analyses.ConclusionsMalnutrition according to CONUT score is associated with impaired humoral responses to SARS-CoV-2 vaccination in patients undergoing hemodialysis. Our results highlight the importance of incorporating nutritional assessment into routine dialysis care to identify patients at risk for suboptimal immune responses after SARS-CoV-2 vaccination. Further research is needed to determine whether nutritional intervention can improve immune responses in these vulnerable patients.

Project description:ObjectivesPlasma levels of copeptin, a surrogate marker for the vasoconstrictor hormone arginine vasopressin (AVP), are increased in hemodialysis patients. Presently, it is unknown what drives copeptin levels in hemodialysis patients. We investigated whether the established physiological stimuli for copeptin release, i.e. plasma osmolality, blood volume and mean arterial pressure (MAP), are operational in hemodialysis patients.MethodsOne hundred and eight prevalent, stable hemodialysis patients on a thrice-weekly dialysis schedule were studied during hemodialysis with constant ultrafiltration rate and dialysate conductivity in this observational study. Plasma levels of copeptin, sodium, MAP, and blood volume were measured before, during and after hemodialysis. Multivariate analysis was used to determine the association between copeptin (dependent variable) and the physiological stimuli plasma sodium, MAP, excess weight as well as NT-pro-BNP immediately prior to dialysis and between copeptin and changes of plasma sodium, MAP and blood volume with correction for age, sex and diabetes during dialysis treatment.ResultsPatients were 63 ± 15.6 years old and 65% were male. Median dialysis vintage was 1.6 years (IQR 0.7-4.0). Twenty-three percent of the patients had diabetes and 82% had hypertension. Median predialysis copeptin levels were 141.5 pmol/L (IQR 91.0-244.8 pmol/L). Neither predialysis plasma sodium levels, nor NT-proBNP levels, nor MAP were associated with predialysis copeptin levels. During hemodialysis, copeptin levels rose significantly (p<0.01) to 163.0 pmol/L (96.0-296.0 pmol/L). Decreases in blood volume and MAP were associated with increases in copeptin levels during dialysis, whereas there was no significant association between the change in plasma sodium levels and the change in copeptin levels.ConclusionsPlasma copeptin levels are elevated predialysis and increase further during hemodialysis. Volume stimuli, i.e. decreases in MAP and blood volume, rather than osmotic stimuli, are associated with change in copeptin levels during hemodialysis.

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Constructing a prediction model for physiological parameters for malnutrition in hemodialysis patients.

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Constructing a prediction model for physiological parameters for malnutrition in hemodialysis patients.

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