Study on the correlation between bioelectrical impedance analysis index and protein energy consumption in maintenance dialysis patients

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Abstract

Background

Protein-energy wasting (PEW) has been reported to be pretty common in maintenance dialysis patients. However, the existing PEW diagnostic standard is limited in clinical use due to the complexity of it. Bioelectrical impedance analysis (BIA), as a non-invasive nutritional assessment method, can objectively and quantitatively analyze the changes of body tissue components under different nutritional states. We aim to explore the association between PEW and BIA and establish a reliable diagnostic model of PEW.

Methods

We collected cross-sectional data of 609 maintenance dialysis patients at the First Affiliated Hospital, College of Medicine, Zhejiang University. PEW was diagnosed according to International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Among them, 448 consecutive patients were included in the training set for the establishment of a diagnostic nomogram. 161 consecutive patients were included for internal validation. 52 patients from Zhejiang Hospital were included for external validation of the diagnostic model. Correlation analysis of BIA indexes with other nutritional indicators was performed. Logistic regression was used to examine the association of BIA indexes with PEW. 12 diagnostic models of PEW in maintenance dialysis patients were developed and the performance of them in terms of discrimination and calibration was evaluated using C statistics and Hosmer–Lemeshow-type χ2 statistics. After comparing to existing diagnostic models, and performing both internal and external validation, we finally established a simple but reliable PEW diagnostic model which may have great value of clinical application.

Results

A total of 609 individuals from First Affiliated Hospital, College of Medicine, Zhejiang University and 52 individuals from Zhejiang Hospital were included. After full adjustment, age, peritoneal dialysis (compared to hemodialysis), subjective global assessment (SGA, compared to non-SGA) and water ratio were independent risk factors, while triglyceride, urea nitrogen, calcium, ferritin, BCM, VFA and phase angle were independent protective factors of PEW. The model incorporated water ratio, VFA, BCM, phase angle and cholesterol revealed best performance. A nomogram was developed according to the results of model performance. The model achieved high C-indexes of 0.843 in the training set, 0.841 and 0.829 in the internal and external validation sets, respectively, and had a well-fitted calibration curve. The net reclassification improvement (NRI) showed 8%, 13%, 2%, 38%, 36% improvement of diagnostic accuracy of our model compared with “PEW score model”, “modified PEW score model”, “3-index model”, “SGA model” and “BIA decision tree model”, respectively.

Conclusions

BIA can be used as an auxiliary tool to evaluate PEW risk and may have certain clinical application value.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12937-023-00890-5.

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Most cited references 50

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Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

Michael J. Pencina, Ralph B. D' Agostino, Ramachandran S. Vasan (2008)

Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.

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Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients.

Olivier Bouillanne, Gilles Morineau, Claire Dupont … (2006)

Patients at risk of malnutrition and related morbidity and mortality can be identified with the Nutritional Risk Index (NRI). However, this index remains limited for elderly patients because of difficulties in establishing their normal weight. Therefore, we replaced the usual weight in this formula by ideal weight according to the Lorentz formula (WLo), creating a new index called the Geriatric Nutritional Risk Index (GNRI). First, a prospective study enrolled 181 hospitalized elderly patients. Nutritional status [albumin, prealbumin, and body mass index (BMI)] and GNRI were assessed. GNRI correlated with a severity score taking into account complications (bedsores or infections) and 6-mo mortality. Second, the GNRI was measured prospectively in 2474 patients admitted to a geriatric rehabilitation care unit over a 3-y period. The severity score correlated with albumin and GNRI but not with BMI or weight:WLo. Risk of mortality (odds ratio) and risk of complications were, respectively, 29 (95% CI: 5.2, 161.4) and 4.4 (95% CI: 1.3, 14.9) for major nutrition-related risk (GNRI: <82), 6.6 (95% CI: 1.3, 33.0), 4.9 (95% CI: 1.9, 12.5) for moderate nutrition-related risk (GNRI: 82 to <92), and 5.6 (95% CI: 1.2, 26.6) and 3.3 (95% CI: 1.4, 8.0) for a low nutrition-related risk (GNRI: 92 to < or =98). Accordingly, 12.2%, 31.4%, 29.4%, and 27.0% of the 2474 patients had major, moderate, low, and no nutrition-related risk, respectively. GNRI is a simple and accurate tool for predicting the risk of morbidity and mortality in hospitalized elderly patients and should be recorded systematically on admission.

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A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients.

K. Kalantar-Zadeh, J Kopple, G Block … (2001)

Malnutrition inflammation complex syndrome (MICS) occurs commonly in maintenance hemodialysis (MHD) patients and may correlate with increased morbidity and mortality. An optimal, comprehensive, quantitative system that assesses MICS could be a useful measure of clinical status and may be a predictor of outcome in MHD patients. We therefore attempted to develop and validate such an instrument, comparing it with conventional measures of nutrition and inflammation, as well as prospective hospitalization and mortality. Using components of the conventional Subjective Global Assessment (SGA), a semiquantitative scale with three severity levels, the Dialysis Malnutrition Score (DMS), a fully quantitative scoring system consisting of 7 SGA components, with total score ranging between 7 (normal) and 35 (severely malnourished), was recently developed. To improve the DMS, we added three new elements to the 7 DMS components: body mass index, serum albumin level, and total iron-binding capacity to represent serum transferrin level. This new comprehensive Malnutrition-Inflammation Score (MIS) has 10 components, each with four levels of severity, from 0 (normal) to 3 (very severe). The sum of all 10 MIS components ranges from 0 to 30, denoting increasing degree of severity. These scores were compared with anthropometric measurements, near-infrared-measured body fat percentage, laboratory measures that included serum C-reactive protein (CRP), and 12-month prospective hospitalization and mortality rates. Eighty-three outpatients (44 men, 39 women; age, 59 +/- 15 years) on MHD therapy for at least 3 months (43 +/- 33 months) were evaluated at the beginning of this study and followed up for 1 year. The SGA, DMS, and MIS were assessed simultaneously on all patients by a trained physician. Case-mix-adjusted correlation coefficients for the MIS were significant for hospitalization days (r = 0.45; P < 0.001) and frequency of hospitalization (r = 0.46; P < 0.001). Compared with the SGA and DMS, most pertinent correlation coefficients were stronger with the MIS. The MIS, but not the SGA or DMS, correlated significantly with creatinine level, hematocrit, and CRP level. During the 12-month follow-up, 9 patients died and 6 patients left the cohort. The Cox proportional hazard-calculated relative risk for death for each 10-unit increase in the MIS was 10.43 (95% confidence interval, 2.28 to 47.64; P = 0.002). The MIS was superior to its components or different subversions for predicting mortality. The MIS appears to be a comprehensive scoring system with significant associations with prospective hospitalization and mortality, as well as measures of nutrition, inflammation, and anemia in MHD patients. The MIS may be superior to the conventional SGA and the DMS, as well as to individual laboratory values, as a predictor of dialysis outcome and an indicator of MICS.

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Author and article information

Contributors

Jianghua Chen: chenjianghua@zju.edu.cn

Jing Yuan: 1183033@zju.edu.cn

Journal

Journal ID (nlm-ta): Nutr J

Journal ID (iso-abbrev): Nutr J

Title: Nutrition Journal

Publisher: BioMed Central (London )

ISSN (Electronic): 1475-2891

Publication date (Electronic): 9 November 2023

Publication date PMC-release: 9 November 2023

Publication date Collection: 2023

Volume: 22

Electronic Location Identifier: 56

Affiliations

[1 ]Kidney Disease Center, First Affiliated Hospital, College of Medicine, Zhejiang University, ( https://ror.org/00a2xv884) Hangzhou, 310000 China

[2 ]College of Medicine, Zhejiang University, ( https://ror.org/00a2xv884) Hangzhou, 310000 China

[3 ]Hebei ophthalmology hospital, Xingtai, 054000 China

Article

Publisher ID: 890

DOI: 10.1186/s12937-023-00890-5

PMC ID: 10633946

PubMed ID: 37940938

SO-VID: 7dcac977-d87c-4a64-b7bb-3bc97e1dc8b7

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 1 December 2022

Date accepted : 1 November 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100017594, Medical Science and Technology Project of Zhejiang Province;

Award ID: 2022PY010

Custom metadata

ScienceOpen disciplines: Nutrition & Dietetics

Keywords: bioelectrical impedance analysis,maintenance dialysis,protein-energy wasting,diagnostic model

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ScienceOpen disciplines: Nutrition & Dietetics

Keywords: bioelectrical impedance analysis, maintenance dialysis, protein-energy wasting, diagnostic model

Study on the correlation between bioelectrical impedance analysis index and protein energy consumption in maintenance dialysis patients

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Abstract

Background

Methods

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Conclusions

Supplementary Information

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Karger: Nutrition and Dietetics

Most cited references 50

Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients.

A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients.

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