Evaluation of body composition monitoring for assessment of nutritional status in hemodialysis patients

Hai-fen, Zhang; Tao, Xingjuan; Shi, Ling; Jiang, Na; Yang, Yan

doi:10.1080/0886022x.2019.1608241

Cited by 13 publications

(14 citation statements)

References 28 publications

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“…The interesting finding in our study indicated that the combination of both low lean and high fat mass appears to be the worst body composition, in terms of the highest risk of the IDH occurrence. In HD patients it is well established that lean tissue loss is independently associated with poor outcomes [16,21], but the effect of changes in fat mass is more complicated. Attempts to verify the potential survival benefit of increased fat mass have shown mixed result [22][23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

“…A body composition monitor (BCM) is the only clinically available method for distinguishing among the 3 body components, providing bedside assessment of volume status, fat mass, and lean tissue mass [14]. The values of the BCM on fluid management and nutritional assessment for HD patients have been extensively examined [15,16]. However, there has been no research on the appropriate use of BCM parameters for assessment of the IDH status of HD patients.…”

Section: Introductionmentioning

confidence: 99%

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Association of Body Composition and Intradialytic Hypotension in Hemodialysis Patients

et al. 2020

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Background/Aim: The relationship between body mass index (BMI) and intradialytic hypotension (IDH) has been inconsistently reported, but no further research has investigated the correlation between body composition and IDH so far. This study aimed to determine whether the lean tissue index (LTI), fat tissue index (FTI), or both derived from body composition monitoring (BCM) is associated with IDH defined as a nadir intradialytic systolic blood pressure of < 90 mm Hg and ≥3 episodes hypotension per 10 hemodialysis (HD) treatments in patients undergoing prevalent HD. Methods: The observational cohort study comprised 1,463 patients receiving thrice-weekly HD from 13 dialysis centers. LTI and FTI were assessed using a BCM machine, a multifrequency bioimpedance spectroscopy device. Unadjusted and multivariable adjusted logistic regression models were fit to estimate the association of body composition with the odds of developing IDH. Results: One hundred and seven patients (7.3%) were diagnosed as IDH. The difference in dialysis vintage, BMI, FTI, LTI, high-density lipoprotein cholesterol, and C-reactive protein between IDH and non-IDH groups was statistically significant (all p < 0.05). The preva-lence of diabetes among IDH patients was slightly higher than among non-IDH patients. In logistic regression models, low LTI and high FTI, but not high BMI were associated with greater odds of IDH ("high" as above median and "low" as below median). When patients were further stratified into 4 distinct body composition groups based on both the LTI and FTI, only the low LTI/high FTI group was connected with a significantly higher odds of IDH (OR 2.686, 95% CI 1.072-6.734; reference: low LTI/low FTI group). Conclusions: The LTI and FTI can provide better correlation of IDH occurrence than the BMI alone in prevalent HD patients. The low LTI/ high FTI appears to be most associated with IDH. An optimal body composition for preventing the occurrence of IDH needs to be determined.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association of Body Composition and Intradialytic Hypotension in Hemodialysis Patients

et al. 2020

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“…The lean tissue is composed of muscles, organs, blood and bones and can estimate the muscle mass of the whole body to reflect the storage of proteins. Hemodialysis patients presented with a decrease in LTI 30 , which was associated with adverse prognosis 11,[18][19][20][21][22][23] . Our results proved that the LTM and BCM standardized by height square (LTI and BCMI) were more associated with mortality compared with those standardized by weight (LTM/weight and BCM/weight), which may be influenced by the hydration status that was very common in hemodialysis patients.…”

Section: Discussionmentioning

confidence: 99%

Comparative Performance of Body Composition Parameters in Prediction of Death in Hospitalized Patients on Maintenance Hemodialysis: A Cohort Study

Yan

Zhang

et al. 2020

Sci Rep

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We compared the prognostic value of nutritional or volumetric parameters measured by body composition in hospitalized patients on maintenance hemodialysis. We conducted a cohort study to assess the association of different parameters of body composition with all-cause mortality in inpatients admitted to our nephrology department from January 2014 to December 2016. Of the 704 study patients, 160 (22.7%) died during a median follow-up of 33 months. In multivariate adjusted Cox models, higher ratio of extracellular water to body cell mass (ECW/BCM) (adjusted HR per 1-SD, 1.49; 95% CI, 1.19 to 1.85), lower lean tissue index (LTI) (adjusted HR per 1-SD, 0.70; 95% CI, 0.57 to 0.86) and lower body cell mass index (BCMI) (adjusted HR per 1-SD, 0.70; 95% CI, 0.58 to 0.85) were associated with a significantly greater risk of death. When these parameters were added to the fully adjusted model, BCMI performed best in improving the predictability for all-cause mortality (integrated discrimination improvement = 0.02, P = 0.04; net reclassification index = 0.11, P = 0.04). Among body composition indexes, ECW/BCM was the most relevant fluid volume indices to mortality and BCMI and LTI were the most relevant nutritional status indices to mortality in maintenance hemodialysis patients. End-stage renal disease (ESRD) has become one of the major health problems in the world. In 2010, it was estimated that 284 individuals per million population were undergoing maintenance dialysis throughout the world 1 , and the number of dialysis patients is growing at an alarming rate 2-5. Dialysis is the major treatment for ESRD and brings a heavy economic burden to all countries 6. Despite this, dialysis patients still have poor prognosis 5,7. The main risk factors for the mortality includes vascular access, cardiovascular complications, cerebrovascular complications, infection, anemia, mineral metabolic disorders, renal osteopathy, fluid overload (FO) and malnutrition 7,8. Bioelectrical impedance analysis-based body composition analysis is widely used and accepted as an ideal tool for assessing fluid volume and nutritional status in maintenance dialysis patients 9. It is easy-to-use, safe, noninvasive, repeatable and comprehensive. It has both anthropometric parameters in assessing fluid volume [such as overhydration (OH) 10 , OH/extracellular water (OH/ECW) 8,11-14 , the ratio of extracellular water to intracellular water (ECW/ICW) 15 , the ratio of extracellular water/body cell mass (ECW/BCM) 16,17 ] and nutrition status [such as lean tissue index (LTI) 11,18-23 , fat tissue index (FTI) 11,18 , body cell mass index (BCMI) 24,25 , BCM/weight 26 ]. However, it is still unclear which nutritional or volumetric parameters of body composition, measured by bioelectrical impedance, have the greatest prognostic value in maintenance hemodialysis patients. We designed this cohort study to assess and compare the association of different parameters in body composition with all-cause medium-term mortality in hemodialysis patients.

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“…The proposed model and algorithms have been validated through a set of bioimpedance measurements in a group of peritoneal dialysis (PD) patients. This second use case has been selected because in uremic patients treated with both hemodialysis (HD) and PD, it is very important to assess the amount of fluid excess to determine how much should be removed through ultrafiltration to achieve a desired state of normohydration [69,70]. If fluid excess is not removed long-term consequences can be severe [1,2].…”

Section: Description Of the Software Validation Studymentioning

confidence: 99%

“…If fluid excess is not removed long-term consequences can be severe [1,2]. In this sense, bioimpedance analysis has also been successfully applied in nephrology for the identification of the patient's dry weight and the improvement of cardiovascular management [1,9,[69][70][71][72].…”

Section: Description Of the Software Validation Studymentioning

confidence: 99%

Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

Naranjo-Hernández

Reina‐Tosina

Roa

et al. 2019

Sensors

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The purpose of this work is to describe a first approach to a smart bioimpedance spectroscopy device for its application to the estimation of body composition. The proposed device is capable of carrying out bioimpedance measurements in multiple configurable frequencies, processing the data to obtain the modulus and the bioimpedance phase in each of the frequencies, and transmitting the processed information wirelessly. Another novelty of this work is a new algorithm for the identification of Cole model parameters, which is the basis of body composition estimation through bioimpedance spectroscopy analysis. Against other proposals, the main advantages of the proposed method are its robustness against parasitic effects by employing an extended version of Cole model with phase delay and three dispersions, its simplicity and low computational load. The results obtained in a validation study with respiratory patients show the accuracy and feasibility of the proposed technology for bioimpedance measurements. The precision and validity of the algorithm was also proven in a validation study with peritoneal dialysis patients. The proposed method was the most accurate compared with other existing algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided better approximations to the bioimpedance values than a reference device.Sensors 2020, 20, 70 2 of 27 during pregnancy and lactation [6], in the evaluation of the risk of various pathologies [7,8], as a marker or direct cause of disease [9,10], during the process of decision making in an illness [11], aging [12] or rehabilitation processes [13], as a complement to the diagnosis and follow-up of conditions related to the cardiovascular system [14,15], in oncology [16] and even in sports science [17], among others.Despite the advances in the clinical application of bioimpedance, there are still some challenges to be solved, such as the integration of devices into e-Health systems supporting remote user monitoring [18]. In addition, the complexity of bioimpedance systems is usually quite high (current injection, voltage measurement, demodulation, processing, etc.) and the use of high-frequency signals (tens to hundreds of kHz) generally requires high power consumption, so new challenges arise for the optimization of hardware in size, energy efficiency, robustness and precision [1,19]. On the other hand, although the bioimpedance spectroscopy technique has proven to be more precise and robust than the single frequency technique, most devices base their operation on the measurement on a single frequency [1,20].Other issues are related to the models and algorithms used to estimate the BC from bioimpedance measures. A key point for an adequate BC estimation by bioimpedance analysis is a correct parameter identification of the bioimpedance model, usually the Cole model [1,[21][22][23][24][25][26][27][28][29][30][31]. Sometimes, this parameterization is performed without directly measuring the impedance values [21][22][23]. Other authors use...

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Evaluation of body composition monitoring for assessment of nutritional status in hemodialysis patients

Cited by 13 publications

References 28 publications

Association of Body Composition and Intradialytic Hypotension in Hemodialysis Patients

Association of Body Composition and Intradialytic Hypotension in Hemodialysis Patients

Comparative Performance of Body Composition Parameters in Prediction of Death in Hospitalized Patients on Maintenance Hemodialysis: A Cohort Study

Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

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