Dry Weight In Hemodialysis Patients Research Articles

#4865 OBSERVATIONAL PILOT STUDY TO EVALUATE THE RELATIONSHIP BETWEEN LUNG ULTRASOUND AND BIOIMPEDANCE IN HEMODIALYSIS

Abstract Background and Aims There is a clear relationship between volume overload and morbidity and mortality in hemodialysis patients, which makes it essential to accurately adjust the normohydrated weight. For this, bioimpedance has become an indispensable tool in dialysis units. In recent years there has been a rapid development of lung ultrasound for the evaluation of pulmonary congestion through the quantification of B lines. Several studies have shown a correlation between the number of B lines and the volume overload estimated by bioimpedance in patients on dialysis. However, all these studies perform a 28-zone lung ultrasound protocol, which may be a limitation in clinical practice. Several studies have recently been published showing a correlation between 28-zone lung ultrasound protocol and simplified 4, 6, and 8-zone ultrasound protocols, which have been widely studied in patients treated in intensive care or emergency units. We set out to evaluate the relationship between bioimpedance and simplified 8-zone lung ultrasound protocol in hemodialysis patients. Method Pilot cross-sectional observation study that included 29 patients on hemodialysis at Infanta Sofía University Hospital during March 2022. Bioimpedance predialysis and postdialysis, 8-zone lung ultrasound protocol (delimited by the parasternal, anterior axillary, posterior axillary, and 4th intercostal space) and inferior vena cava ultrasound. Predialysis proBNP and Ca-125 were measured. Results There is a significant reduction in overhydration estimated by bioimpedance and in the number of B lines after the hemodialysis session (p<0.05). No correlation has been established between the two parameters, neither pre-dialysis nor post-dialysis. Nor with the pre-dialysis levels of proBNP or Ca125. There is a correlation of r = 0.45 (p<0.05) in the subgroup of patients from the afternoon shift (n = 9) between the number of B lines and extracellular water. Conclusion Lung ultrasound allows the evaluation of the degree of lung congestion, and therefore of extracellular water, becoming a useful tool for adjusting the dry weight of hemodialysis patients. In our study we found no association between the number of B lines in the simplified 8-zone lung ultrasound protocol. It is possible that this type of lung ultrasound is not sensitive enough in outpatients on hemodialysis, but it is sensitive enough in critically ill patients in whom lung overload is greater. Further studies with a larger number of patients are needed for evaluation.

Importance of Bio-electrical Impedance for Measurement of Body Fluid Status in Chronic Kidney Disease Patients on Maintenance Haemodialysis: A Narrative Review

Assessing the precise body water status in patients with renal disorders is crucial for their health. It has been reported that chronic fluid overload is present even in the early stages of renal insufficiency. If volume overload in a patient with chronic renal failure is not controlled, serious problems such as pulmonary oedema, cardiac remodelling, and diastolic heart failure can develop. Bioelectrical Impedance Analysis (BIA) has emerged as one of the most useful tools, although randomised clinical studies are lacking to support its universal use. Early management of hydration may improve clinical outcomes, as it allows for personalised dialysis prescriptions and nutritional support. BIA practice is utilised as non invasive health monitoring for Body Composition (BC). BIA is a practical and inexpensive method. Moreover, BIA parameters estimated for disease prognosis analysis have been found to be reasonably predictable for both patient status and healthcare. Additionally, BIA is a simple, accurate, portable, quick, easy, and low-cost method. In patients with End-stage Renal Disease (ESRD) undergoing maintenance Haemodialysis (HD) treatment, excessive fluid volume is considered a risk factor for death. Furthermore, fluid elimination to achieve Dry Weight (DW) is a crucial component of HD treatment for ESRD patients. DW is an important concept related to patients undergoing HD. Conventional methods seem to be time-consuming and operator-dependent. BIA is a new and simple method that has been reported to accurately estimate DW. Estimating the dry weight of HD patients is a challenging task. Many tools are available, but not every HD centre has access to them. Several strategies have been used to develop a more standardised method of assessing dry weight in HD patients. The Bio-impedance Spectroscopy (BIS) device has been validated against gold standard methods of volume assessment. Body composition monitoring appears to be a helpful diagnostic tool that reasonably complements existing clinical methods in assessing the DW of HD patients. BIS identifies Fluid Overload (FO) as a virtual "Over Hydration (OH)" compartment, which is calculated from the difference between the measured extracellular volume and the predicted values based on a fixed hydration of lean and adipose tissue mass.

Effect of oral nutritional supplementation combined with impedance vectors for dry weight adjustment on the nutritional status, hydration status and quality of life in patients on chronic hemodialysis: A pilot study

Protein energy wasting frequently affect hemodialysis patients and contribute to the development of overhydration. The objective of this study was to assess the effect of oral nutritional supplementation (ONS) combined with bioelectrical vector analysis (BIVA) on the nutritional and hydration status and the quality of life (QoL) in hemodialysis (HD) patients. Thirty-two chronic HD patients were included in a 6-month randomized pilot study. Patients in SUPL group received a simultaneous intervention consisting of a personalized diet, 245mL/d ONS and dry weight adjustment through BIVA. Patients in CON group received a personalized diet and dry weight adjustment by BIVA. Anthropometrical, biochemical, dietary, QoL, handgrip strength (HGS) and bioimpedance measurements were performed. Malnutrition Inflammation Score (MIS) was applied. At the end of the intervention, moderate undernutrition decreased by 43.8% in SUPL group while in CON group, severe undernutrition increased by 13% (p<0.04 between groups). In the adjusted covariance analysis, SUPL compared to CON group, increased HGS (Δ 2.8Kg vs Δ-1.8Kg, p=0.003), serum albumin (Δ 0.29g/dL vs Δ-0.03g/dL, p=0.04) and serum transferrin (Δ 4.7mg/dL vs Δ-0.7mg/dL, p=0.0007). The increase in QoL was significantly higher in SUPL group. Dry weight was achieved in 100% of patients in SUPL and 95% in CON group. ONS combined with BIVA for dry weight adjustment, improved nutritional status, QoL and achieved dry weight in HD patients.

Proof-of-Concept Model for the Prediction of Dry Weight in Hemodialysis Patients

Proof-of-Concept Model for the Prediction of Dry Weight in Hemodialysis Patients

A new technique for the detection of dry weight in hemodialysis patients: Estimated pulmonary capillary wedge pressure. A tissue Doppler imaging study

Background and objectiveFluid overload is an important factor of morbidity and mortality in hemodialysis patients. Today correct determination of dry weight (DW) remains an important issue of hemodialysis practice. Within this context, it is subjected to new method searching. The objective of this study was to calculate estimated pulmonary capillary wedge pressure (ePCWP) with Tissue Doppler Imaging (TDI) in hemodialysis patients and to evaluate its correlation with the other volume markers and to evaluate whether it can be a new method for detection of DW. Materials and methodsEchocardiographic, hemodynamic, and biochemical volume markers of 41 hemodialysis patients were evaluated in the pre- and post-dialysis periods. Patients were divided into two groups based on ePCWP values (Group 1 ePCWP<20mmHg, Group 2 ePCWP>20mmHg). ResultsIn the pre-dialysis period; parameters related to volume load including ePCWP, systolic blood pressure, mean arterial pressure, pulse pressure, left atrial diameter, left atrial volume, E/é, ratio and E/Vp ratio were statistically significantly higher in Group2 compared to Group1. On the other hand, strong correlations were found between pre-dialysis ePCWP and systolic blood pressure, mean arterial pressure, pulse pressure, NT-ProBNP, left atrial diameter, E/é ratio and E/Vp ratio. ConclusionsStrong correlations found between ePCWP which was calculated with TDI and the other volume markers both in pre-dialysis and post-dialysis periods. These findings can provide a significant contribution to routine evaluating of DW in hemodialysis patients. From this aspect, the prediction of ePCWP with TDI can be a new practical and reproducible method for the determination of DW.

Bioelectrical Impedance Vector Analysis and Brain Natriuretic Peptide in the Evaluation of Patients with Chronic Kidney Disease in Hemodialitic Treatment

Introduction: Setting dry weight (DW) in hemodialysis (HD) patients is still a hard issue. Several clinical, hematochemical, and instrumental parameters have been considered. In the last years, bioelectrical impedance vector analysis (BIVA) became the main method to evaluate body composition and water body percentage. However, it is still difficult to assess the nutritional status and identify a correct DW in HD patients. Aim: The aim of the study was to set DW and nutritional status, combining BIVA with phase angle (PhA) and serum brain natriuretic peptide (BNP) in HD patients. Methods: We evaluated PhA and BNP modifications before (T0), after HD section (T1), and after 60 days (T2), in all patients treated in our HD center. Results: A total of 50 patients (36 males) with a mean age of 70.1 ± 8.85 years were recruited. We did not report significant changes in BNP and PhA between T0 and T1, while they were significantly different between T0 and T2. We also reported a significant difference between T0 and T2 in ECW/TBW, while we did not show significant variations in ECM/BMC between T0, T1, and T2 indicating a stability of the nutritional status. PhA, BNP, and ECW/TBW returned to a normal value in patients in which we reached a DW, also considering clinical parameters such as blood pressure and antihypertensive therapy. The weight loss obtained with the evaluation of the BIVA and the BNP was 1.2–5.7 kg, greater than that calculated empirically which stood at around 0.9–4.3 kg. Conclusion: We suggest to carry out BIVA with PhA combined with BNP to assess an adequate DW and evaluate a correct nutritional status in HD patients.

POS-608 DRY WEIGHT ADJUSTMENTS FOR HEMODIALYSIS PATIENTS USING MACHINE LEARNING

Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study, we tried to predict the clinically proper dry weight (DWcp) using machine learning for patient’s clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight.

Role of the new bioimpedance monitoring device (Seca®) in assessing dry weight in hemodialysis patients.

In recent years, bioimpedance analysis has come to be widely used in clinical practice for dialysis patients, but there is not sufficient consensus on its significance. We aimed to examine the merits of performing bioimpedance analysis in addition to conventional evaluation methods for dry weight such as measuring human atrial natriuretic peptide (hANP), blood pressure, and cardiothoracic ratio in patients on chronic hemodialysis. Body composition of 78 hemodialysis patients was performed by using a new and more accurate segmental multifrequency bioimpedance analysis device (Seca® medical body composition analyzer 525, Seca GmbH & Co. KG, Hamburg, Germany). Laboratory data including hANP at post-dialysis and demographic profile were collected. Statistical analysis was performed with SPSS software. Mean age of the patients was 66.9 ± 12.6years and 80.8% were males. Mean value of hANP and the ratio of extracellular water to total body water (ECW/TBW) were 61.4 ± 36.4pg/mL and 46.1 ± 3.9%, respectively. The calculated ECW/TBW cutoff point for hANP > 50pg/mL was 45.0%, with sensitivity of 74.4% and specificity of 64.7%. Patients with an ECW/TBW of more than 45% and hANP value of > 50pg/mL had a higher blood pressure and cardiothoracic ratio on chest X-ray examination. Our results suggest that the ratio of extracellular water to total body water of more than 45% and hANP value of ≥ 50pg/mL were overhydrated in chronic hemodialysis patients. Whether monitoring levels of these parameters has a role in the outcome including patients' survival and cardiovascular events requires further study.

Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease.

Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.

Consistency of the Dry Weight of Hemodialysis Patients Predicted Using Bioelectrical Impedance Analysis Between Standing and Lying-Down Positions

Consistency of the Dry Weight of Hemodialysis Patients Predicted Using Bioelectrical Impedance Analysis Between Standing and Lying-Down Positions

A new technique for the detection of dry weight in hemodialysis patients: Estimated pulmonary capillary wedge pressure. A tissue Doppler imaging study

Background and objectiveFluid overload is an important factor of morbidity and mortality in hemodialysis patients. Today correct determination of dry weight (DW) remains an important issue of hemodialysis practice. Within this context, it is subjected to new method searching. The objective of this study was to calculate estimated pulmonary capillary wedge pressure (ePCWP) with Tissue Doppler Imaging (TDI) in hemodialysis patients and to evaluate its correlation with the other volume markers and to evaluate whether it can be a new method for detection of DW. Materials and methodsEchocardiographic, hemodynamic, and biochemical volume markers of 41 hemodialysis patients were evaluated in the pre- and post-dialysis periods. Patients were divided into two groups based on ePCWP values (Group 1 ePCWP<20mmHg, Group 2 ePCWP>20mmHg). ResultsIn the pre-dialysis period; parameters related to volume load including ePCWP, systolic blood pressure, mean arterial pressure, pulse pressure, left atrial diameter, left atrial volume, E/é, ratio and E/Vp ratio were statistically significantly higher in Group2 compared to Group1. On the other hand, strong correlations were found between pre-dialysis ePCWP and systolic blood pressure, mean arterial pressure, pulse pressure, NT-ProBNP, left atrial diameter, E/é ratio and E/Vp ratio. ConclusionsStrong correlations found between ePCWP which was calculated with TDI and the other volume markers both in pre-dialysis and post-dialysis periods. These findings can provide a significant contribution to routine evaluating of DW in hemodialysis patients. From this aspect, the prediction of ePCWP with TDI can be a new practical and reproducible method for the determination of DW.

Use of Point-of-Care Ultrasound for Evaluation of Extravascular and Intravascular Fluid Status in Pediatric Patients Maintained on Chronic Hemodialysis

Aims: Traditional methods that use clinical parameters to determine dry weight in hemodialysis patients are inaccurate. This study aimed to compare clinical assessment of fluid status to sonographic parameters of fluid status in pediatric patients undergoing chronic hemodialysis. Methods: In a prospective observational study, pediatric patients maintained on chronic hemodialysis (ages 2.3–20 years) were evaluated clinically and sonographically before and after dialysis at 6 consecutive sessions. Sonographic parameters examined were number of lung B-lines as a measure of extravascular volume and inferior vena cava (IVC)/aorta ratio as a measure of intravascular volume. Clinical assessment of fluid status was compared to sonographic assessment. Results: Twelve patients were evaluated during 72 dialysis sessions. Sonographic parameters were significantly lower post-dialysis than pre-dialysis (B-lines number 4.5 ± 5 vs. 7.69 ± 7.46, p < 0.0001; IVC/aorta ratio 0.9 ± 0.2 vs. 1.1 ± 0.2, p < 0.0001, respectively). Ultrafiltration volume correlated with change in B-lines number during dialysis (r = 0.39, p < 0.01). Percent of blood volume drop correlated with post-dialysis IVC/aorta ratio (r = 0.48, p < 0.001). A higher percent of symptomatic episodes occurred with post-dialysis IVC/aorta ratio <0.8 versus ≥0.8 (39.1 vs. 15.2%, p = 0.036). Four patients were hypertensive, a clinical parameter implying fluid overload, in only one sonographic evaluation indicated fluid overload. Eight patients were clinically determined to be euvolemic, in three of them sonographic evaluation discovered covert fluids. Conclusion: Bedside ultrasound is a single modality that can be used to assess both extravascular and intravascular fluid status. It may contribute to clinical decisions differentiating fluid-related versus fluid-unrelated hypertension and identifying patients with covert fluids.

Evaluation of Dry Weight in Hemodialysis Patients

Evaluation of Dry Weight in Hemodialysis Patients

A novel approach to dry weight adjustments for dialysis patients using machine learning.

Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study, we tried to predict the clinically proper dry weight (DWCP) using machine learning for patient's clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight. As a retrospective, single center study, data of 1672 hemodialysis patients were reviewed. DWCP data were collected when the dry weight was measured using the BIS (DWBIS). The gap between the two (GapDW) was calculated and then grouped and analyzed based on gaps of 1 kg and 2 kg. Based on the gap between DWBIS and DWCP, 972, 303, and 384 patients were placed in groups with gaps of <1 kg, ≧1kg and <2 kg, and ≧2 kg, respectively. For less than 1 kg and 2 kg of GapDW, It can be seen that the average accuracies for the two groups are 83% and 72%, respectively, in usign XGBoost machine learning. As GapDW increases, it is more difficult to predict the target property. As GapDW increase, the mean values of hemoglobin, total protein, serum albumin, creatinine, phosphorus, potassium, and the fat tissue index tended to decrease. However, the height, total body water, extracellular water (ECW), and ECW to intracellular water ratio tended to increase. Machine learning made it slightly easier to predict DWCP based on DWBIS under limited conditions and gave better insights into predicting DWCP. Malnutrition-related factors and ECW were important in reflecting the differences between DWBIS and DWCP.

Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm.

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient's dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

An Efficient Multiple Kernel Support Vector Regression Model for Assessing Dry Weight of Hemodialysis Patients

Background: Dry Weight (DW) is the lowest weight after dialysis, and patients with lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches have been presented to assess the dry weight of hemodialysis patients. However, these traditional methods all depend on special instruments and professional technicians. Objective: In order to avoid this limitation, we need to find a machine-independent way to assess dry weight, therefore we collected some clinical influencing characteristic data and constructed a Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients. Methods:: In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements, and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were closely related to their dry weight. All these relevant data were used to enter the regression equation. Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS) model was proposed to predict the dry weight of hemodialysis patients. Result: The experimental results show that dry weight is positively correlated with BMI and HR. And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negatively correlated with dry weight. Moreover, the Root Mean Square Error (RMSE) of our model was 1.3817. Conclusion: Our proposed model could serve as a viable alternative for dry weight estimation of hemodialysis patients, thus providing a new way for clinical practice. Our proposed model could serve as a viable alternative of dry weight estimation for hemodialysis patients, thus providing a new way for the clinic.

Effectiveness of bioimpedance spectroscopy on dry weight (DW) measurement in haemodialysis patients: a systematic review

Aim To review the effects of bioimpedance spectroscopy (BIS) to measure dry weight (DW) in haemodialysis (HD)patients when compared to clinical examination. Methods The review was conducted according to the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MasARI). Meta-analysis was performed using Review Manager 5 software. The search was limited to English language papers published from 2005 to 2017. A standard JBI three-step search strategy was used. We searched all quantitative studies from major databases for investigations of the BIS for measuring DW in HD patients. Results A total of four randomised controlled trials (RCTs) met the inclusion criteria. These studies all involved patients with end-stage kidney disease (ESKD) receiving HD and the comparison of BIS versus physician’s clinical judgement using cardiovascular parameters and mortality outcomes. The meta-analysis of BIS when compared with clinical judgement found no significant differences in the reduction of the mortality rate, hospitalisation rate or other cardiovascular parameters after 12 months of the intervention; only significant improvement in pre-dialysis systolic BP (WMD –5.41, 95% CI –9.00 to –1.82; p=0.003) was reported. However, individual RCTs found that BIS is an effective intervention for HD patients in the reduction of the mortality rate (HR 0.100, 95% CI, 0.013 to 0.805; p=0.04) after 2.5 years of the intervention. Conclusion The small number of participants and restricted participant selection criteria all has considerable impact with regard to generalising these findings to wider HD populations.

Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis.

Dry weight is the lowest weight patients on hemodialysis can tolerate; correct dry weight estimation is necessary to minimize morbi-mortality, but is difficult to achieve. Here, we used artificial intelligence to improve the accuracy of dry weight assessment in hemodialysis patients. We designed a neural network which used bio-impedancemetry, blood volume monitoring, and blood pressure values as inputs; output was artificial intelligence dry weight. Fourteen pediatric patients were switched from nephrologist to artificial intelligence dry weight. Artificial intelligence dry weight was higher (28.6%), lower (50%), or identical to nephrologist dry weight. Mean difference between artificial intelligence and nephrologist dry weights was 0.497kg (- 1.33 to + 1.29kg). In patients for whom artificial intelligence dry weight was lower than nephrologist dry weight, systolic blood pressure significantly decreased after dry weight decrease to artificial intelligence dry weight (77th to 60th percentile, p= 0.022); anti-hypertensive treatments were successfully decreased or discontinued in 28.7% of cases. In patients for whom artificial intelligence dry weight was higher than nephrologist dry weight, no hypertension was observed after dry weight increase to artificial intelligence dry weight; when present, symptoms of dry weight underestimation receded. Neural network predictions outperformed those of experienced nephrologists in most cases, proving artificial intelligence is a powerful tool for predicting dry weight in hemodialysis patients.

Effectiveness of bioelectrical impedance analysis interventions on volume status in hemodialysis patient:a systematic review

Objective To evaluate the effectiveness of bioelectrical impedance analysis(BIA) interventions on volume status in hemodialysis patient. Methods Searched The Cochrane Library, PubMed, EMbase, EBsco, Wanfang Data, China Biology Medicine, China National Knowledge Infrastructure to collect clinical trials. The retrieval time was from database to July 15, 2016. The studies were selected according to the inclusion and exclusion criteria and were critically appraised by two reviewers. Then the data of included studies were extracted. Meta-analysis was conducted by using RevMan 5.3 software and descriptive analysis. Results Ten clinical trials were included six random control trial, one controlled clinical trail, three longitudinal study, involving 2 009 patients. The results of Meta-analysis showed that the effectiveness of BIA interventions on volume management could improve blood pressure (P=0.001), protect the heart function(left ventricular mass index,P=0.002), compared with the clinical evaluation method, the BIA assessment of dry weight of hemodialysis patients could reduce hospitalization rate 14%. Descriptive analysis showed that BIA intervention volume management had certain advantages for the survival benefit of patients, but the current research was still few, had not yet come to a certain conclusion. Conclusions BIA can improve the management of the volume status and dry weight of hemodialysis patients, so as to it can improve the clinical benefit and survival benefit of patients.Better methods and guidelines for assessing DW and using BIA need to be developed.We propose that experienced HD nursing staff be trained in the use of the BIA to help monitor patient over hydration and approximate dry weight in consultation with the nephrologists responsible for the care of these patients so as to obviate excessive residual over hydration between nephrology reviews. Key words: Renal Dialysis; Electric impedance; Meta-analysis; Volume management

Bioimpedance vector analysis as a tool for the determination and adjustment of dry weight in patients undergoing hemodialysis

Overhydration in hemodialysis patients is associated with cardiovascular mortality. Adequate removal of liquids and achievement of dry weight is one of the main goals of therapy. So far there are no clinical or laboratory parameters that are reliable, simple and affordable for its determination. The bioelectrical impedance vector analysis (BIVE) is a tool that allows identifying and monitoring hydration status, so the aim of this study was to use BIVE to adjust the intensity of ultrafiltration and achieve dry weight in hemodialysis patients. We studied 26 patients on hemodialysis, which were performed monthly measurements of bioelectrical impedance for four months. Corresponding vectors were plotted to know in an individual way the state of hydration, according to which the dry weight was adjusted when necessary. Dry weight adjustment was performed in 13 patients, 7 of which required increase and 6 decrease of dry weight. The displacement of vectors on the ellipses corresponded to the type of intervention made. Dry weight was reached in 84.6% of patients at the end of the study with a significant decrease in mean arterial blood pressure and an increase in phase angle in the group of decrease of dry weight. Bioelectrical impedance vector analysis is an useful tool for adjusting the dry weight in patients undergoing hemodialysis.