ORIGINAL RESEARCH

Body Composition Affects Urea Distribution Volume Estimated by Watson’s Formula Almudena Vega, MD, PhD, Caridad Ruiz, MD, Soraya Abad, MD, Borja Quiroga, MD, PhD, Kyra Velazquez, MD, Jara Ampuero, MD, and Juan Manuel Lopez-Gomez, MD, PhD Objective: Dialysis machines use the Watson formula (Vwatson) to estimate the urea distribution volume (UDV) to calculate the online Kt/V for each dialysis session. However, the equation could give rise to inaccuracies. The present study analyzes whether body composition affects UDV estimated by Vwatson in comparison to bioimpedance spectroscopy (Vbis) as the reference method. Design: This is a transversal study performed in the setting of a hemodialysis unit. Subjects: Prevalent hemodialysis patients. Intervention: The same day, UDV was measured using Vwatson and Vbis. We compared their results. Main Outcome Measure: Differences between UDV using Watson equation and Vbis. Results: We included 144 prevalent patients. Vwatson overestimated the volume with regard to Vbis (Vwatson 2 Vbis) by 2.5 L (1.8 L; P 5 .001). We found an excellent correlation between the 2 methods. A higher mean Vwatson 2 Vbis value was correlated to older age (P 5 .03), body mass index (P 5 .01), fat tissue index (P 5 .001), lower lean tissue index (P 5 .001), lower extracellular water (P 5 .01), and intracellular water (P 5 .001). Conclusion: Body composition affects UDV estimated by Vwatson, thus modifying the result of Kt/V. In young patients who present more lean tissue and less fat tissue, Kt/V is underestimated with Vwatson. Ó 2015 by the National Kidney Foundation, Inc. All rights reserved.

T

Introduction

HE DIALYSIS DOSE is usually set based on urea clearance, which is calculated using the Kt/V formula. In this equation, ‘‘K’’ represents urea clearance during the dialysis session, ‘‘t’’ is the time of dialysis, and ‘‘V’’ is the urea distribution volume (UDV).1 This volume is equivalent to total body water (TBW). UDV is commonly estimated using the Watson formula (Vwatson), which is based on anthropometric measures that influence body composition, such as sex, age, weight, and height.2 UDV as measured by Vwatson has been shown to be more closely correlated to the modeled UDV than when using other equations.3,4 Accordingly, dialysis monitors use Vwatson to estimate the distribution volume to calculate the online Kt/V for each dialysis session. However, Vwatson could give rise to inaccuracies.5 Department of Nephrology, Hospital Gregorio Mara~non, Madrid, Spain. Financial Disclosure: The authors declare that they have no relevant financial interests. The authors declare that they have participated in the present study. C.R. did data collection from patients and estimated volume of urea distribution using the Watson equation. S.A. contributed to the design of the study and performed bioimpedance spectroscopy studies. K.V. and J.A. performed bioimpedance spectroscopy studies. B.Q. contributed to statistical analysis and the design of the study. J.M.L.G. designed the study. A.V. performed bioimpedance spectroscopy studies, contributed to the design of the study, and wrote the article. Address correspondence to Almudena Vega, MD, PhD, Hospital Gregorio Mara~non, Doctor Esquerdo 46, Madrid 28007, Spain. E-mail: vega.

[email protected] Ó

2015 by the National Kidney Foundation, Inc. All rights reserved. 1051-2276/$36.00 http://dx.doi.org/10.1053/j.jrn.2015.02.008

Journal of Renal Nutrition, Vol -, No - (-), 2015: pp 1-6

Bioimpedance spectroscopy is a simple and noninvasive technique based on the resistance of tissue to the flow of an alternating current ranging from 5 to 1000 kHz in frequency.6-9 It has been validated against gold standard methods for assessing body composition and hydration state and is therefore considered a reference method.7 The present study analyzes whether body composition affects the UDV estimated by Vwatson in comparison to Vbis as the reference method.

Methods

Study Population Prevalent hemodialysis patients aged more than 18 years and of Caucasian race were included in this cross-sectional, noninterventional study. Exclusion criteria were amputations and carriers of pacemakers, implantable defibrillators, or metallic prostheses because of contraindications or difficulties in interpreting the bioimpedance results. We also excluded patients with clinical instability, defined as any hospital admission within 3 months before the start of the study, and patients with recent changes in dry weight or body composition. Baseline characteristics were recorded, including age, sex, etiology of clinical kidney disease, percentage of diabetes, dialysis vintage, and mean Daugirdas Kt/V. Characteristics of Renal Replacement Therapy Patients were receiving 3 sessions of 4 hours of hemodialysis per week. Seventy patients were treated by 1

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VEGA ET AL

postdilution online hemodiafiltration, and 74 patients were treated by hemodialysis. We used the monitor Artis AK 200 ULTRA S (Gambro, Stockholm, Sweden) and the dialyzers 4008 and 5008 (Fresenius Medical Care [FMC], Bad Homburg, Germany). Dialyzers were FX1000 (FMC; Helixone membrane; ultrafiltration coefficient of 75 mL/h 3 mm Hg; effective surface of 2.2 m2; wall thickness of 35 mm; and lumen of 210 mm) and Polyflux 210H, (Gambro; polyamide membrane; ultrafiltration coefficient of 85 mL/h 3 mm Hg; effective surface area of 2.1 m2; wall thickness of 50 mm; and lumen of 215 mm) for online hemodiafiltration and FX60 (FMC; Helixone membrane; ultrafiltration coefficient of 46 mL/h 3 mm Hg; effective surface of 1.4 m2; wall thickness of 35 mm; and lumen of 185 mm) for hemodialysis. Mean blow flow rate was 403 6 97 mL/min and mean dialysate flow was 604 6 127 mL/min.

Measurement of Body Composition UDV Estimation Using Bioimpedance Spectroscopy Body composition was analyzed by Vbis, BCM (FMC, Bad Homburg, Germany). Measurements were taken in the limbs contralateral to the arteriovenous fistulae after a 10-minute resting period in the supine position before the dialysis session according to operating instructions advice. We collected body composition and hydration parameters. The recorded hydration parameters were TBW (L), which is equivalent to UDV, extracellular water (ECW [L]), intracellular water (ICW, [L]), and overhydration (OH, [L]), defined as water not included in the extracellular and extracellular compartments and considered as an excess of water. Body composition parameters in turn were fat tissue index (FTI) and lean tissue index (LTI), defined respectively as fat and lean tissues adjusted for body surface (kg/m2). UDV Estimation Using Vwatson We used the following formulas to estimate UDV:

Statistical Analysis The SPSS version 17.0 statistical package (Chicago, IL) was used for data processing and analysis. The Kolmogorov-Smirnov test was performed to determine whether the values followed a normal distribution. Quantitative variables were expressed as means and standard deviations or medians (interquartile ranges). Qualitative variables were expressed as percentages. We calculated the numerical difference in UDV between Vbis (as reference) and Vwatson. To assess the factors associated with the overestimation or underestimation of UDV with both methods, we compared the mean of the differences with each collected value using a univariate Student t test for independent samples or analysis of variance as appropriate. Multivariate analysis was performed when differences in variable were significant in the univariate analysis. Correlations and agreement were also established between the differences in the estimation (Vwatson 2 Vbis) and the variables using the Spearman rho test, Bland-Altman test, and intraclass correlation coefficient. Statistical significance was considered for P ,.05.

Results The study population consisted of 144 patients. The baseline characteristics, including the measurement of body composition using Vbis, are listed in Table 1. The mean UDV was 34.1 6 6.5 L and 31.6 6 7.0 L using Vwatson and Vbis, respectively (difference 2.5 6 1.8 L, P 5.001). It can be concluded from Table 2 that this difference remains on dividing patients into body mass index (BMI) tertiles. An excellent correlation was observed between Vwatson and Vbis as depicted in Figure 1 (Spearman rho 5 0.846; P 5 .001) and by Bland-Altman test in Figure 2. Intraclass correlation coefficient between them also showed an excellent agreement 0.87 (0.80-0.93). Mean online Kt/V was 1.62 6 0.36 and 1.78 6 0.46 using Vwatson and Vbis, respectively, and the difference between them was statistically significant (P 5.001).

Vwatson for males52:4472ð0:091563ageÞ1ð0:10743heightÞ1ð0:33623weightÞ Vwatson for females522:0971ð0:10693heightÞ1ð0:24663weightÞ

The weight corresponded to dry weight. We analyzed Vwatson and Vbis in the same dialysis session for each patient. To avoid changes in hemodialysis, we estimated them when the sessions had been finished. To calculate the online Kt/V, we collected data from ‘‘K’’ using online ionic dialysance given by monitors, time of the session (t) in minutes, and ‘‘V’’ using Vwatson and Vbis. Two different Kt/V values were therefore estimated.

For assessing the factors associated to increased differences in UDV between both methods, we performed univariate Student t test and multivariate logistic regression, which showed old age, higher BMI and FTI, and lower LTI, ICW, and ECW to be related to greater differences between Vwatson and Vbis (Table 3). We did not find significant association between Vwatson and Vbis differences and sex and diabetes. Correlations between mean Vwatson 2 Vbis and patient age, ICW, ECW, BMI, FTI, and LTI are depicted

BIOIMPEDANCE UREA DISTRIBUTION VOLUME

3

Table 1. Descriptive Characteristics of the Study Population Total (N 5 144)

Characteristic Age (y) Sex, M/F (%) Etiology of chronic kidney disease (%) Diabetes mellitus Glomerulonephritis Vascular nephropathy Interstitial nephropathy Polycystic kidney disease Others Unknown Diabetes mellitus (%) Dialysis vintage (mo)* Kt/V BMI (kg/m2) FTI (kg/m2) LTI (kg/m2) TBW (L) ECW (L) ICW (L) OH (L)

68 6 13 56/44 24 21 10 13 11 5 16 32 85 (43-128) 1.8 6 0.6 26.0 6 5.6 13.9 6 6.1 12.1 6 3.4 31.6 6 7.0 15.4 6 3.3 16.1 6 4.3 1.2* (0.5-4.2)

BMI, body mass index; ECW, extracellular water; F, females; FTI, fat tissue index; ICW, intracellular water; LTI, lean tissue index; M, males; OH, overhydration in relation to patient dry weight; TBW, total body water. *Median and interquartile range.

in Figure 3. We only found global Vwatson overestimation in all age groups. The remaining variables exhibited cutoff points where overestimation became underestimation or vice versa. The cutoff point of 15 kg/m2 for LTI, which the Watson underestimates, the urea volume has been described in Figure 3. We divided LTI in tertiles as summarized in Table 4. The highest tertile showed an inverse association, although not significant. We also compared variables associated with positive and negative differences between volume of urea distribution estimated with Watson equation and spectroscopy

Figure 1. Correlation between urea distribution volume (UDV) measured with the Watson formula and spectroscopic bioimpedance. Vwatson, UDV measured with the Watson formula and with bioimpedance spectroscopy (Vbis).

bioimpedance, as summarized in Table 5. We found an association with ICW, FTI, and LTI. Independent association was found only in LTI.

Discussion The present study shows that variations in body composition influence UDV, as estimated by Vwatson. We consider that these variations should be taken into account to correctly interpret Kt/Vonline, considering that Kt/V is recognized to be the best dialysis dose parameter.10 Corporal composition of our hemodialysis patients (BMI, LTI, FTI, ECW, and OH) represents similar characteristics compared to that of the previous reports.11 Body composition of our hemodialysis patients is in agreement with those previously reported11 and extends these observations to Spanish hemodialysis patients.

Table 2. Analysis of the Difference in UDV Using Spectroscopic Bioimpedance Versus the Watson Formula Comparing BMI Tertiles BMI Tertile BMI tertile 1 BMI tertile 2 BMI tertile 3

Vwatson Vbis Vwatson 2 Vbis (Mean 6 SD) (Mean 6 SD) (Mean 6 SD)

P

30.1 6 4.4

28.9 6 5.5

1.2 6 0.6

.027

34.0 6 4.9

31.3 6 6.2

2.7 6 1.0

.001

38.1 6 7.2

34.5 6 8.1

3.6 6 2.3

.001

BMI, body mass index; SD, standard deviation; UDV, urea distribution volume; Vbis, volume of urea distribution using spectroscopy bioimpedance; Vwatson, volume of urea distribution using Watson equation; Vwatson 2 Vbis, difference between volume of urea distribution using Watson equation and spectroscopy bioimpedance.

Figure 2. Bland-Altman method to assess agreement between urea distribution volume (UDV) measured with Watson’s formula and spectroscopic bioimpedance. (Vwatson 1 Vbis)/2, mean UDV measured with both methods. Vwatson 1 Vbis, mean difference between both methods. Line represents mean difference.

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Table 3. Analysis and Multivariate Logistic Regression of the Difference in UDV Using Spectroscopic Bioimpedance Versus the Watson Formula Characteristic Age (y) ECW (L) ICW (L) LTI (kg/m2) FTI (kg/m2) BMI (kg/m2)

Vwatson 2 Vbis ,2.5 L: Mean 6 SD (N 5 69)

Vwatson 2 Vbis .2.5 L: Mean 6 SD (N 5 75)

P

Beta Exp

P

65 6 14 16.1 6 2.9 17.9 6 3.9 14.2 6 3.1 10.8 6 5.7 24.8 6 5.4

70 6 16 14.7 6 3.2 14.3 6 3.8 10.1 6 2.4 16.6 6 5.2 27.1 6 5.4

.03 .007 .001 .001 .001 .014

.976 .505 2.413 .111 .444 2.841

.26 .007 .003 .001 .017 .005

BMI, body mass index; ECW, extracellular water; FTI, fat tissue index; ICW, intracellular water; LTI, lean tissue index; SD, standard deviation; UDV, urea distribution volume; Vbis, volume of urea distribution using spectroscopy bioimpedance; Vwatson, volume of urea distribution using Watson equation; Vwatson 2 Vbis, difference between volume of urea distribution using Watson equation and spectroscopy bioimpedance. The value 2.5 L corresponds to the mean difference value between Vwatson and Vbis.

Vbis represents a 3-compartmental model that reflects advantages compared with 2-compartmental models for increased accuracy in measurement of fluid overload and fat tissue techniques.12 In the course of its development, Vbis was validated against relevant gold standard measures in both healthy individuals and in patients to assess not only the state of hydration but also body composition.13,14 These measures were sodium bromide for ECW, deuterium for TBW, body potassium for intracellular volume; and dual-energy X-ray absorptiometry, air displacement pleth-

ysmography, and 4-compartmental modeling for body composition.8,10,15-19 Comparison of bioimpedance spectroscopy against the gold standard methods showed excellent concordance. For this reason, we used distribution volume measured by Vbis as reference. Accordingly, variations in UDV estimated with Vwatson were regarded as inaccuracies. However, we cannot forget that calculations using gold standard methods have mostly used the same diffusion and equilibration constants in dialysis patients as in normal subjects. A practical ‘‘gold

Figure 3. Correlations between mean differences in urea distribution volume (UDV) measured by the Watson formula versus spectroscopy bioimpedance in relation to hydration and body composition. Vwatson, urea distribution volume with Watson equation; Vbis, urea distribution volume with bioimpedance spectroscopy,Vwatson-Vbis, the difference between UDV measured with the watson formula and bioimpedance spectroscopy. A possitive result correspond to a overestimation Vwatson. A negative result correspond to a infraestimation of Vwatson. (A) correlation between overestimation Vwatson and age. (B) correlation between Vwatson-Vbis and extracellular water. Arrow shows the point where overestimation Vwatson becomes infraestimation compared to Vbis. (C) correlation between Vwatson-Vbis and intracellular water. Arrow shows the point where overestimation Vwatson becomes infraestimation compared to Vbis. (D) correlation between overestimation Vwatson and BMI. (E) correlation between Vwatson-Vbis and FTI. Arrow shows the point where infraestimation Vwatson becomes overestimation compared to Vbis. (F) correlation between Vwatson-Vbis and LTI. Arrow shows the point where overestimation Vwatson becomes infraestimation compared to Vbis. BMI, body mass index; ECW, extracellular water; FTI, fat tissue index; ICW, intracellular water; LTI, lean tissue index.

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BIOIMPEDANCE UREA DISTRIBUTION VOLUME Table 4. Analysis of the Difference in UDV Using Spectroscopic Bioimpedance Versus the Watson Formula Comparing LTI Tertiles LTI Tertile

Vwatson Vbis Vwatson 2 Vbis (Mean 6 SD) (Mean 6 SD) (Mean 6 SD)

LTI tertile 1 LTI tertile 2 LTI tertile 3

P

31.7 6 5.1

26.1 6 4.3

5.6 6 2.3

.001

33.3 6 5.6

30.6 6 5.4

2.67 6 2.0

.001

37.2 6 7.3

37.9 6 6.2

20.69 6 3.6

.185

LTI, lean tissue index; SD, standard deviation; UDV, urea distribution volume; Vbis, volume of urea distribution using spectroscopy bioimpedance; Vwatson, volume of urea distribution using Watson equation; Vwatson 2 Vbis, difference between volume of urea distribution using Watson equation and spectroscopy bioimpedance.

standard’’ method for body composition is yet to come. As previous reports have suggested, although measurements of hydration status are useful and accurate, they will always be just one part of the clinical practice.20 Our data revealed good agreement between Vwatson and Vbis but also a mean overall overestimation with Vwatson, and logically, an underestimation of Kt/V, as described elsewhere.21 This fact may not pose a problem because we could simply assume that the real dialysis doses are even higher than estimated. However, we should be aware of the opposite cases in which Vwatson produces underestimation and Kt/V online could be overestimated because the real dialysis doses might not be sufficient. In any case, Vwatson overestimation in aged patients should not be only related to the content of age in Watson equation. Daugirdas3 analyzed how anthropometrically estimated TBW volumes are larger in hemodialysis patients and how age, race, and sex contributed to this overestimation. Our study shows that overestimation with Vwatson increases with age, which means that Kt/V in the elderly population is underestimated, and the real dialysis doses would be higher. Although knowing that these age-related variations are not a problem but an advantage among elderly patients, it is important to be strict with the dialysis doses in younger patients.

However, body composition changes with age, and older people are known to have less proportions of lean tissue and water. Our study showed overestimation with Vwatson in individuals with a lower LTI and underestimation in those with higher LTI. Our cutoff point is 15 kg/m2, as shown in Figure 2. In this case, an LTI of .15 kg/m2 would overestimate Kt/V, and patients would be receiving lesser dialysis doses than recommended. On the other hand, when focusing on fat tissue variations, we observe the opposite situation: Patients with a high FTI present an overestimation with Vwatson and an underestimation of Kt/V. This finding may to some extent explain the so-called ‘‘reverse epidemiology,’’ in which patients with a higher proportion of fat tissue have higher survival rates.22-25 This paradox could be partially explained by the fact that these patients are receiving higher dialysis doses than those we are recording. Furthermore, a higher percentage of fat tissue implies lesser volumes of ICW and ECW, as described by Chamney et al.26 We thus postulate that the higher survival rates observed in individuals with a higher BMI—which is known to be dependent on a greater percentage of adipose tissue—are because of an overestimation of Vwatson causing a false decrease in Kt/ V that is compensated by increasing the dialysis dose. Taking into account the variations experienced by distribution volume with regard to body composition, we propose the possibility of introducing Vbis in the dialysis monitor, if available in the dialysis center.22 This would allow us to obtain an accurate online Kt/V for each dialysis session. Monitors measure the difference in conductivity between the dialysate entering and leaving the dialyzer with 2 different dialysate inlet electrolyte concentrations. These measurements can be used to calculate the ionic dialysance, which is equal to the effective urea clearance.27 Our study has several limitations. We performed bioimpedances before starting dialysis. Although we performed them according to BCM FMC operating instructions, there are studies that recommend bioimpedance after hemodialysis to avoid the added confounder of the predialysis weight gain. Because of complexity in analyzing body composition in healthy

Table 5. Student t Analysis and Multivariate Logistic Regression of Positive and Negative Difference in UDV Using the Watson Equation and Spectroscopic Bioimpedance Characteristic Age (y) ECW (L) ICW (L) LTI (kg/m2) FTI (kg/m2) BMI (kg/m2)

Vwatson 2 Vbis ,0; Mean 6 SD (N 5 30)

Vwatson 2 Vbis .0.1; Mean 6 SD (N 5 113)

P

65 6 17 16.3 6 3.1 19.3 6 4.3 16.0 6 3.4 8.8 6 5.0 24.2 6 5.8

68 6 12 15.1 6 3.1 15.2 6 3.8 11.1 6 2.6 15.2 6 5.5 26.4 6 5.4

.31 .07 .001 .001 .001 .07

Beta Exp

P

1.232 .482 1.104

.11 .001 .093

BMI, body mass index; ECW, extracellular water; FTI, fat tissue index; ICW, intracellular water; LTI, lean tissue index; SD, standard deviation; Vwatson 2 Vbis, difference between volume of urea distribution using Watson equation and spectroscopy bioimpedance; ,0, positive difference; .0, negative difference.

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people and in patients, we have to take into account that even our method considered as reference method would not be completely exact. We did not analyze urea kinetic modeling; therefore we could not compare this modeling with our data. The results of the present study recommend an individualized approach in the interpretation of Kt/V in each of our patients. In the same way as dialysis prescriptions are individualized, we must consider each Kt/V in the context of the individual’s body composition. We conclude that body composition affects UDV estimated by Vwatson, thus modifying the Kt/V result. In young patients, who present greater lean tissue and less fat tissue, Kt/V will be underestimated.

Practical Application The present article provides a better and more accurate estimation of dialysis dose in each hemodialysis session when using bioimpedance. UDV measured by bioimpedance may be introduced easily in dialysis monitors to reach this purpose. When performing bioimpedance is not possible, the present article shows a better comprehension of strength and weakness of the Watson equation.

References 1. Daugirdas JT. Simplified equations for monitoring Kt/V, PCRn, eKt/V, and ePCRn. Adv Ren Replace Ther. 1995;2:295-304. 2. Watson PE, Watson ID, Batt RD. Total body water volumes for adult males and females estimated from simple anthropometric measurements. Am J Clin Nutr. 1980;33:27-39. 3. Daugirdas JT, Greene T, Depner TA, Chumlea C, Rocco MJ, Chertow GM, Hemodialysis (HEMO) Study Group. Anthropometrically estimated total body water volumes are larger than modeled urea volume in chronic hemodialysis patients: effects of age, race, and gender. Kidney Int. 2003;64:1108-1119. 4. Wuepper A, Tattersall J, Kraemer M, Wilkie M, Edwards L. Determination of urea distribution volume for Kt/V assessed by conductivity monitoring. Kidney Int. 2003;64:2262-2271. 5. Dumler F. Best method for estimating urea volume of distribution: comparison of single pool variable volume kinetic modeling measurements with bioimpedance and anthropometric methods. ASAIO J. 2004;50:237241. 6. Chazot C, Wabel P, Chamney P, Moissl U, Wieskotten S, Wizemann V. Importance of normohydration for the long-term survival of haemodialysis patients. Nephrol Dial Transplant. 2012;27:2404-2410. 7. Wizemann V, Rode C, Wabel P. Whole-body spectroscopy (BCM) in the assessment of normovolemia in hemodialysis patients. Contrib Nephrol. 2008;161:115-118. 8. Machek P, Jirka T, Moissl U, Chamney P, Wabel P. Guided optimization of fluid status in haemodialysis patients. Nephrol Dial Transplant. 2010;25:538-544.

9. Earthman C, Traughber D, Dobratz J, Howell W. Bioimpedance spectroscopy for clinical assessment of fluid distribution and body cell mass. Nutr Clin Pract. 2007;22:389-405. 10. European Best Practice Guidelines for Hemodialysis: part 1. Nephrol Dial Transplant. 2002;17(suppl 7):S16-S31. 11. van Biesen W, Claes K, Covic A, et al. A multicentric, international matched pair analysis of body composition in peritoneal dialysis versus haemodialysis patients. Nephrol Dial Transplant. 2013;28:2620-2628. 12. Broers NJ, Martens RJ, Cornelis T, et al. Body composition in dialysis patients: a functional assessment of bioimpedance using different prediction models. J Ren Nutr. 2015;25:121-128. 13. Wabel P, Chamney P, Moissl U, Jirka T. Importance of whole-body bioimpedance spectroscopy for the management of fluid balance. Blood Purif. 2009;27:75-80. 14. Dumler F, Kilates C. Body composition analysis by bioelectrical impedance in chronic maintenance dialysis patients: comparisons to the National Health and Nutrition Examination Survey III. J Ren Nutr. 2003;13:166-172. 15. Moissl U, Wabel P, Chamney PW, et al. Validation of a bioimpedance spectroscopy method for the assessment of fat free mass. NDT Plus. 2008;1(suppl 2):ii215. 16. Moissl UM, Wabel P, Chamney PW, et al. Body fluid volume determination via body composition spectroscopy in health and disease. Physiol Meas. 2006;27:921-933. 17. Dumler F. Body composition modifications in patients under low protein diets. J Ren Nutr. 2011;21:76-81. 18. Mattsson S, Thomas BJ. Development of methods for body composition studies. Phys Med Biol. 2006;51:203-228. 19. Lee SY, Gallagher D. Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care. 2008;11:566-572. 20. Lindley EJ, Lopot F. The use of bioimpedance to aid volume assessment in dialysis patients. Kidney Int. 2015;87:240. 21. Lindley EJ, Chamney PW, Wuepper A, Ingles H, Tattersall JE, Will EJ. A comparison of methods for determining urea distribution volume for routine use in on-line monitoring of haemodialysis adequacy. Nephrol Dial Transplant. 2009;24:211-216. 22. Leavey SF, McCullough K, Hecking E, Goodkin D, Port FK, Young EW. Body mass index and mortality in ’healthier’ as compared with ’sicker’ haemodialysis patients: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrol Dial Transplant. 2001;16:2386-2394. 23. Ricks J, Molnar MZ, Kovesdy CP, et al. Racial and ethnic differences in the association of body mass index and survival in maintenance hemodialysis patients. Am J Kidney Dis. 2011;58:574-582. 24. Molnar MZ, Streja E, Kovesdy CP, et al. Associations of body mass index and weight loss with mortality in transplant-waitlisted maintenance hemodialysis patients. Am J Transplant. 2011;11:725-736. 25. Davenport A. Differences in prescribed Kt/Vand delivered haemodialysis dose—why obesity makes a difference to survival for haemodialysis patients when using a ’one size fits all’ Kt/V target. Nephrol Dial Transplant. 2013;28(suppl 4):iv219-iv223. 26. Chamney PW, Wabel P, Moissl UM, et al. A whole-body model to distinguish excess fluid from the hydration of major body tissues. Am J Clin Nutr. 2007;85:80-89. 27. Polaschegg HD. Automatic, noninvasive intradialytic clearance measurement. Int J Artif Organs. 1993;16:185-191.

Body Composition Affects Urea Distribution Volume Estimated by Watson's Formula.

Dialysis machines use the Watson formula (Vwatson) to estimate the urea distribution volume (UDV) to calculate the online Kt/V for each dialysis sessi...
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