Bioimpedance analysis of total body water in hemodialysis patients

Ho, Lam T.; Kushner, Robert F.; Schoeller, Dale A.; Gudivaka, Rani; Spiegel, David

doi:10.1038/ki.1994.416

Cited by 98 publications

(42 citation statements)

References 10 publications

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“…These equations had as dependent variables L 2 aR 50 with or without weight, gender and age. Multifrequency BIA generated new equations for ECW and TBW with regression equations based on L 2 aR x at ®xed frequencies (x), in which x was low (1 ± 5 kHz) for ECW 7,8,16 and high ( b 50 kHz) for TBW predictions, 7,8,16,17 or with the extrapolated resistance R ECW and R TBW . 12,13 The software of the Xitron 4000B model we used contains volume prediction equations-based emulsion sciences, a theory developed by Hanai.…”

Section: Volume Equationsmentioning

confidence: 99%

Validation of bio-impedance spectroscopy: Effects of degree of obesity and ways of calculating volumes from measured resistance values

Cox-Reijven¹,

Soeters

2000

Int J Obes

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BACKGROUND: Bioelectrical-impedance spectroscopy (BIS) is a very attractive method for body composition measurements in a clinical setting. However, validation studies often yield different results. This can partly be explained by the different approaches used to transform measured resistance values into body compartments. OBJECTIVE: The aim of this study was to compare the linear regression (LR) method with the Hanai Mixture theory (HM). Secondly, the effect of degree of overweight on the accuracy of BIS was analysed. DESIGN: In 90 people (10 M, 80 F; body mass index (BMI) 23 ± 62 kgam 2 ) total body water (TBW) and extracellular water (ECW) were measured by deuterium and NaBr dilution methods, respectively, and by BIS. Resistance values of ECW (R ECW ) and TBW (R TBW ) were used for volume calculations. Data of half the group were used for LR based on L 2 aR (L length, R resistance) to predict TBW and ECW and to calculate the constants used in the HM (k ECW ), k p ). Prediction equations and constants were cross-validated in Group 2. RESULTS: Bland and Altman analysis showed that the LR method underestimated TBW by 1.1 l (P`0.005) and ECW by 1.1 l (P`0.005). The HM approach underestimated ECW by 0.8 l (P`0.005). The correlations with the dilution methods and the SEEs for TBW and ECW were comparable for the two approaches. The prediction error of BIS for TBW and ECW correlated with BMI. The constant k ECW , and the speci®c resistivities of the ECW and intracellular water (ICW) p ECW and p ICW were also correlated with BMI. CONCLUSIONS: The mixture approach is slightly more accurate than linear regression, but not sensitive enough for clinical use. The constants used in the HM model are not constants in a population with a wide variation in degree of overweight. The physical causes of the correlation between BMI and constants used in the model should be studied further in order to optimize the mixture model.

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Section: Volume Equationsmentioning

confidence: 99%

Validation of bio-impedance spectroscopy: Effects of degree of obesity and ways of calculating volumes from measured resistance values

Cox-Reijven¹,

Soeters

2000

Int J Obes

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“…Although MF-BIA cannot measure TBW directly as do radiotracer techniques, several studies support its reliability in hemodialysis patients [28,29,30]. We constructed an ANN to predict TBW with data from demographic and anthropometric variables and relevant clinical history – without using radiotracers or MF-BIA.…”

Section: Discussionmentioning

confidence: 99%

Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients

et al. 2005

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Background: Estimating total body water (TBW) is crucial in determining dry weight and dialytic dose for hemodialysis patients. Several anthropometric equations have been used to predict TBW, but a more accurate method is needed. We developed an artificial neural network (ANN) to predict TBW in hemodialysis patients. Methods: Demographic data, anthropometric measurements, and multifrequency bioelectrical impedance analysis (MF-BIA) were investigated in 54 patients. TBW measured by MF-BIA (TBW-BIA) was the reference. The predictive value of TBW based on ANN and five anthropometric equations (58% of actual body weight, Watson formula, Hume formula, Chertow formula, and Lee formula) was evaluated. Results: Predictive TBW values derived from anthropometric equations were significantly higher than TBW-BIA (31.341 ± 6.033 liters). The only non-significant difference was between TBW-ANN (31.468 ± 5.301 liters) and TBW-BIA (p = 0.639). ANN had the strongest Pearson’s correlation coefficient (0.911) and smallest root mean square error (2.480); its peak centered most closely to zero with the shortest tails in an empirical cumulative distribution plot when compared with the other five equations. Conclusion: ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients.

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“…The parallel model described by Fricke takes into account the conductive elements exhibiting a certain degree of resistance connected in parallel and isolated by a cell membrane. This method has been validated by Van Loan [29,30,31]. At high and low frequencies, the Hanaï theory takes into account the presence of non-conductive elements connected in parallel.…”

Section: Theories Usedmentioning

confidence: 99%

“…Much research has been devoted to the determination of body hydration status and fluid compartments, particularly in hemodialysis patients [14]. Work has focused on various physiological parameters with different modalities to study individual segments of the body or the whole body [15,16,17] using multiple or unique frequencies [18].…”

Section: Usefulness Of Bioimpedance For the Kidney Disease Patientmentioning

confidence: 99%

Bioimpedance Measurement in the Kidney Disease Patient

Cridlig¹,

Nadi²,

Kessler³

2011

Technical Problems in Patients on Hemodialysis

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Bioimpedance analysis of total body water in hemodialysis patients

Cited by 98 publications

References 10 publications

Validation of bio-impedance spectroscopy: Effects of degree of obesity and ways of calculating volumes from measured resistance values

Validation of bio-impedance spectroscopy: Effects of degree of obesity and ways of calculating volumes from measured resistance values

Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients

Bioimpedance Measurement in the Kidney Disease Patient

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