scholarly journals Bioelectrical impedance analysis and assessment of body composition in end-stage renal disease

2003 ◽  
Vol 18 (9) ◽  
pp. 1943-b-1944
Author(s):  
B. R. Di Iorio
Keyword(s):  
Body Composition ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Bioelectrical Impedance Analysis ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Stage Renal Disease ◽  
End Stage

scholarly journals Correlation of bioelectrical impedance analysis phase angle with changes in oxidative stress on end-stage renal disease patients, before, during, and after dialysis

Renal Failure ◽  
2016 ◽  
Vol 38 (5) ◽  
pp. 738-743 ◽  
Author(s):  
Andreas Zouridakis ◽  
Yannis V. Simos ◽  
Ioannis I. Verginadis ◽  
Konstantinos Charalabopoulos ◽  
Vasilios Ragos ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Renal Disease ◽  
Phase Angle ◽  
End Stage Renal Disease ◽  
Bioelectrical Impedance Analysis ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Analysis Phase ◽  
Stage Renal Disease ◽  
End Stage

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Yan Yu ◽  
Yinghua Cai ◽  
Yuan Zhang ◽  
...  
Keyword(s):  
Renal Disease ◽  
End Stage Renal Disease ◽  
Rbf Neural Network ◽  
Bioelectrical Impedance ◽  
Dry Weight ◽  
Impedance Analysis ◽  
Artificial Intelligence Technology ◽  
Stage Renal Disease ◽  
Computed Tomography Image ◽  
End Stage

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.

scholarly journals Body Composition and Physical Activity in End-Stage Renal Disease

2007 ◽  
Vol 17 (3) ◽  
pp. 196-204 ◽  
Author(s):  
Karen M. Majchrzak ◽  
Lara B. Pupim ◽  
Mary Sundell ◽  
T. Alp Ikizler
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Stage Renal Disease ◽  
End Stage

Transient Elastography in End-Stage Renal Disease Patients on Hemodialysis: The Effect of Net Fluid Withdrawal

2015 ◽  
Vol 40 (3) ◽  
pp. 256-259 ◽  
Author(s):  
Narongrit Khunpakdee ◽  
Kullapong Jayanama ◽  
Piyaporn Kaewdoung ◽  
Kwannapa Promson ◽  
Sasivimol Rattanasiri ◽  
...  
Keyword(s):  
Renal Disease ◽  
End Stage Renal Disease ◽  
Transient Elastography ◽  
Liver Stiffness ◽  
Bioelectrical Impedance ◽  
Cross Sectional ◽  
Before And After ◽  
Stage Renal Disease ◽  
End Stage ◽  
The Impact

Background/Aims: The impact of volume status on liver stiffness measurement (LSM) as measured by transient elastography (TE) as in end-stage renal disease (ESRD) was unclear. We evaluated LSM before and after hemodialysis (HD) and identified the associated factors if the difference of LSM existed. Methods: A cross-sectional study was conducted in ESRD patients on regular HD. Subjects underwent TE and bioelectrical impedance before and after HD. Results: Thirty-six patients were enrolled. Mean (SD) net fluid withdrawal volume (NFWV) per session was 2.55 (0.9) l. Median (range) pre- and post-HD LSMs were 5.38 (2.8-25.7) and 5.4 (2.8-26) kPa, respectively (p = 0.712). Mean differences of pre- and post-HD LSMs correlated with NFWV (r = 0.49, 95% CI 0.19-0.71, p = 0.002). Conclusion: In ESRD on regular HD, LSM is not affected by HD. TE can be done before or after HD with similar results. However, fluid excess at pre-HD can cause inaccurately high LSM.

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