Applying an artificial neural network to predict total body water in hemodialysis patients

Jainn Shiun Chiu, Chee Fah Chong, Yuh Feng Lin, Chia Chao Wu, Yuh Feng Wang, Yu Chuan Li

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)507-513
Number of pages7
JournalAmerican Journal of Nephrology
Volume25
Issue number5
DOIs
Publication statusPublished - Sep 2005

Keywords

  • Anthropometry
  • Bioelectrical impedance
  • Body water
  • Hemodialysis
  • Neural network

ASJC Scopus subject areas

  • Nephrology

Fingerprint Dive into the research topics of 'Applying an artificial neural network to predict total body water in hemodialysis patients'. Together they form a unique fingerprint.

Cite this