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


CONCLUSIONS
ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients.
Chiu
Jainn-Shiun
JS
Department of Nuclear Medicine, Buddhist Dalin Tzu Chi General Hospital, Chiayi County, Taiwan.
Chong
Chee-Fah
CF
Lin
Yuh-Feng
YF
Wu
Chia-Chao
CC
Wang
Yuh-Feng
YF
Li
Yu-Chuan
YC
D000328:Adult; D000368:Aged; D000886:Anthropometry / Q000379:methods; D001834:Body Water / Q000378:metabolism*; D017097:Electric Impedance; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D006435:Renal Dialysis