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. Conclusion: ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients. Copyright (C) 2005 S. Karger AG, Basel.