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

研究成果: 雜誌貢獻文章同行評審

13 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
頁(從 - 到)507-513
頁數7
期刊American Journal of Nephrology
25
發行號5
DOIs
出版狀態已發佈 - 九月 2005

ASJC Scopus subject areas

  • Nephrology

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