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Vol. 25, No. 5, 2005
Issue release date: September–October 2005
Am J Nephrol 2005;25:507–513

Applying an Artificial Neural Network to Predict Total Body Water in Hemodialysis Patients

Chiu J.-S. · Chong C.-F. · Lin Y.-F. · Wu C.-C. · Wang Y.-F. · Li Y.-C.
aDepartment of Nuclear Medicine, Buddhist Dalin Tzu Chi General Hospital, Chiayi County; bSchool of Medicine, Fu Jen Catholic University, Taipei County; cDivision of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, and dGraduate Institute of Medical Informatics, Wanfang Hospital, Taipei Medical University, Taipei City, Taiwan

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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.

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  1. Martinoli R, Mohamed EI, Maiolo C, Cianci R, Denoth F, Salvadori S, Iacopino L: Total body water estimation using bioelectrical impedance: a meta-analysis of the data available in the literature. Acta Diabetol 2003;40(suppl 1):S203–S206.

    External Resources

  2. Chong CF, Li YC, Wang TL, Chang H: Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model. AMIA Annu Symp Proc 2003, pp 160–164.
  3. Forsstrom JJ, Dalton KJ: Artificial neural networks for decision support in clinical medicine. Ann Med 1995;27:509–517.
  4. Gabutti L, Burnier M, Mombelli G, Male F, Pellegrini L, Marone C: Usefulness of artificial neural networks to predict follow-up dietary protein intake in hemodialysis patients. Kidney Int 2004;66:399–407.
  5. Gabutti L, Vadilonga D, Mombelli G, Burnier M, Marone C: Artificial neural networks improve the prediction of Kt/V, follow-up dietary protein intake and hypotension risk in haemodialysis patients. Nephrol Dial Transplant 2004;19:1204–1211.
  6. Fernandez EA, Valtuille R, Willshaw P, Perazzo CA: Using artificial intelligence to predict the equilibrated postdialysis blood urea concentration. Blood Purif 2001;19:271–285.
  7. Akl AI, Sobh MA, Enab YM, Tattersall J: Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy. Am J Kidney Dis 2001;38:1277–1283.
  8. Guh JY, Yang CY, Yang JM, Chen LM, Lai YH: Prediction of equilibrated postdialysis BUN by an artificial neural network in high-efficiency hemodialysis. Am J Kidney Dis 1998;31:638–646.
  9. Rajimehr R, Farsiu S, Kouhsari LM, Bidari A, Lucas C, Yousefian S, Bahrami F: Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks. Lupus 2002;11:485–492.
  10. Goldfarb-Rumyantzev AS, Pappas L: Prediction of renal insufficiency in Pima Indians with nephropathy of type 2 diabetes mellitus. Am J Kidney Dis 2002;40:252–264.
  11. Dimitrov BD, Ruggenenti P, Stefanov R, Perna A, Remuzzi G: Chronic nephropathies: individual risk for progression to end-stage renal failure as predicted by an integrated probabilistic model. Nephron Clin Pract 2003;95:c47–c59.

    External Resources

  12. Van BW, Sieben G, Lameire N, Vanholder R: Application of Kohonen neural networks for the non-morphological distinction between glomerular and tubular renal disease. Nephrol Dial Transplant 1998;13:59–66.
  13. Geddes CC, Fox JG, Allison ME, Boulton-Jones JM, Simpson K: An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. Nephrol Dial Transplant 1998;13:67–71.
  14. Brier ME, Ray PC, Klein JB: Prediction of delayed renal allograft function using an artificial neural network. Nephrol Dial Transplant 2003;18:2655–2659.
  15. Sheppard D, McPhee D, Darke C, Shrethra B, Moore R, Jurewitz A, Gray A: Predicting cytomegalovirus disease after renal transplantation: an artificial neural network approach. Int J Med Inform 1999;54:55–76.
  16. Abdolmaleki P, Movhead M, Taniguchi RI, Masuda K, Buadu LD: Evaluation of complications of kidney transplantation using artificial neural networks. Nucl Med Commun 1997;18:623–630.
  17. Watson PE, Watson ID, Batt RD: Total body water volumes for adult males and females estimated from simple anthropometric measurements. Am J Clin Nutr 1980;33:27–39.
  18. Hume R, Weyers E: Relationship between total body water and surface area in normal and obese subjects. J Clin Pathol 1971;24:234–238.
  19. Chertow GM, Lazarus JM, Lew NL, Ma L, Lowrie EG: Development of a population-specific regression equation to estimate total body water in hemodialysis patients. Kidney Int 1997;51:1578–1582.
  20. Lee SW, Song JH, Kim GA, Lee KJ, Kim MJ: Assessment of total body water from anthropometry-based equations using bioelectrical impedance as reference in Korean adult control and haemodialysis subjects. Nephrol Dial Transplant 2001;16:91–97.
  21. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel GJ, Lilienthal HB, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols WJ, Pichard C: Bioelectrical impedance analysis. II. Utilization in clinical practice. Clin Nutr 2004;23:1430–1453.
  22. Das A, Ben-Menachem T, Cooper GS, Chak A, Sivak MV Jr, Gonet JA, Wong RC: Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet 2003;362:1261–1266.
  23. Banerjee R, Das A, Ghoshal UC, Sinha M: Predicting mortality in patients with cirrhosis of liver with application of neural network technology. J Gastroenterol Hepatol 2003;18:1054–1060.
  24. Szpurek D, Moszynski R, Smolen A, Sajdak S: Artificial neural network computer prediction of ovarian malignancy in women with adnexal masses. Int J Gynaecol Obstet 2005;89:108–113.
  25. Poon TC, Hui AY, Chan HL, Ang IL, Chow SM, Wong N, Sung JJ: Prediction of liver fibrosis and cirrhosis in chronic hepatitis B infection by serum proteomic fingerprinting: a pilot study. Clin Chem 2005;51:328–335.
  26. Guan P, Huang DS, Zhou BS: Forecasting model for the incidence of hepatitis A based on artificial neural network. World J Gastroenterol 2004;10:3579–3582.

    External Resources

  27. Krouwer JS, Monti KL: A simple, graphical method to evaluate laboratory assays. Eur J Clin Chem Clin Biochem 1995;33:525–527.
  28. Cooper BA, Aslani A, Ryan M, Zhu FY, Ibels LS, Allen BJ, Pollock CA: Comparing different methods of assessing body composition in end-stage renal failure. Kidney Int 2000;58:408–416.
  29. Ho LT, Kushner RF, Schoeller DA, Gudivaka R, Spiegel DM: Bioimpedance analysis of total body water in hemodialysis patients. Kidney Int 1994;46:1438–1442.
  30. Cha K, Chertow GM, Gonzalez J, Lazarus JM, Wilmore DW: Multifrequency bioelectrical impedance estimates the distribution of body water. J Appl Physiol 1995;79:1316–1319.
  31. Linder R, Mohamed EI, De LA, Poppl SJ: The capabilities of artificial neural networks in body composition research. Acta Diabetol 2003;40(suppl 1):S9–S14.

    External Resources

  32. Di Iorio BR, Scalfi L, Terracciano V, Bellizzi V: A systematic evaluation of bioelectrical impedance measurement after hemodialysis session. Kidney Int 2004;65:2435–2440.

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