title: Prediction of equilibrated postdialysis BUN by an artificial neural network in high-efficiency hemodialysis.
abstract: In urea kinetic modeling, postdialysis blood urea nitrogen (BUN) is usually underestimated with an overestimation of the Kt/V especially in high-efficiency hemodialysis (HD). Thus, an artificial neural network (ANN) was used to predict the equilibrated BUN (Ceq) and equilibrated Kt/V (eKt/V60) by using both predialysis, postdialysis, and low-flow postdialysis BUN. The results were compared to a Smye formula to predict Ceq and a Daugirdas' formula (eKt/V30) to predict eKt/V60. Seventy-four patients on high-efficiency or high-flux HD were recruited. Their mean urea rebound was 28.6+/-2%. Patients were divided into a "training" set (n = 40) and a validation set (n = 34) for the ANN. Their status was exchanged later, and the two results were pooled. In the prediction of Ceq, both Smye formula and low-flow ANN were equally highly accurate. In patients with a high urea rebound (>30%), although Smye formula lost its accuracy, low-flow ANN remained accurate. In the prediction of eKt/V60, both Daugirdas' formula and low-flow ANN were equally accurate, although the Smye formula was not so accurate. In patients with a high urea rebound, although both Smye and Daugirdas' formulas lost their accuracy, low-flow ANN remained accurate. We concluded that low-flow ANN can accurately predict both Ceq and eKt/V60 regardless of the degree of urea rebound.
authors.affiliation: Department of Internal Medicine, Kaohsiung Medical College, Taiwan, ROC.
authors.forename: J Y
authors.initials: JY
authors.lastname: Guh
authors.forename: C Y
authors.initials: CY
authors.lastname: Yang
authors.forename: J M
authors.initials: JM
authors.lastname: Yang
authors.forename: L M
authors.initials: LM
authors.lastname: Chen
authors.forename: Y H
authors.initials: YH
authors.lastname: Lai
mesh_terms: D001806:Blood Urea Nitrogen; D005260:Female; D006801:Humans; D007676:Kidney Failure, Chronic / Q000097:blood / Q000628:therapy; D007700:Kinetics; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011379:Prognosis; D006435:Renal Dialysis / Q000379:methods / Q000706:statistics & numerical data