Prediction of equilibrated postdialysis BUN by an artificial neural network in high-efficiency hemodialysis

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 (C-eq) and equilibrated Kt/V (eKt/V-60) by using both predialysis, postdialysis, and low-flow postdialysis BUN. The results were compared to a Smye formula to predict C-eq and a Daugirdas' formula (eKt/V-30) to predict eKt/V-60. 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 C-eq, 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/V-60, 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 C-eq and eKt/V-60 regardless of the degree of urea rebound. (C) 1998 by the National Kidney Foundation, Inc.