Prof. Ahmad Taher Azar :: Publications:

Background. Determination of the adequacy of dialysis is a routine but crucial procedure in patient evaluation. The total dialysis dose, expressed as Kt/V, has been widely recognized to be a major determinant of morbidity and mortality in hemodialysis patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) post-dialysis blood urea or equilibrated Kt/V results in an inadequate hemodialysis prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated post-dialysis blood urea (eqU) is therefore crucial in order to estimate the equilibrated (true) Kt/V. Methods. In this work a supervised neural network was proposed to predict the eqU at 60 min after the end of hemodialysis. Data from 133 patients from dialysis unit were analyzed. Equilibrated post-dialysis blood urea was measured 60 min after each hemodialysis session to calcu¬late URReq and Kt/Veq Results. With this approach we achieve a mean difference error of 0.22 ± 7.71 mg/ml (mean % error: 1.88 ± 13.46) on the eqU prediction and a mean difference error for eqKt/V of –0.01 ± 0.15 (mean % error: –0.95 ± 14.73). The equilibrated Kt/V estimated with the eqU calculated using the Smye formula is not appropriate because it showed a great dispersion. The Daugirdas double-pool Kt/V estimation formula appeared to be accurate and in agreement with the results of the HEMO study. Conclusion. The use of the ANN urea estimation yields accurate results when used to calculate eqKt/V. Artificial neural networks are very promising tools for the analysis of nephrological data, improving the performance and enabling another way of multivariate analysis.