Artificial Neural Network for accurate prediction of post-dialysis urea rebound

Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed 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 (C_eq) is therefore crucial in order to estimate the equilibrated (true) Kt/V. Measuring post dialysis urea rebound (PDUR) requires a 30- or 60-minute post-dialysis sampling, which is inconvenient. In this work a supervised Artificial Neural Network (ANN) is proposed for predicting equilibrated urea concentration taken at 30 min after the end of the hemodialysis (HD) session in order to predict PDUR. The advantage of ANN approach is that it doesn´t require 30-60-minute post-dialysis urea sample. This approach is compared experimentally with other traditional methods for predicting equilibrated urea concentration (C_eq), PDUR and equilibrated dialysis dose (_eqKt/V). The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms other traditional urea kinetic models (UKM) and the previous work using ANN.