Artificial neural network for prediction of equilibrated dialysis dose without intradialytic sample

الملخص EN

Post-dialysis urea rebound (PDUR) is a cause of Kt/V overestimation when it is calculated from pre-dialysis and the immediate post-dialysis blood urea collections.

Measuring PDUR requires a 30-or 60-min post-dialysis sampling, which is inconvenient.

In this study, a supervised neural network was proposed to predict the equilibrated urea (C eq) at 60 min after the end of hemodialysis (HD).

Data of 150 patients from a dialysis unit were analyzed.

C eq was measured 60 min after each HD session to calculate PDUR, equilibrated urea reduction rate eq (URR), and ( eq Kt / V).

The mean percentage of true urea rebound measured after 60 min of HD session was 19.6 ± 10.7.

The mean urea rebound observed from the artificial neural network (ANN) was 18.6 ± 13.9%, while the means were 24.8 ± 14.1% and 21.3 ± 3.49% using Skye and Daugirdas methods, respectively.

The ANN model achieved a correlation coefficient of 0.97 (P <0.0001), while the Skye and Daugirdas methods yielded R = 0.81 and 0.93, respectively (P <0.0001); the errors of the Skye method were larger than those of the other methods and resulted in a considerable bias in all cases, while the predictive accuracy for ( eq Kt/V) 60 was equally good by the Daugirdas' formula and the ANN .

We conclude that the use of the ANN urea estimation yields accurate results when used to calculate (eq Kt / V).