Artificial neural networks improve the prediction of Kt/V, follow-up dietary protein intake and hypotension risk in haemodialysis patients

Abstract

Background: Artificial neural networks (ANN) represent a promising alternative to classical statistical and mathematical methods to solve multidimensional non-linear problems. The aim of the study was to compare the performance of ANN in predicting the dialysis quality (Kt/V), the follow-up dietary protein intake and the risk of intradialytic hypotension in haemodialysis patients with that predicted by experienced nephrologists.

Methods: A combined retrospective and prospective observational study was performed in two Swiss dialysis units (80 chronic haemodialysis patients, 480 monthly clinical observations and biochemical test results). Using mathematical models based on linear and logistic regressions as background, ANN were built and then prospectively compared with the ability of six experienced nephrologists to predict the Kt/V and the follow-up protein catabolic rate (PCR) and to detect a Kt/V < 1.30, a follow-up PCR < 1.00 g/kg/day and the occurrence of hypotension.

Results: ANN compared with nephrologists gave a more accurate correlation between estimated and calculated Kt/V and follow-up PCR (P<0.001). The same superiority of ANN was also seen in the ability to detect a Kt/V < 1.30, a follow-up PCR < 1.00 g/kg/day and the occurrence of hypotension expressed as a percentage of correct answers, sensitivity, specificity and predictivity.

Conclusions: The use of ANN significantly improves the ability of experienced nephrologists to estimate the Kt/V and the follow-up PCR and to detect a Kt/V < 1.30, a follow-up PCR < 1.00 g/kg/day and the occurrence of intradialytic hypotension.