Theme-Logo
  • Login
  • Home
  • Course
  • Publication
  • Theses
  • Reports
  • Published books
  • Workshops / Conferences
  • Supervised PhD
  • Supervised MSc
  • Supervised projects
  • Education
  • Language skills
  • Positions
  • Memberships and awards
  • Committees
  • Experience
  • Scientific activites
  • In links
  • Outgoinglinks
  • News
  • Gallery
publication name Azar AT, Wahba KM (2011) Artificial Neural Network for Prediction of Equilibrated Dialysis Dose without Intradialytic Sample. Saudi J Kidney Dis Transpl.; 22(4):705-711.[Impact Factor: 0.77]
Authors
year 2011
keywords
journal
volume Not Available
issue Not Available
pages Not Available
publisher Not Available
Local/International International
Paper Link http://www.ncbi.nlm.nih.gov/pubmed/21743214
Full paper download
Supplementary materials Not Available
Abstract

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 Smye and Daugirdas methods, respectively. The ANN model achieved a correlation coefficient of 0.97 (P

Benha University © 2023 Designed and developed by portal team - Benha University