Literature DB >> 12892360

Dialysate-side urea kinetics. Neural network predicts dialysis dose during dialysis.

E A Fernández1, R Valtuille, P Willshaw, C A Perazzo.   

Abstract

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 recognised to be a major determinant of morbidity and mortality in haemodialysed patients. Many different factors influence the correct determination of Kt/V, such as urea sequestration in different body compartments, access and cardiopulmonary recirculation. These factors are responsible for urea rebound after the end of the haemodialysis session, causing poor Kt/V estimation. There are many techniques that try to overcome this problem. Some of them use analysis of blood-side urea samples, and, in recent years, on-line urea monitors have become available to calculate haemodialysis dose from dialysate-side urea kinetics. All these methods require waiting until the end of the session to calculate the Kt/V dose. In this work, a neural network (NN) method is presented for early prediction of the Kt/V dose. Two different portions of the dialysate urea concentration-time profile (provided by an on-line urea monitor) were analysed: the entire curve A and the first half B, using an NN to predict the Kt/V and compare this with that provided by the monitor. The NN was able to predict Kt/V is the middle of the 4h session (B data) without a significant increase in the percentage error (B data: 6.69% +/- 2.46%; A data: 5.58% +/- 8.77%, mean +/- SD) compared with the monitor Kt/V.

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Year:  2003        PMID: 12892360     DOI: 10.1007/bf02348080

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  18 in total

1.  Using artificial intelligence to predict the equilibrated postdialysis blood urea concentration.

Authors:  E A Fernández; R Valtuille; P Willshaw; C A Perazzo
Journal:  Blood Purif       Date:  2001       Impact factor: 2.614

Review 2.  The role of technology in hemodialysis.

Authors:  C Ronco; P M Ghezzi; G La Greca
Journal:  J Nephrol       Date:  1999 Jul-Aug       Impact factor: 3.902

3.  Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.

Authors:  A I Akl; M A Sobh; Y M Enab; J Tattersall
Journal:  Am J Kidney Dis       Date:  2001-12       Impact factor: 8.860

4.  Simplified equations for monitoring Kt/V, PCRn, eKt/V, and ePCRn.

Authors:  J T Daugirdas
Journal:  Adv Ren Replace Ther       Date:  1995-10

Review 5.  Dialysate-based kinetic modeling.

Authors:  L J Garred
Journal:  Adv Ren Replace Ther       Date:  1995-10

6.  Urea reduction ratio that considers effects of ultrafiltration and intradialytic urea generation.

Authors:  Y L Cheng; K S Choi; K F Chau; C S Li; C U Yung; A W Yu; K K Wong
Journal:  Am J Kidney Dis       Date:  2001-03       Impact factor: 8.860

7.  Comparison of methods to predict equilibrated Kt/V in the HEMO Pilot Study.

Authors:  J T Daugirdas; T A Depner; F A Gotch; T Greene; P Keshaviah; N W Levin; G Schulman
Journal:  Kidney Int       Date:  1997-11       Impact factor: 10.612

8.  Multicenter clinical validation of an on-line monitor of dialysis adequacy.

Authors:  T A Depner; P R Keshaviah; J P Ebben; P F Emerson; A J Collins; K K Jindal; A R Nissenson; J M Lazarus; K Pu
Journal:  J Am Soc Nephrol       Date:  1996-03       Impact factor: 10.121

9.  Variation in blood sample collection for determination of hemodialysis adequacy. Council on Renal Nutrition National Research Question Collaborative Study Group.

Authors:  J A Beto; V K Bansal; T S Ing; J T Daugirdas
Journal:  Am J Kidney Dis       Date:  1998-01       Impact factor: 8.860

10.  Biostat 1000 and Daugirdas blood-based hemodialysis quantification: agreement and reproducibility.

Authors:  M R Marshall; P Santamaria; J F Collins
Journal:  Am J Kidney Dis       Date:  1998-06       Impact factor: 8.860

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