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Dialysate-side urea kinetics. Neural network predicts dialysis dose during dialysis.
Fernández, E A; Valtuille, R; Willshaw, P; Perazzo, C A.
Affiliation
  • Fernández EA; Bioengineering Department, Favaloro University, Buenos Aires, Argentina. elmerfer@favaloro.edu.ar
Med Biol Eng Comput ; 41(4): 392-6, 2003 Jul.
Article in En | MEDLINE | ID: mdl-12892360
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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Collection: 01-internacional Database: MEDLINE Main subject: Urea / Renal Dialysis / Neural Networks, Computer / Monitoring, Physiologic Type of study: Prognostic_studies / Risk_factors_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: Med Biol Eng Comput Year: 2003 Document type: Article Affiliation country: Argentina
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Collection: 01-internacional Database: MEDLINE Main subject: Urea / Renal Dialysis / Neural Networks, Computer / Monitoring, Physiologic Type of study: Prognostic_studies / Risk_factors_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: Med Biol Eng Comput Year: 2003 Document type: Article Affiliation country: Argentina