2001
DOI: 10.1159/000046955
|View full text |Cite
|
Sign up to set email alerts
|

Using Artificial Intelligence to Predict the Equilibrated Postdialysis Blood Urea Concentration

Abstract: Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate hemodialysis prescription, with predictably poor clinical outcomes for th… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

1
22
0

Year Published

2002
2002
2013
2013

Publication Types

Select...
5
4

Relationship

1
8

Authors

Journals

citations
Cited by 27 publications
(23 citation statements)
references
References 16 publications
1
22
0
Order By: Relevance
“…ANN has the advantage of recognizing relationships between inputs (data from cases) and outputs (known outcomes) that may not be obvious with conventional statistical methods [3]. Furthermore, ANNs can improve accuracy through learning algorithms and have been applied to evaluation of dialytic adequacy [4,5,6,7,8], diagnosis and prognosis of nephropathies [9,10,11,12,13], and issues of renal transplantation [14,15,16]. …”
Section: Introductionmentioning
confidence: 99%
“…ANN has the advantage of recognizing relationships between inputs (data from cases) and outputs (known outcomes) that may not be obvious with conventional statistical methods [3]. Furthermore, ANNs can improve accuracy through learning algorithms and have been applied to evaluation of dialytic adequacy [4,5,6,7,8], diagnosis and prognosis of nephropathies [9,10,11,12,13], and issues of renal transplantation [14,15,16]. …”
Section: Introductionmentioning
confidence: 99%
“…We showed that by means of linear models we were able to build bedside equations that can be easily implemented in any calculator or electronic spreadsheet such as Excel®. All the presented methods performed better than traditional methods (Smye et al, 1999) over the same data (Fernández et al, 2001) suggesting the appropriateness of the simple linear approaches. In addition, each hemodialysis centre can build its own predictor based on its own patient population by following the described process or implementing the accompanying source code (see appendix).…”
Section: Discussionmentioning
confidence: 75%
“…In the case of urea, the most popular target biomarker, its equilibrium concentration is reached between 30 and 60 min after the end of the session. Most hemodialysis adequacy indices used in practice are based on the molecular concentration at the end of the session, 5,14,15,23,[27][28][29] although this could lead to inadequate hemodialysis (HD) dose estimation. By contrast, the use of an equilibrated concentration of the biomarker produces a better evaluation of treatment since it is independent of marker kinetic behavior.…”
Section: Introductionmentioning
confidence: 99%
“…By contrast, the use of an equilibrated concentration of the biomarker produces a better evaluation of treatment since it is independent of marker kinetic behavior. [14][15][16]27 In clinical practice, waiting for the achievement of the equilibrated urea concentration is usually impractical. Therefore, the availability of a model able to predict subject-specific equilibrated concentration will be very helpful.…”
Section: Introductionmentioning
confidence: 99%