Comparison of standard and artificial neural network estimators of hemodialysis adequacy

The National Kidney Foundation and the European Renal Association recommend routine measurement of hemodialysis (HD) dose and have set standards for adequacy of treatment. We compare the results of five methods for HD dose estimation, classifying each result as adequate or inadequate on the basis of...

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Detalles Bibliográficos
Autores principales: Fernández, Elmer Andrés, Valtuille, Rodolfo, Presedo, Jesús, Willshaw, Peter
Formato: Artículo
Lenguaje:Español
Publicado: 2005
Materias:
R Medicina (General)
Acceso en línea:http://pa.bibdigital.ucc.edu.ar/3991/1/A_Fern%C3%A1ndez_Valtuille_Presedo_Willshaw.pdf
Aporte de:
Producción Académica Universidad Católica de Córdoba (UCCor) de Universidad Católica de Córdoba
id I38-R144-3991
record_format dspace
spelling I38-R144-39912025-04-10T17:59:29Z http://pa.bibdigital.ucc.edu.ar/3991/ Comparison of standard and artificial neural network estimators of hemodialysis adequacy Fernández, Elmer Andrés Valtuille, Rodolfo Presedo, Jesús Willshaw, Peter R Medicina (General) The National Kidney Foundation and the European Renal Association recommend routine measurement of hemodialysis (HD) dose and have set standards for adequacy of treatment. We compare the results of five methods for HD dose estimation, classifying each result as adequate or inadequate on the basis of equilibrated (eq) Urea Reduction Ratio (URReq) ≥ 65% or Kt/V eq ≥ 1.2, to assess the accuracy of each method as a diagnostic tool. Data from 113 patients from two different dialysis units were analyzed. Equilibrated postdialysis blood urea was measured 60 min after each hemodialysis session to calculate URReq and Kt/Veq, considered as gold standard indexes (GSI). URR and Kt/V were estimated by using the Smye formula, an artificial neural network (ANN), modified URR, the second generation Kt/V Daugirdas formula, and standard indexes based on postdialysis urea, then compared to the GSI. For URR, best estimator was ANN (error rate: ER% = 12.70), followed by modified URR (ER% = 17.46%), the Smye (ER% = 22.22), and standard URR (ER% = 23.81). For Kt/V, the Daugirdas equation and the ANN were similar (ER% = 9.52 and 11.11). The single-pool Kt/V (Kt/Vsp) ≥ 1.4 (ERA recommended) produced an ER% = 7.94 and a false positive rate (FPR%) equal to that shown by the ANN (FPR% = 3.17). According to the current threshold limits for HD dose adequacy, the ANN was a reliable and accurate tool for URR monitoring, better than the Smye and the modified URR methods. The use of the ANN urea estimation yields accurate results when used to calculate Kt/V. The Kt/Vsp with an adequacy threshold of 1.4 is a superior approach for HD adequacy monitoring, suggesting that the current adequacy limits should be reviewed for both URR and Kt/V. 2005-02-31 info:eu-repo/semantics/article info:eu-repo/semantics/closedAccess application/pdf spa http://pa.bibdigital.ucc.edu.ar/3991/1/A_Fern%C3%A1ndez_Valtuille_Presedo_Willshaw.pdf Fernández, Elmer Andrés ORCID: https://orcid.org/0000-0002-4711-8634 <https://orcid.org/0000-0002-4711-8634>, Valtuille, Rodolfo ORCID: https://orcid.org/0000-0003-2434-9226 <https://orcid.org/0000-0003-2434-9226>, Presedo, Jesús and Willshaw, Peter (2005) Comparison of standard and artificial neural network estimators of hemodialysis adequacy. Artificial Organs, 29 (2). pp. 159-165. ISSN 0160-564X info:eu-repo/semantics/altIdentifier/doi/10.1111/j.1525-1594.2005.29027.x
institution Universidad Católica de Córdoba
institution_str I-38
repository_str R-144
collection Producción Académica Universidad Católica de Córdoba (UCCor)
language Español
orig_language_str_mv spa
topic R Medicina (General)
spellingShingle R Medicina (General)
Fernández, Elmer Andrés
Valtuille, Rodolfo
Presedo, Jesús
Willshaw, Peter
Comparison of standard and artificial neural network estimators of hemodialysis adequacy
topic_facet R Medicina (General)
description The National Kidney Foundation and the European Renal Association recommend routine measurement of hemodialysis (HD) dose and have set standards for adequacy of treatment. We compare the results of five methods for HD dose estimation, classifying each result as adequate or inadequate on the basis of equilibrated (eq) Urea Reduction Ratio (URReq) ≥ 65% or Kt/V eq ≥ 1.2, to assess the accuracy of each method as a diagnostic tool. Data from 113 patients from two different dialysis units were analyzed. Equilibrated postdialysis blood urea was measured 60 min after each hemodialysis session to calculate URReq and Kt/Veq, considered as gold standard indexes (GSI). URR and Kt/V were estimated by using the Smye formula, an artificial neural network (ANN), modified URR, the second generation Kt/V Daugirdas formula, and standard indexes based on postdialysis urea, then compared to the GSI. For URR, best estimator was ANN (error rate: ER% = 12.70), followed by modified URR (ER% = 17.46%), the Smye (ER% = 22.22), and standard URR (ER% = 23.81). For Kt/V, the Daugirdas equation and the ANN were similar (ER% = 9.52 and 11.11). The single-pool Kt/V (Kt/Vsp) ≥ 1.4 (ERA recommended) produced an ER% = 7.94 and a false positive rate (FPR%) equal to that shown by the ANN (FPR% = 3.17). According to the current threshold limits for HD dose adequacy, the ANN was a reliable and accurate tool for URR monitoring, better than the Smye and the modified URR methods. The use of the ANN urea estimation yields accurate results when used to calculate Kt/V. The Kt/Vsp with an adequacy threshold of 1.4 is a superior approach for HD adequacy monitoring, suggesting that the current adequacy limits should be reviewed for both URR and Kt/V.
format Artículo
author Fernández, Elmer Andrés
Valtuille, Rodolfo
Presedo, Jesús
Willshaw, Peter
author_facet Fernández, Elmer Andrés
Valtuille, Rodolfo
Presedo, Jesús
Willshaw, Peter
author_sort Fernández, Elmer Andrés
title Comparison of standard and artificial neural network estimators of hemodialysis adequacy
title_short Comparison of standard and artificial neural network estimators of hemodialysis adequacy
title_full Comparison of standard and artificial neural network estimators of hemodialysis adequacy
title_fullStr Comparison of standard and artificial neural network estimators of hemodialysis adequacy
title_full_unstemmed Comparison of standard and artificial neural network estimators of hemodialysis adequacy
title_sort comparison of standard and artificial neural network estimators of hemodialysis adequacy
publishDate 2005
url http://pa.bibdigital.ucc.edu.ar/3991/1/A_Fern%C3%A1ndez_Valtuille_Presedo_Willshaw.pdf
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