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A back propagation neural network approach to estimate the glomerular filtration rate in an older population

BACKGROUND: The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. METHODS: Adults aged...

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Autores principales: Jiang, Shimin, Li, Yetong, Jiao, Yuanyuan, Zhang, Danyang, Wang, Ying, Li, Wenge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207816/
https://www.ncbi.nlm.nih.gov/pubmed/37226135
http://dx.doi.org/10.1186/s12877-023-04027-5
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author Jiang, Shimin
Li, Yetong
Jiao, Yuanyuan
Zhang, Danyang
Wang, Ying
Li, Wenge
author_facet Jiang, Shimin
Li, Yetong
Jiao, Yuanyuan
Zhang, Danyang
Wang, Ying
Li, Wenge
author_sort Jiang, Shimin
collection PubMed
description BACKGROUND: The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. METHODS: Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid ((99m)Tc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). RESULTS: The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m(2), which was smaller than that of LMR (4.59 ml/min/1.73 m(2); p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m(2); p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m(2); p = 0.31), EKFC (-1.41 ml/min/1.73 m(2); p = 0.26), BIS1 (0.64 ml/min/1.73 m(2); p = 0.99), and MDRD (1.11 ml/min/1.73 m(2); p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m(2)) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m(2), the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m(2)). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55−2.78] and 0.24 [-2.58−1.61], respectively), smaller than any other equation. CONCLUSIONS: The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04027-5.
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spelling pubmed-102078162023-05-25 A back propagation neural network approach to estimate the glomerular filtration rate in an older population Jiang, Shimin Li, Yetong Jiao, Yuanyuan Zhang, Danyang Wang, Ying Li, Wenge BMC Geriatr Research BACKGROUND: The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. METHODS: Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid ((99m)Tc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). RESULTS: The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m(2), which was smaller than that of LMR (4.59 ml/min/1.73 m(2); p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m(2); p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m(2); p = 0.31), EKFC (-1.41 ml/min/1.73 m(2); p = 0.26), BIS1 (0.64 ml/min/1.73 m(2); p = 0.99), and MDRD (1.11 ml/min/1.73 m(2); p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m(2)) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m(2), the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m(2)). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55−2.78] and 0.24 [-2.58−1.61], respectively), smaller than any other equation. CONCLUSIONS: The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04027-5. BioMed Central 2023-05-24 /pmc/articles/PMC10207816/ /pubmed/37226135 http://dx.doi.org/10.1186/s12877-023-04027-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Jiang, Shimin
Li, Yetong
Jiao, Yuanyuan
Zhang, Danyang
Wang, Ying
Li, Wenge
A back propagation neural network approach to estimate the glomerular filtration rate in an older population
title A back propagation neural network approach to estimate the glomerular filtration rate in an older population
title_full A back propagation neural network approach to estimate the glomerular filtration rate in an older population
title_fullStr A back propagation neural network approach to estimate the glomerular filtration rate in an older population
title_full_unstemmed A back propagation neural network approach to estimate the glomerular filtration rate in an older population
title_short A back propagation neural network approach to estimate the glomerular filtration rate in an older population
title_sort back propagation neural network approach to estimate the glomerular filtration rate in an older population
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10207816/
https://www.ncbi.nlm.nih.gov/pubmed/37226135
http://dx.doi.org/10.1186/s12877-023-04027-5
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