An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation

In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiation of dialysis, we conducted a retrospective cohort study including 25 haemodialysis centres in Mainland China. Patients with ESRD (n = 1281) who comme...

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Published inScientific reports Vol. 9; no. 1; p. 5871
Main Authors Liu, Ying, Wang, Degang, Chen, Xiangmei, Sun, Xuefeng, Song, Wenyan, Jiang, Hongli, Shi, Wei, Liu, Wenhu, Fu, Ping, Ding, Xiaoqiang, Chang, Ming, Yu, Xueqing, Cao, Ning, Chen, Menghua, Ni, Zhaohui, Cheng, Jing, Sun, Shiren, Wang, Huimin, Wang, Yunyan, Gao, Bihu, Wang, Jianqin, Hao, Lirong, Li, Suhua, He, Qiang, Liu, Hongmei, Shao, Fengmin, Li, Wei, Wang, Yang, Szczech, Lynda, Lv, Qiuxia, Han, Xianfeng, Wang, Luping, Fang, Ming, Odeh, Zach, Sun, Ximing, Lin, Hongli
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 10.04.2019
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Abstract In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiation of dialysis, we conducted a retrospective cohort study including 25 haemodialysis centres in Mainland China. Patients with ESRD (n = 1281) who commenced haemodialysis from 2008 to 2011 were enrolled in the development cohort, whereas 504 patients who began haemodialysis between 2012 and 2013 were enrolled in the validation cohort comprised. An artificial neural network model was used to select variables, and a fuzzy neural network model was then constructed using factors affecting haemodialysis initiation as input variables and 3-year survival as the output variable. A logistic model was set up using the same variables. The equation’s performance was compared with that of the logistic model and conventional eGFR-based assessment. The area under the bootstrap-corrected receiver-operating characteristic curve of the equation was 0.70, and that of two conventional eGFR-based assessments were 0.57 and 0.54. In conclusion, the new equation based on Fuzzy mathematics, covering laboratory and clinical variables, is more suitable for assessing the timing of dialysis initiation in a Chinese ESRD population than eGFR, and may be a helpful tool to quantitatively evaluate the initiation of haemodialysis.
AbstractList In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiation of dialysis, we conducted a retrospective cohort study including 25 haemodialysis centres in Mainland China. Patients with ESRD (n = 1281) who commenced haemodialysis from 2008 to 2011 were enrolled in the development cohort, whereas 504 patients who began haemodialysis between 2012 and 2013 were enrolled in the validation cohort comprised. An artificial neural network model was used to select variables, and a fuzzy neural network model was then constructed using factors affecting haemodialysis initiation as input variables and 3-year survival as the output variable. A logistic model was set up using the same variables. The equation’s performance was compared with that of the logistic model and conventional eGFR-based assessment. The area under the bootstrap-corrected receiver-operating characteristic curve of the equation was 0.70, and that of two conventional eGFR-based assessments were 0.57 and 0.54. In conclusion, the new equation based on Fuzzy mathematics, covering laboratory and clinical variables, is more suitable for assessing the timing of dialysis initiation in a Chinese ESRD population than eGFR, and may be a helpful tool to quantitatively evaluate the initiation of haemodialysis.
Abstract In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiation of dialysis, we conducted a retrospective cohort study including 25 haemodialysis centres in Mainland China. Patients with ESRD (n = 1281) who commenced haemodialysis from 2008 to 2011 were enrolled in the development cohort, whereas 504 patients who began haemodialysis between 2012 and 2013 were enrolled in the validation cohort comprised. An artificial neural network model was used to select variables, and a fuzzy neural network model was then constructed using factors affecting haemodialysis initiation as input variables and 3-year survival as the output variable. A logistic model was set up using the same variables. The equation’s performance was compared with that of the logistic model and conventional eGFR-based assessment. The area under the bootstrap-corrected receiver-operating characteristic curve of the equation was 0.70, and that of two conventional eGFR-based assessments were 0.57 and 0.54. In conclusion, the new equation based on Fuzzy mathematics, covering laboratory and clinical variables, is more suitable for assessing the timing of dialysis initiation in a Chinese ESRD population than eGFR, and may be a helpful tool to quantitatively evaluate the initiation of haemodialysis.
ArticleNumber 5871
Author Shi, Wei
Sun, Shiren
Song, Wenyan
Yu, Xueqing
Chen, Xiangmei
Gao, Bihu
Wang, Jianqin
Hao, Lirong
Li, Wei
Cheng, Jing
Liu, Wenhu
Jiang, Hongli
Lin, Hongli
Wang, Huimin
Wang, Yunyan
Szczech, Lynda
He, Qiang
Shao, Fengmin
Lv, Qiuxia
Wang, Luping
Fang, Ming
Sun, Xuefeng
Chang, Ming
Li, Suhua
Ding, Xiaoqiang
Wang, Degang
Liu, Hongmei
Han, Xianfeng
Wang, Yang
Liu, Ying
Cao, Ning
Sun, Ximing
Ni, Zhaohui
Fu, Ping
Chen, Menghua
Odeh, Zach
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  givenname: Xuefeng
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SSID ssj0000529419
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Snippet In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiation of...
Abstract In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the...
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StartPage 5871
SubjectTerms 692/4022/1950/1544
692/699/1585/104/1586
Adult
Aged
Area Under Curve
Epidermal growth factor receptors
Female
Fuzzy logic
Glomerular Filtration Rate
Hemodialysis
Humanities and Social Sciences
Humans
Kidney Failure, Chronic - mortality
Kidney Failure, Chronic - pathology
Kidney Failure, Chronic - therapy
Logistic Models
Male
Middle Aged
multidisciplinary
Neural networks
Neural Networks, Computer
Renal Dialysis
Retrospective Studies
ROC Curve
Science
Science (multidisciplinary)
Time Factors
Variables
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Title An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation
URI https://link.springer.com/article/10.1038/s41598-018-37762-6
https://www.ncbi.nlm.nih.gov/pubmed/30971708
https://www.proquest.com/docview/2207147651
https://pubmed.ncbi.nlm.nih.gov/PMC6458145
Volume 9
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