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 in | Scientific reports Vol. 9; no. 1; p. 5871 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Ying surname: Liu fullname: Liu, Ying organization: Dalian Medical University Graduate School, Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Liaoning Province Translational Medicine Research Center of Kidney Disease, Kidney Research Institute of Dalian Medical University – sequence: 2 givenname: Degang surname: Wang fullname: Wang, Degang organization: School of Control Science and Engineering, Dalian University of Technology – sequence: 3 givenname: Xiangmei surname: Chen fullname: Chen, Xiangmei organization: Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research – sequence: 4 givenname: Xuefeng surname: Sun fullname: Sun, Xuefeng organization: Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research – sequence: 5 givenname: Wenyan surname: Song fullname: Song, Wenyan organization: School of Economics, Dongbei University of Finance and Economics – sequence: 6 givenname: Hongli surname: Jiang fullname: Jiang, Hongli organization: Blood Purification Center, The First Affiliated Hospital of Xi’an Jiaotong University – sequence: 7 givenname: Wei surname: Shi fullname: Shi, Wei organization: Division of Nephrology, Guangdong General Hospital, Guangdong Academy of Medical Sciences – sequence: 8 givenname: Wenhu surname: Liu fullname: Liu, Wenhu organization: Division of Nephrology, Beijing Friendship Hospital, Capital Medical University – sequence: 9 givenname: Ping surname: Fu fullname: Fu, Ping organization: Kidney Research Institute, Division of Nephrology, West China Hospital of Sichuan University – sequence: 10 givenname: Xiaoqiang surname: Ding fullname: Ding, Xiaoqiang organization: Division of Nephrology, Zhongshan Hospital, Fudan University – sequence: 11 givenname: Ming surname: Chang fullname: Chang, Ming organization: Division of Nephrology, Dalian Municipal Central Hospital – sequence: 12 givenname: Xueqing surname: Yu fullname: Yu, Xueqing organization: Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Key Laboratory of Nephrology, Ministry of Health of China – sequence: 13 givenname: Ning surname: Cao fullname: Cao, Ning organization: Blood Purification Center, General Hospital of Shenyang Military Area Command – sequence: 14 givenname: Menghua surname: Chen fullname: Chen, Menghua organization: Department of Nephrology, General Hospital of Ningxia Medical University – sequence: 15 givenname: Zhaohui surname: Ni fullname: Ni, Zhaohui organization: Department of Nephrology, Renji Hospital, School of Medicine, Shanghai Jiaotong University – sequence: 16 givenname: Jing surname: Cheng fullname: Cheng, Jing organization: Division of Nephrology, Huashan Hospital, Fudan University – sequence: 17 givenname: Shiren surname: Sun fullname: Sun, Shiren organization: Department of Nephrology, Xijing Hospital, the Fourth Military Medical University – sequence: 18 givenname: Huimin surname: Wang fullname: Wang, Huimin organization: Division of Nephrology, General Hospital of Benxi Iron and Steel Co., Ltd – sequence: 19 givenname: Yunyan surname: Wang fullname: Wang, Yunyan organization: Blood Purification Center, Daping Hospital & Surgery Institute, the Third Military Medical University – sequence: 20 givenname: Bihu surname: Gao fullname: Gao, Bihu organization: Division of Nephrology, Affiliated Zhongshan Hospital of Dalian University – sequence: 21 givenname: Jianqin surname: Wang fullname: Wang, Jianqin organization: Division of Nephrology, Lanzhou University Second Hospital – sequence: 22 givenname: Lirong surname: Hao fullname: Hao, Lirong organization: Division of Nephrology, the First Affiliated Hospital of Harbin Medical University – sequence: 23 givenname: Suhua surname: Li fullname: Li, Suhua organization: Division of Nephrology, the First Affiliated Hospital of Xinjiang Medical University – sequence: 24 givenname: Qiang surname: He fullname: He, Qiang organization: Division of Nephrology, Zhejiang Provincial People’s Hospital – sequence: 25 givenname: Hongmei surname: Liu fullname: Liu, Hongmei organization: Division of Nephrology, An Steel Group Hospital – sequence: 26 givenname: Fengmin surname: Shao fullname: Shao, Fengmin organization: Blood Purification Center, The People’s Hospital of Zhengzhou University & Henan Provincial People’s Hospital – sequence: 27 givenname: Wei surname: Li fullname: Li, Wei organization: Medical Research & Biometrics Center, Fuwai Hospital, Chinese Academy of Medical Sciences – sequence: 28 givenname: Yang surname: Wang fullname: Wang, Yang organization: Medical Research & Biometrics Center, Fuwai Hospital, Chinese Academy of Medical Sciences – sequence: 29 givenname: Lynda surname: Szczech fullname: Szczech, Lynda organization: FibroGen, Inc – sequence: 30 givenname: Qiuxia surname: Lv fullname: Lv, Qiuxia organization: School of Control Science and Engineering, Dalian University of Technology – sequence: 31 givenname: Xianfeng surname: Han fullname: Han, Xianfeng organization: Dalian Medical University Graduate School, Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Liaoning Province Translational Medicine Research Center of Kidney Disease – sequence: 32 givenname: Luping surname: Wang fullname: Wang, Luping organization: Dalian Medical University Graduate School, Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Liaoning Province Translational Medicine Research Center of Kidney Disease – sequence: 33 givenname: Ming surname: Fang fullname: Fang, Ming organization: Dalian Medical University Graduate School, Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Liaoning Province Translational Medicine Research Center of Kidney Disease, Kidney Research Institute of Dalian Medical University – sequence: 34 givenname: Zach surname: Odeh fullname: Odeh, Zach organization: Dalian Medical University Graduate School, Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Liaoning Province Translational Medicine Research Center of Kidney Disease – sequence: 35 givenname: Ximing surname: Sun fullname: Sun, Ximing organization: School of Control Science and Engineering, Dalian University of Technology – sequence: 36 givenname: Hongli surname: Lin fullname: Lin, Hongli email: hllin@dlmedu.edu.cn organization: Dalian Medical University Graduate School, Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Liaoning Province Translational Medicine Research Center of Kidney Disease, Kidney Research Institute of Dalian Medical University |
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CitedBy_id | crossref_primary_10_1136_bmjopen_2022_066675 crossref_primary_10_1177_11297298241234610 crossref_primary_10_1136_bmjopen_2018_023162 crossref_primary_10_1186_s12882_023_03184_4 |
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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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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 |
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