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Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease

AIMS: Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in patients with diabetes. Identification of risk factors involved in the progression of DKD to ESRD is expected to result in early detection and ap...

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Autores principales: Zou, Yutong, Zhao, Lijun, Zhang, Junlin, Wang, Yiting, Wu, Yucheng, Ren, Honghong, Wang, Tingli, Zhang, Rui, Wang, Jiali, Zhao, Yuancheng, Qin, Chunmei, Xu, Huan, Li, Lin, Chai, Zhonglin, Cooper, Mark E., Tong, Nanwei, Liu, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986220/
https://www.ncbi.nlm.nih.gov/pubmed/35373711
http://dx.doi.org/10.1080/0886022X.2022.2056053
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author Zou, Yutong
Zhao, Lijun
Zhang, Junlin
Wang, Yiting
Wu, Yucheng
Ren, Honghong
Wang, Tingli
Zhang, Rui
Wang, Jiali
Zhao, Yuancheng
Qin, Chunmei
Xu, Huan
Li, Lin
Chai, Zhonglin
Cooper, Mark E.
Tong, Nanwei
Liu, Fang
author_facet Zou, Yutong
Zhao, Lijun
Zhang, Junlin
Wang, Yiting
Wu, Yucheng
Ren, Honghong
Wang, Tingli
Zhang, Rui
Wang, Jiali
Zhao, Yuancheng
Qin, Chunmei
Xu, Huan
Li, Lin
Chai, Zhonglin
Cooper, Mark E.
Tong, Nanwei
Liu, Fang
author_sort Zou, Yutong
collection PubMed
description AIMS: Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in patients with diabetes. Identification of risk factors involved in the progression of DKD to ESRD is expected to result in early detection and appropriate intervention and improve prognosis. Therefore, this study aimed to establish a risk prediction model for ESRD resulting from DKD in patients with type 2 diabetes mellitus (T2DM). METHODS: Between January 2008 and July 2019, a total of 390 Chinese patients with T2DM and DKD confirmed by percutaneous renal biopsy were enrolled and followed up for at least 1 year. Four machine learning algorithms (gradient boosting machine, support vector machine, logistic regression, and random forest (RF)) were used to identify the critical clinical and pathological features and to build a risk prediction model for ESRD. RESULTS: There were 158 renal outcome events (ESRD) (40.51%) during the 3-year median follow up. The RF algorithm showed the best performance at predicting progression to ESRD, showing the highest AUC (0.90) and ACC (82.65%). The RF algorithm identified five major factors: Cystatin-C, serum albumin (sAlb), hemoglobin (Hb), 24-hour urine urinary total protein, and estimated glomerular filtration rate. A nomogram according to the aforementioned five predictive factors was constructed to predict the incidence of ESRD. CONCLUSION: Machine learning algorithms can efficiently predict the incident ESRD in DKD participants. Compared with the previous models, the importance of sAlb and Hb were highlighted in the current model. HIGHLIGHTS: What is already known? Identification of risk factors for the progression of DKD to ESRD is expected to improve the prognosis by early detection and appropriate intervention. What this study has found? Machine learning algorithms were used to construct a risk prediction model of ESRD in patients with T2DM and DKD. The major predictive factors were found to be CysC, sAlb, Hb, eGFR, and UTP. What are the implications of the study? In contrast with the treatment of participants with early-phase T2DM with or without mild kidney damage, major emphasis should be placed on indicators of kidney function, nutrition, anemia, and proteinuria for participants with T2DM and advanced DKD to delay ESRD, rather than age, sex, and control of hypertension and glycemia.
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spelling pubmed-89862202022-04-07 Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease Zou, Yutong Zhao, Lijun Zhang, Junlin Wang, Yiting Wu, Yucheng Ren, Honghong Wang, Tingli Zhang, Rui Wang, Jiali Zhao, Yuancheng Qin, Chunmei Xu, Huan Li, Lin Chai, Zhonglin Cooper, Mark E. Tong, Nanwei Liu, Fang Ren Fail Clinical Study AIMS: Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in patients with diabetes. Identification of risk factors involved in the progression of DKD to ESRD is expected to result in early detection and appropriate intervention and improve prognosis. Therefore, this study aimed to establish a risk prediction model for ESRD resulting from DKD in patients with type 2 diabetes mellitus (T2DM). METHODS: Between January 2008 and July 2019, a total of 390 Chinese patients with T2DM and DKD confirmed by percutaneous renal biopsy were enrolled and followed up for at least 1 year. Four machine learning algorithms (gradient boosting machine, support vector machine, logistic regression, and random forest (RF)) were used to identify the critical clinical and pathological features and to build a risk prediction model for ESRD. RESULTS: There were 158 renal outcome events (ESRD) (40.51%) during the 3-year median follow up. The RF algorithm showed the best performance at predicting progression to ESRD, showing the highest AUC (0.90) and ACC (82.65%). The RF algorithm identified five major factors: Cystatin-C, serum albumin (sAlb), hemoglobin (Hb), 24-hour urine urinary total protein, and estimated glomerular filtration rate. A nomogram according to the aforementioned five predictive factors was constructed to predict the incidence of ESRD. CONCLUSION: Machine learning algorithms can efficiently predict the incident ESRD in DKD participants. Compared with the previous models, the importance of sAlb and Hb were highlighted in the current model. HIGHLIGHTS: What is already known? Identification of risk factors for the progression of DKD to ESRD is expected to improve the prognosis by early detection and appropriate intervention. What this study has found? Machine learning algorithms were used to construct a risk prediction model of ESRD in patients with T2DM and DKD. The major predictive factors were found to be CysC, sAlb, Hb, eGFR, and UTP. What are the implications of the study? In contrast with the treatment of participants with early-phase T2DM with or without mild kidney damage, major emphasis should be placed on indicators of kidney function, nutrition, anemia, and proteinuria for participants with T2DM and advanced DKD to delay ESRD, rather than age, sex, and control of hypertension and glycemia. Taylor & Francis 2022-04-04 /pmc/articles/PMC8986220/ /pubmed/35373711 http://dx.doi.org/10.1080/0886022X.2022.2056053 Text en © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Study
Zou, Yutong
Zhao, Lijun
Zhang, Junlin
Wang, Yiting
Wu, Yucheng
Ren, Honghong
Wang, Tingli
Zhang, Rui
Wang, Jiali
Zhao, Yuancheng
Qin, Chunmei
Xu, Huan
Li, Lin
Chai, Zhonglin
Cooper, Mark E.
Tong, Nanwei
Liu, Fang
Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease
title Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease
title_full Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease
title_fullStr Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease
title_full_unstemmed Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease
title_short Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease
title_sort development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease
topic Clinical Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986220/
https://www.ncbi.nlm.nih.gov/pubmed/35373711
http://dx.doi.org/10.1080/0886022X.2022.2056053
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