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

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 appropri...

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Vydáno v:	Renal failure Ročník 44; číslo 1; s. 562 - 570
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Taylor & Francis 01.12.2022 Taylor & Francis Ltd Taylor & Francis Group
Témata:	Algorithms Biopsy Blood glucose Clinical Study Diabetes Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - complications Diabetes Mellitus, Type 2 - pathology diabetic kidney disease Diabetic Nephropathies - diagnosis Diabetic Nephropathies - etiology Diabetic Nephropathies - metabolism End-stage renal disease Glomerular filtration rate Hemoglobin Humans Kidney diseases Kidney Failure, Chronic - diagnosis Kidney Failure, Chronic - epidemiology Kidney Failure, Chronic - etiology Learning algorithms Machine Learning Morbidity Patients Prediction models Prognosis Proteinuria Risk factors risk prediction model Type 2 diabetes mellitus diabetic kidney disease risk prediction model Type 2 diabetes mellitus end-stage renal disease machine learning
ISSN:	0886-022X, 1525-6049, 1525-6049
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Abstract	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). 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. 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. 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.
AbstractList	AimsDiabetic 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).MethodsBetween 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.ResultsThere 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.ConclusionMachine 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.HighlightsWhat 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. 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). 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. 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. 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 Identification of risk factors for the progression of DKD to ESRD is expected to improve the prognosis by early detection and appropriate intervention. 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. 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. 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). 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. 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. 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. 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.HighlightsWhat 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. 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).AIMSDiabetic 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).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.METHODSBetween 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.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.RESULTSThere 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.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.HighlightsWhat 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.CONCLUSIONMachine 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.HighlightsWhat 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.
Author	Li, Lin Tong, Nanwei Zou, Yutong Wang, Yiting Wang, Tingli Zhao, Lijun Wu, Yucheng Zhang, Junlin Zhao, Yuancheng Xu, Huan Chai, Zhonglin Ren, Honghong Wang, Jiali Cooper, Mark E. Zhang, Rui Qin, Chunmei Liu, Fang
Author_xml	– sequence: 1 givenname: Yutong surname: Zou fullname: Zou, Yutong organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 2 givenname: Lijun orcidid: 0000-0001-7750-1864 surname: Zhao fullname: Zhao, Lijun organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 3 givenname: Junlin surname: Zhang fullname: Zhang, Junlin organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 4 givenname: Yiting surname: Wang fullname: Wang, Yiting organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 5 givenname: Yucheng surname: Wu fullname: Wu, Yucheng organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 6 givenname: Honghong surname: Ren fullname: Ren, Honghong organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 7 givenname: Tingli surname: Wang fullname: Wang, Tingli organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 8 givenname: Rui surname: Zhang fullname: Zhang, Rui organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 9 givenname: Jiali surname: Wang fullname: Wang, Jiali organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 10 givenname: Yuancheng surname: Zhao fullname: Zhao, Yuancheng organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 11 givenname: Chunmei surname: Qin fullname: Qin, Chunmei organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University – sequence: 12 givenname: Huan surname: Xu fullname: Xu, Huan organization: Division of Pathology, West China Hospital of Sichuan University – sequence: 13 givenname: Lin surname: Li fullname: Li, Lin organization: Division of Pathology, West China Hospital of Sichuan University – sequence: 14 givenname: Zhonglin surname: Chai fullname: Chai, Zhonglin organization: Department of Diabetes, Central Clinical School, Monash University – sequence: 15 givenname: Mark E. surname: Cooper fullname: Cooper, Mark E. organization: Department of Diabetes, Central Clinical School, Monash University – sequence: 16 givenname: Nanwei surname: Tong fullname: Tong, Nanwei organization: Division of Endocrinology, West China Hospital of Sichuan University – sequence: 17 givenname: Fang orcidid: 0000-0003-1121-3004 surname: Liu fullname: Liu, Fang organization: Laboratory of Diabetic Kidney Disease, Centre of Diabetes and Metabolism Research, West China Hospital of Sichuan University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35373711$$D View this record in MEDLINE/PubMed
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Keywords	diabetic kidney disease risk prediction model Type 2 diabetes mellitus end-stage renal disease machine learning
Language	English
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PublicationTitle	Renal failure
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References_xml	– volume: 2019 start-page: 1 year: 2019 ident: e_1_3_5_6_1 article-title: The level of serum albumin is associated with renal prognosis in patients with diabetic nephropathy publication-title: J Diabetes Res doi: 10.1155/2019/7825804 – ident: e_1_3_5_11_1 doi: 10.1038/s41591-018-0239-8 – ident: e_1_3_5_25_1 doi: 10.1111/j.1523-1755.2005.09909.x – ident: e_1_3_5_28_1 doi: 10.1016/j.clinimag.2018.04.015 – ident: e_1_3_5_4_1 doi: 10.1080/03007995.2019.1682981 – ident: e_1_3_5_10_1 doi: 10.7717/peerj.8499 – ident: e_1_3_5_24_1 doi: 10.1007/s00125-006-0247-y – ident: e_1_3_5_13_1 doi: 10.1681/ASN.2010010010 – ident: e_1_3_5_18_1 doi: 10.1016/j.freeradbiomed.2013.02.005 – volume: 18 start-page: 277 issue: 5 year: 2015 ident: e_1_3_5_27_1 article-title: Relationship between anemia and chronic complications in Chinese patients with type 2 diabetes mellitus publication-title: Arch Iran Med – ident: e_1_3_5_2_1 doi: 10.1016/j.mcna.2012.10.001 – ident: e_1_3_5_26_1 doi: 10.1097/JCMA.0000000000000175 – ident: e_1_3_5_5_1 doi: 10.1136/bmjopen-2019-035308 – volume: 38 start-page: S41–S48 issue: 1 year: 2015 ident: e_1_3_5_12_1 article-title: Comment on American Diabetes Association. Approaches to glycemic treatment. Sec. 7. In standards of medical care in diabetes-2015 publication-title: Diabetes Care – ident: e_1_3_5_17_1 doi: 10.1111/dom.14178 – ident: e_1_3_5_3_1 doi: 10.1186/s12882-017-0671-x – ident: e_1_3_5_32_1 doi: 10.4158/EP-2019-0238 – volume: 2019 start-page: 1 year: 2019 ident: e_1_3_5_15_1 article-title: Comparison of performance of equations for estimated glomerular filtration rate in Chinese patients with biopsy-proven diabetic nephropathy publication-title: Dis Markers doi: 10.1155/2019/4354061 – ident: e_1_3_5_29_1 doi: 10.1097/MNH.0000000000000341 – ident: e_1_3_5_21_1 doi: 10.1186/1471-2369-6-8 – ident: e_1_3_5_8_1 doi: 10.3390/jcm10010003 – ident: e_1_3_5_9_1 doi: 10.1111/jdi.12533 – ident: e_1_3_5_19_1 doi: 10.1016/j.bbrc.2010.09.027 – ident: e_1_3_5_23_1 doi: 10.1111/j.1523-1755.2004.00863.x – ident: e_1_3_5_22_1 doi: 10.1038/ncpneph0378 – ident: e_1_3_5_31_1 doi: 10.1371/journal.pone.0190930 – ident: e_1_3_5_33_1 doi: 10.1053/j.ackd.2017.12.005 – ident: e_1_3_5_7_1 doi: 10.5888/pcd17.200076 – ident: e_1_3_5_14_1 doi: 10.1016/j.brat.2016.11.008 – ident: e_1_3_5_16_1 doi: 10.1159/000101958 – ident: e_1_3_5_30_1 doi: 10.1053/j.ajkd.2020.04.016 – ident: e_1_3_5_20_1 doi: 10.1371/journal.pone.0177799
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Snippet	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... AimsDiabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in... 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...
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SubjectTerms	Algorithms Biopsy Blood glucose Clinical Study Diabetes Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - complications Diabetes Mellitus, Type 2 - pathology diabetic kidney disease Diabetic Nephropathies - diagnosis Diabetic Nephropathies - etiology Diabetic Nephropathies - metabolism End-stage renal disease Glomerular filtration rate Hemoglobin Humans Kidney diseases Kidney Failure, Chronic - diagnosis Kidney Failure, Chronic - epidemiology Kidney Failure, Chronic - etiology Learning algorithms Machine Learning Morbidity Patients Prediction models Prognosis Proteinuria Risk factors risk prediction model Type 2 diabetes mellitus
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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
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