Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus
Background Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetic mellitus (DM), the risk of cardiovascular events and all-cause mortality also increases in DKD patients. This study aimed to detect the influencing factors of DKD in type 2 DM (T2DM) patients, and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2022.2113797 |
| _version_ | 1811183150600028160 |
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| author | Ling Sun Yu Wu Rui-Xue Hua Lu-Xi Zou |
| author_facet | Ling Sun Yu Wu Rui-Xue Hua Lu-Xi Zou |
| author_sort | Ling Sun |
| collection | DOAJ |
| description | Background Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetic mellitus (DM), the risk of cardiovascular events and all-cause mortality also increases in DKD patients. This study aimed to detect the influencing factors of DKD in type 2 DM (T2DM) patients, and construct DKD prediction models and nomogram for clinical decision-making.Methods A total of 14,628 patients with T2DM were included. These patients were divided into pre-DKD and non-DKD groups, depending on the occurrence of DKD during a 3-year follow-up from first clinic attendance. The influencing indicators of DKD were analyzed, the prediction models were established by multivariable logistic regression, and a nomogram was drawn for DKD risk assessment.Results Two prediction models for DKD were built by multivariate logistic regression analysis. Model 1 was created based on 17 variables using the forward selection method, Model 2 was established by 19 variables using the backward elimination method. The Somers’ D values of both models were 0.789. Four independent predictors were selected to build the nomogram, including age, UACR, eGFR, and neutrophil percentages. The C-index of the nomogram reached 0.864, suggesting a good predictive accuracy for DKD development.Conclusions Our prediction models had strong predictive powers, and our nomogram provided visual aids to DKD risk calculation, which was simple and fast. These algorithms can provide early DKD risk prediction, which might help to improve the medical care for early detection and intervention in T2DM patients, and then consequently improve the prognosis of DM patients. |
| first_indexed | 2024-04-11T09:42:59Z |
| format | Article |
| id | doaj.art-44b3231d0cfc4dbebf90be2ed7748ae4 |
| institution | Directory Open Access Journal |
| issn | 0886-022X 1525-6049 |
| language | English |
| last_indexed | 2024-04-11T09:42:59Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj.art-44b3231d0cfc4dbebf90be2ed7748ae42022-12-22T04:31:09ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492022-12-014411454146110.1080/0886022X.2022.2113797Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitusLing Sun0Yu Wu1Rui-Xue Hua2Lu-Xi Zou3Department of Nephrology, Xuzhou Central Hospital, Xuzhou, ChinaXuzhou Clinical School of Xuzhou Medical University, Xuzhou, ChinaXuzhou Clinical School of Xuzhou Medical University, Xuzhou, ChinaSchool of Management, Xuzhou Medical University, Xuzhou, ChinaBackground Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetic mellitus (DM), the risk of cardiovascular events and all-cause mortality also increases in DKD patients. This study aimed to detect the influencing factors of DKD in type 2 DM (T2DM) patients, and construct DKD prediction models and nomogram for clinical decision-making.Methods A total of 14,628 patients with T2DM were included. These patients were divided into pre-DKD and non-DKD groups, depending on the occurrence of DKD during a 3-year follow-up from first clinic attendance. The influencing indicators of DKD were analyzed, the prediction models were established by multivariable logistic regression, and a nomogram was drawn for DKD risk assessment.Results Two prediction models for DKD were built by multivariate logistic regression analysis. Model 1 was created based on 17 variables using the forward selection method, Model 2 was established by 19 variables using the backward elimination method. The Somers’ D values of both models were 0.789. Four independent predictors were selected to build the nomogram, including age, UACR, eGFR, and neutrophil percentages. The C-index of the nomogram reached 0.864, suggesting a good predictive accuracy for DKD development.Conclusions Our prediction models had strong predictive powers, and our nomogram provided visual aids to DKD risk calculation, which was simple and fast. These algorithms can provide early DKD risk prediction, which might help to improve the medical care for early detection and intervention in T2DM patients, and then consequently improve the prognosis of DM patients.https://www.tandfonline.com/doi/10.1080/0886022X.2022.2113797Diabetes Mellitusdiabetic kidney diseaseprediction modelNomogram |
| spellingShingle | Ling Sun Yu Wu Rui-Xue Hua Lu-Xi Zou Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus Renal Failure Diabetes Mellitus diabetic kidney disease prediction model Nomogram |
| title | Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus |
| title_full | Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus |
| title_fullStr | Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus |
| title_full_unstemmed | Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus |
| title_short | Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus |
| title_sort | prediction models for risk of diabetic kidney disease in chinese patients with type 2 diabetes mellitus |
| topic | Diabetes Mellitus diabetic kidney disease prediction model Nomogram |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2022.2113797 |
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