A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study
Objective. To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM). Methods. Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Endocrinology |
| Online Access: | http://dx.doi.org/10.1155/2021/6672444 |
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| author | Chunfeng Xi Caimei Wang Guihong Rong Jinhuan Deng |
| author_facet | Chunfeng Xi Caimei Wang Guihong Rong Jinhuan Deng |
| author_sort | Chunfeng Xi |
| collection | DOAJ |
| description | Objective. To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM). Methods. Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095 patients with T2DM from Guilin. A least absolute contraction selection operator (LASSO) regression and multivariable logistic regression analysis were used to screen out DN risk factors. A logistic regression analysis incorporating the screened risk factors was used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using the C-index, an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Bootstrapping was applied for internal validation. Results. Independent predictors for DN incidence risk included gender, age, hypertension, medicine use, duration of diabetes, body mass index, blood urea nitrogen level, serum creatinine level, neutrophil to lymphocyte ratio, and red blood cell distribution width. The nomogram model exhibited moderate prediction ability with a C-index of 0.819 (95% confidence interval (CI): 0.783–0.853) and an AUC of 0.813 (95%CI: 0.778–0.848). The C-index from internal validation reached 0.796 (95%CI: 0.763–0.829). The decision curve analysis displayed that the DN risk nomogram was clinically applicable when the risk threshold was between 1 and 83%. Conclusion. Our novel and simple nomogram containing 10 factors may be useful in predicting DN incidence risk in T2DM patients. |
| format | Article |
| id | doaj-art-cd63b0fa125b4cbfa5a74fd773c596a2 |
| institution | OA Journals |
| issn | 1687-8337 1687-8345 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
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| series | International Journal of Endocrinology |
| spelling | doaj-art-cd63b0fa125b4cbfa5a74fd773c596a22025-08-20T02:19:54ZengWileyInternational Journal of Endocrinology1687-83371687-83452021-01-01202110.1155/2021/66724446672444A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective StudyChunfeng Xi0Caimei Wang1Guihong Rong2Jinhuan Deng3Department of Laboratory Medicine, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, ChinaDepartment of Laboratory Medicine, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, ChinaDepartment of Laboratory Medicine, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, ChinaDepartment of Laboratory Medicine, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, ChinaObjective. To construct a novel nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in Chinese patients with type 2 diabetes mellitus (T2DM). Methods. Questionnaire surveys, physical examinations, routine blood tests, and biochemical index evaluations were conducted on 1095 patients with T2DM from Guilin. A least absolute contraction selection operator (LASSO) regression and multivariable logistic regression analysis were used to screen out DN risk factors. A logistic regression analysis incorporating the screened risk factors was used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using the C-index, an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Bootstrapping was applied for internal validation. Results. Independent predictors for DN incidence risk included gender, age, hypertension, medicine use, duration of diabetes, body mass index, blood urea nitrogen level, serum creatinine level, neutrophil to lymphocyte ratio, and red blood cell distribution width. The nomogram model exhibited moderate prediction ability with a C-index of 0.819 (95% confidence interval (CI): 0.783–0.853) and an AUC of 0.813 (95%CI: 0.778–0.848). The C-index from internal validation reached 0.796 (95%CI: 0.763–0.829). The decision curve analysis displayed that the DN risk nomogram was clinically applicable when the risk threshold was between 1 and 83%. Conclusion. Our novel and simple nomogram containing 10 factors may be useful in predicting DN incidence risk in T2DM patients.http://dx.doi.org/10.1155/2021/6672444 |
| spellingShingle | Chunfeng Xi Caimei Wang Guihong Rong Jinhuan Deng A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study International Journal of Endocrinology |
| title | A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study |
| title_full | A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study |
| title_fullStr | A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study |
| title_full_unstemmed | A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study |
| title_short | A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study |
| title_sort | nomogram model that predicts the risk of diabetic nephropathy in type 2 diabetes mellitus patients a retrospective study |
| url | http://dx.doi.org/10.1155/2021/6672444 |
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