Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study
Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collecte...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2020/7261047 |
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| _version_ | 1832568562731974656 |
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| author | Ruohui Mo Rong Shi Yuhong Hu Fan Hu |
| author_facet | Ruohui Mo Rong Shi Yuhong Hu Fan Hu |
| author_sort | Ruohui Mo |
| collection | DOAJ |
| description | Objectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals. |
| format | Article |
| id | doaj-art-463794662343486b860fb9e112dbdf58 |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-463794662343486b860fb9e112dbdf582025-02-03T00:58:49ZengWileyJournal of Diabetes Research2314-67452314-67532020-01-01202010.1155/2020/72610477261047Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective StudyRuohui Mo0Rong Shi1Yuhong Hu2Fan Hu3School of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, ChinaSchool of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, ChinaSchool of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, ChinaSchool of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, ChinaObjectives. This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). Methods. A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. Results. Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. Conclusion. Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.http://dx.doi.org/10.1155/2020/7261047 |
| spellingShingle | Ruohui Mo Rong Shi Yuhong Hu Fan Hu Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study Journal of Diabetes Research |
| title | Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study |
| title_full | Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study |
| title_fullStr | Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study |
| title_full_unstemmed | Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study |
| title_short | Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study |
| title_sort | nomogram based prediction of the risk of diabetic retinopathy a retrospective study |
| url | http://dx.doi.org/10.1155/2020/7261047 |
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