Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population in Xinjiang, China

Abstract

Yongsheng Li,1,* Cheng Li,2,* Shi Zhao,3 Yi Yin,4 Xueliang Zhang,5 Kai Wang5 1College of Public Health, Xinjiang Medical University, Urumqi, 830011, People’s Republic of China; 2Center for Data Statistics and Analysis, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China; 3JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, 999077, People’s Republic of China; 4Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People’s Republic of China; 5Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xueliang Zhang; Kai Wang, Department of Medical Engineering and Technology, Xinjiang Medical University, No. 567 Shangde North Road, Shuimogou District, Urumqi City, Xinjiang, 830011, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected]; [email protected]: To establish an accurate risk prediction model of diabetic retinopathy (DR) using cost effective and easily available patients’ characteristics and clinical biomarkers.Patients and Methods: Totally 18,904 cases diagnosed type 2 diabetes mellitus (T2DM) were collected, among which 13,980 cases were selected after quality screening. The least absolute shrinkage and selection operator (LASSO) regression models were used for univariate analysis and factors selection, and the multi-factor logistic regression analysis was used to establish the prediction model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using AUC/ Harrell’s C statistic, calibration plot, and decision curve analysis. Both the development group and validation group were assessed.Results: Candidate variables were selected by Lasso regression and multivariate logistic regression analysis. Finally, the candidate predictive variables were included diabetic peripheral neuropathy (DPN), age, neutrophilic granulocyte (NE), high-density lipoprotein (HDL), hemoglobin A1c (HbA1C), duration of T2DM, and glycosylated serum protein (GSP) were used to establish a nomogram model for predicting the risk of DR. In the development group, the area under the receiver operating characteristic curve (AUC) was 0.882 (95% CI, 0.875– 0.888). In the validation group, the AUC was 0.870 (95% CI, 0.856– 0.881). Meanwhile, the optimism-corrected Harrell’s C statistic were 0.878 and 0.867 in the development group and the validation group, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful.Conclusion: We constructed and verified nomograms that could accurately predict the risk of DR in T2DM patients, which could be used to predict the personalized risk of DR patients in Xinjiang, China.Keywords: diabetic peripheral neuropathy, risk factors, prediction model, nomogram

Keywords

• diabetic peripheral neuropathy
• risk factors
• prediction model
• nomogram