Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy

Di Gong; Lyujie Fang; Yixian Cai; Ieng Chong; Junhong Guo; Zhichao Yan; Xiaoli Shen; Weihua Yang; Jiantao Wang

doi:10.3389/fendo.2023.1244601

Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy

Front Endocrinol (Lausanne). 2023 Aug 24:14:1244601. doi: 10.3389/fendo.2023.1244601. eCollection 2023.

Authors

Di Gong^{1

2}, Lyujie Fang², Yixian Cai², Ieng Chong³, Junhong Guo⁴, Zhichao Yan⁴, Xiaoli Shen⁴, Weihua Yang⁴, Jiantao Wang⁴

Affiliations

¹ Shenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong, China.
² The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, Guangdong, China.
³ Macau University Hospital, Macao, Macao SAR, China.
⁴ Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, Guangdong, China.

Abstract

Objective: This study aims to develop and evaluate a non-imaging clinical data-based nomogram for predicting the risk of vision-threatening diabetic retinopathy (VTDR) in diabetes mellitus type 2 (T2DM) patients.

Methods: Based on the baseline data of the Guangdong Shaoguan Diabetes Cohort Study conducted by the Zhongshan Ophthalmic Center (ZOC) in 2019, 2294 complete data of T2DM patients were randomly divided into a training set (n=1605) and a testing set (n=689). Independent risk factors were selected through univariate and multivariate logistic regression analysis on the training dataset, and a nomogram was constructed for predicting the risk of VTDR in T2DM patients. The model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) in the training and testing datasets to assess discrimination, and Hosmer-Lemeshow test and calibration curves to assess calibration.

Results: The results of the multivariate logistic regression analysis showed that Age (OR = 0.954, 95% CI: 0.940-0.969, p = 0.000), BMI (OR = 0.942, 95% CI: 0.902-0.984, p = 0.007), systolic blood pressure (SBP) (OR =1.014, 95% CI: 1.007-1.022, p = 0.000), diabetes duration (10-15y: OR =3.126, 95% CI: 2.087-4.682, p = 0.000; >15y: OR =3.750, 95% CI: 2.362-5.954, p = 0.000), and glycated hemoglobin (HbA1C) (OR = 1.325, 95% CI: 1.221-1.438, p = 0.000) were independent risk factors for T2DM patients with VTDR. A nomogram was constructed using these variables. The model discrimination results showed an AUC of 0.7193 for the training set and 0.6897 for the testing set. The Hosmer-Lemeshow test results showed a high consistency between the predicted and observed probabilities for both the training set (Chi-square=2.2029, P=0.9742) and the testing set (Chi-square=7.6628, P=0.4671).

Conclusion: The introduction of Age, BMI, SBP, Duration, and HbA1C as variables helps to stratify the risk of T2DM patients with VTDR.

Keywords: diabetes mellitus type 2; diabetic retinopathy; prediction model; risk factors; vision-threatening diabetic retinopathy.

Publication types

Randomized Controlled Trial
Research Support, Non-U.S. Gov't

MeSH terms

Cohort Studies
Diabetes Mellitus, Type 2* / complications
Diabetic Retinopathy* / diagnosis
Diabetic Retinopathy* / etiology
Glycated Hemoglobin
Humans
Risk Factors

Substances

Glycated Hemoglobin

Grants and funding

This work was supported by National Nature Science Foundation of China (82070961), Supported by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No.SZGSP014), Supported by Shenzhen Key Medical Discipline Construction Fund (No.SZXK037), Supported by Shenzhen Science and Technology Program (No.JCYJ20220818103207015), Supported by the Medical Science and Technology Research Fund Project of Guangdong Province (No.A2022403), Supported by Science and Technology Projects in Guangzhou (No.202201020089).