Development and Validation of a Predictive Risk Model for Vision-threatening Diabetic Retinopathy in Patients with Type 2 Diabetes
10.13471/j.cnki.j.sun.yat-sen.univ（med.sci）.20231026.001
- VernacularTitle:威胁视力的2型糖尿病视网膜病变风险预测模型的建立与验证
- Author:
Jin LUO
1
;
Wen-yong HUANG
1
;
Yu-ting LI
1
;
Jian ZHANG
1
;
Min-yu CHEN
2
;
Shu-hui CHEN
2
;
Jia-hui LIU
2
;
Sheng-song HUANG
1
Author Information
1. State Key Laboratory of Ophthalmology//Zhongshan Ophthalmic Center， Sun Yat-sen University//Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science， Guangzhou 510060， China
2. The Tenth Affiliated Hospital of Southern Medical University （Dongguan People's Hospital）， Dongguan 523059， China
- Publication Type:Journal Article
- Keywords:
type 2 diabetes;
vision-threatening diabetic retinopathy;
influencing factors;
predictive model;
nomogram
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2023;44(6):999-1007
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo develop and validate a predictive risk model for vision-threatening diabetic retinopathy in patients with type 2 diabetes using readily accessible clinical data， which may provide a convenient and effective prediction tool for early identification and referral of at-risk populations. MethodsA nomogram model was developed using a dataset obtained from patients with T2DM who participated in the Guangzhou Diabetic Eye Study from November 2017 to December 2020. Logistic regression was used to construct the model， and model performance was evaluated using receiver operating characteristic curve， Hosmer-Lemeshow test， calibration curve and decision curve analysis. The model underwent internal validation through the mean AUC of k-fold cross-validation method， and further external validation was conducted in the Dongguan Eye Study. ResultsA total of 2 161 individuals were included in the model development dataset， of whom 135 （6.25%） people were diagnosed with VTDR. Age （P＜0.001，OR=0.927，95%CI：0.898~0.957） and body mass index （P＜0.001，OR =0.845，95%CI：0.821~0.932） were found to be negatively correlated with VTDR， whereas diabetes duration （P＜0.001，OR=1.064，95%CI：1.035~1.094）， insulin use （P =0.045，OR =1.534，95%CI：1.010~2.332）， systolic blood pressure （P＜0.001，OR =1.019，95%CI：1.008~1.029）， glycated hemoglobin （P＜0.001，OR =1.484，95%CI：1.341~1.643）， and serum creatinine （P＜0.001，OR =1.017，95%CI：1.010~1.023） were positively correlated with VTDR. All these variables were included in the model as predictors. The model showed strong discrimination in the development dataset with an area under the receiver operating characteristic curve （AUC） of 0.797 and in the external validation dataset （AUC 0.762）. The Hosmer-Lemeshow test（P＞0.05）and the calibration curve displayed good agreement. Decision curve analysis showed that the nomogram produced net benefit in the two datasets. ConclusionsIndependent factors influencing VTDR include age， duration of diabetes mellitus， insulin use， body mass index， systolic blood pressure， glycosylated hemoglobin， and serum creatinine. The nomogram constructed using these variables demonstrates a high degree of predictive validity. The model can serve as a valuable tool for early detection and referral of VTDR in primary care clinics. Therefore， its application and promotion are highly recommended.
