Development and Validation of a Predictive Risk Model for Vision-threatening Diabetic Retinopathy in Patients with Type 2 Diabetes

LUO Jin; HUANG Wen-yong; LI Yu-ting; ZHANG Jian; CHEN Min-yu; CHEN Shu-hui; LIU Jia-hui; HUANG Sheng-song

doi:10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).20231026.001

您当前的位置：

首页 >

文章列表页 >

Development and Validation of a Predictive Risk Model for Vision-threatening Diabetic Retinopathy in Patients with Type 2 Diabetes

Clinical Research | 更新时间：2024-02-19

- Development and Validation of a Predictive Risk Model for Vision-threatening Diabetic Retinopathy in Patients with Type 2 Diabetes
- Journal of Sun Yat-sen University(Medical Sciences) Vol. 44, Issue 6, Pages: 999-1007(2023)
- 作者机构：
  
  1.中山大学中山眼科中心//眼病防治全国重点实验室//广东省眼科学视觉科学重点实验室,广东,广州,510060
  2.南方医科大学第十附属医院//东莞市人民医院,广东,东莞,523059
- 作者简介：
  
  HUANG Sheng-song； E-mail： hshengs@mail.sysu.edu.cn
- 基金信息：
- DOI：10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).20231026.001
  CLC： R77
- Published：20 November 2023，
  
  Received：06 September 2023，
扫描看全文
罗瑾,黄文勇,黎宇婷等.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[J].中山大学学报（医学科学版）,2023,44(06):999-1007.

LUO Jin,HUANG Wen-yong,LI Yu-ting,et al.Development and Validation of a Predictive Risk Model for Vision-threatening Diabetic Retinopathy in Patients with Type 2 Diabetes[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(06):999-1007.
罗瑾,黄文勇,黎宇婷等.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[J].中山大学学报（医学科学版）,2023,44(06):999-1007. DOI： 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).20231026.001.

LUO Jin,HUANG Wen-yong,LI Yu-ting,et al.Development and Validation of a Predictive Risk Model for Vision-threatening Diabetic Retinopathy in Patients with Type 2 Diabetes[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(06):999-1007. DOI： 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).20231026.001.

摘要

目的

基于简单易得的临床资料，开发并验证２型糖尿病（T2DM）患者并发威胁视力的视网膜病变（VTDR）的风险预测模型，为基层医院提供便捷有效的预测工具，以便早期识别和转诊高危人群。

方法

使用2017年至2020年广州糖尿病眼病研究中T2DM患者临床数据构建列线图预测模型。使用Logistic回归分析VTDR的影响因素并建立模型，受试者工作特征曲线（ROC）、Hosmer-Lemeshow检验、校准曲线、决策曲线（DCA）用于评价模型的性能。使用

折交叉验证得到的平均ROC下面积对模型进行内部验证，使用东莞眼科研究数据对模型进行外部验证。

结果

建模集共纳入患者2 161例，并发VTDR者135例（6.25%）。年龄（

＜0.001，OR=0.927，95%CI：0.898~0.957）、体质量指数（

＜0.001，OR =0.845，95%CI：0.821~0.932）与VTDR负相关，糖尿病病程（

＜0.001，OR=1.064，95%CI：1.035~1.094）、是否使用胰岛素（

=0.045，OR =1.534，95%CI：1.010~2.332）、收缩压（

＜0.001，OR =1.019，95%CI：1.008~1.029）、糖化血红蛋白（

＜0.001，OR =1.484，95%CI：1.341~1.643）和血清肌酐（

＜0.001，OR =1.017，95%CI：1.010~1.023）与VTDR正相关，均被纳入模型。ROC提示建模集和验证集中该模型预测VTDR发生的曲线下面积分别为0.797和0.762；Hosmer-Lemeshow检验（

＞0.05）及校准曲线表明预测概率与观测概率具有较高一致性；DCA表明模型在建模集和验证集中均能产生净效益。

结论

年龄、糖尿病病程、是否使用胰岛素、体质量指数、收缩压、糖化血红蛋白和血清肌酐是VTDR的独立影响因素，基于上述变量构建的列线图模型有良好的预测效力，可以为基层医院早期识别和转诊VTDR提供科学依据，值得应用推广。

Abstract

Objective

To 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.

Methods

A 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

-fold cross-validation method， and further external validation was conducted in the Dongguan Eye Study.

Results

A total of 2 161 individuals were included in the model development dataset， of whom 135 （6.25%） people were diagnosed with VTDR. Age （

＜0.001，OR=0.927，95%CI：0.898~0.957） and body mass index （

＜0.001，OR =0.845，95%CI：0.821~0.932） were found to be negatively correlated with VTDR， whereas diabetes duration （

＜0.001，OR=1.064，95%CI：1.035~1.094）， insulin use （

=0.045，OR =1.534，95%CI：1.010~2.332）， systolic blood pressure （

＜0.001，OR =1.019，95%CI：1.008~1.029）， glycated hemoglobin （

＜0.001，OR =1.484，95%CI：1.341~1.643）， and serum creatinine （

＜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（

＞0.05）and the calibration curve displayed good agreement. Decision curve analysis showed that the nomogram produced net benefit in the two datasets.

Conclusions

Independent 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.

关键词

2型糖尿病威胁视力的糖尿病视网膜病变影响因素预测模型列线图

Keywords

type 2 diabetesvision-threatening diabetic retinopathyinfluencing factorspredictive modelnomogram

references

Steinmetz JD， Bourne RRA， Briant PS， et al. Causes of blindness and vision impairment in 2020 and trends over 30 years， and prevalence of avoidable blindness in relation to VISION 2020： the Right to Sight： an analysis for the Global Burden of Disease Study［J］.Lancet Glob Health， 2021， 9（2）： e144-e160.

刘明珠，管怀进.糖尿病前期和糖尿病视网膜病变临床前期的视网膜改变［J］.眼科学报，2023，38（6）：454-460.

Liu MZ ，Guan HJ .Retinal changes in pre-diabetes and pre-clinic diabetes retinopathy［J］.Eye Science， 2023， 38（6）：454-460.

American Diabetes Association. 11. Microvascular complications and foot care： standards of medical care in diabetes-2020［J］. Diabetes Care， 2020， 43（Supplement_1）： S135-S151.

刘涵，方晏红，陈健. 糖尿病视网膜病变的全科医学管理［J］. 眼科学报， 2023， 38（4）： 350-359.

Liu H， Fang YH， Chen J. General practice management of diabetic retinopathy［J］. Eye Science， 2023， 38（4）： 350-359.

Vujosevic S， Aldington SJ， Silva P， et al. Screening for diabetic retinopathy： new perspectives and challenges［J］. Lancet Diabetes Endocrinol， 2020， 8（4）： 337-347.

Ipp E， Liljenquist D， Bode B， et al. Pivotal evaluation of an artificial intelligence system for autonomous detection of referrable and vision-threatening diabetic retinopathy［J］. JAMA Netw Open， 2021， 4（11）： e2134254.

Toll DB， Janssen KJM， Vergouwe Y， et al. Validation， updating and impact of clinical prediction rules： a review［J］. J Clin Epidemiol， 2008， 61（11）： 1085-1094.

Aspelund T， Thornórisdóttir O， Olafsdottir E， et al. Individual risk assessment and information technology to optimise screening frequency for diabetic retinopathy［J］. Diabetologia， 2011， 54（10）： 2525-2532.

Nugawela MD， Gurudas S， Prevost AT， et al. Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings［J］. EClinicalMedicine， 2022， 51： 101578.

Ke J， Li K， Cao B. A nomogram for predicting vision-threatening diabetic retinopathy among mild diabetic retinopathy patients： a case-control and prospective study of type 2 diabetes［J］. Diabetes Metab Syndr Obes， 2023， Volume 16： 275-283.

Zhans S， Chen Y， Wang L， et al. Design and baseline data of the diabetes registration study： Guangzhou Diabetic Eye Study［J］. Curr Eye Res， 2023， 48（6）： 591-599.

Meng Q， Cui Y， Zhang M， et al. Design and baseline characteristics of a population-based study of eye disease in southern Chinese people： the Dongguan Eye Study［J］. Clin Exp Ophthalmol， 2016， 44（3）： 170-180.

Riley RD， Ensor J， Snell KIE， et al. Calculating the sample size required for developing a clinical prediction model［J］. BMJ， 2020， 368： m441.

Wong TY， Cheung N， Tay WT， et al. Prevalence and risk factors for diabetic Retinopathy［J］. Ophthalmology， 2008， 115（11）： 1869-1875.

Varma R， Macias GL， Torres M， et al. Biologic risk factors associated with diabetic retinopathy［J］. Ophthalmology， 2007， 114（7）： 1332-1340.

Ting DSW， CHEUNGheung GCM， Wong TY. Diabetic retinopathy： global prevalence， major risk factors， screening practices and public health challenges： a review［J］. Clin Exp Ophthalmol， 2016， 44（4）： 260-277.

Zhang X， Saaddine JB， Chou CF， et al. Prevalence of diabetic retinopathy in the United States， 2005-2008［J］. JAMA， 2010， 304（6）： 649.

Rajalakshmi R， Shanthi Rani CS， Venkatesan U， et al. Correlation between markers of renal function and sight-threatening diabetic retinopathy in type 2 diabetes： a longitudinal study in an Indian clinic population［J］. BMJ Open Diab Res Care， 2020， 8（1）： e001325.

Zoungas S， Woodward M， Li Q， et al. Impact of age， age at diagnosis and duration of diabetes on the risk of macrovascular and microvascular complications and death in type 2 diabetes［J］. Diabetologia， 2014， 57（12）： 2465-2474.

Nanayakkara N， Ranasinha S， Gadowski A， et al. Age， age at diagnosis and diabetes duration are all associated with vascular complications in type 2 diabetes［J］. J Diabetes Complications， 2018， 32（3）： 279-290.

Wong J， Molyneaux L， Constantino M， et al. Timing is everything： age of onset influences long-term retinopathy risk in type 2 diabetes， independent of traditional risk factors［J］. Diabetes Care， 2008， 31（10）： 1985-1990.

DIirani M， Xie J， Fenwick E， et al. Are obesity and anthropometry risk factors for diabetic retinopathy？ The diabetes management project［J］. Invest Ophthalmol Vis Sci， 2011，52（7）：4416-4421.

Diabetes Prevention Program Research Group. The prevalence of retinopathy in impaired glucose tolerance and recent-onset diabetes in the diabetes prevention program［J］. Diabet Med， 2007， 24（2）： 137-144.

Han X， Jiang Y， Niu Y， et al. Differential associations between body mass index with diabetes and vision-threatening diabetic retinopathy in an adult Chinese population［J］. Br J Ophthalmol， 2022，106（6）：852-856.

Klein R. The wisconsin epidemiologic study of diabetic retinopathy［J］. Arch Ophthalmol， 1984， 102（4）： 527.

Looker HC， Nyangoma SO， Cromie D， et al. Diabetic retinopathy at diagnosis of type 2 diabetes in Scotland［J］. Diabetologia， 2012， 55（9）： 2335-2342.

Sabanayagam C， Sultana R， Banu R， et al. Association between body mass index and diabetic retinopathy in Asians： the Asian Eye Epidemiology Consortium （AEEC） study［J］. Br J Ophthalmol， 2022， 106（7）： 980-986.

Wang Y， Wan H， Chen Y， et al. Association of C-peptide with diabetic vascular complications in type 2 diabetes［J］. Diabetes Metab， 2020， 46（1）： 33-40.

Huang Y， Wang Y， Liu C， et al. C‐peptide， glycaemic control， and diabetic complications in type 2 diabetes mellitus： a real‐world study［J］. Diabetes Metab Res Rev， 2022，38（4）：e3514.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Correlation between Serum Osteoprotegerin/Soluble Receptor Activator of Nuclear Factor-κβ Ligand Levels and the Left Ventricular Diastolic Dysfunction in Type 2 Diabetes

Risk Prediction Model for Pulmonary Fungal Infections in Patients with Lung Cancer

Risk Factors and Predictive Model for Severe Myelosuppression due to Chemotherapy in Triple-negative Breast Cancer

Influencing Factors of HIV/ AIDS Death Based on Bayesian Cox Proportional Hazard Regression Model

Related Author

HUANG Sheng-song

ZHENG Ben-rong

TANG Xi-xiang

JIANG Bo-xiong

LI Mei

WANG Yi-na

DU Wei-wei

JI Wen-tao

Related Institution

VIP Medical Service Center， The Third Affiliated Hospital of Sun Yat-sen University

Department of Respiratory and Critical Care Medicine， Zhongshan City People's Hospital

Graduate School of Zunyi Medical University Zhuhai Campus

Department of Breast Surgery, The Second Affiliated Hospital of Nanchang University

Department of General Surgery, The Second Affiliated Hospital of Jiujiang College

Postal code：510080
Tel：020-87331643，87330827 Email：xbmed@mail.sysu.edu.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰