Dongling Niu, Ziwei Kang, Juanling Sun, Li Zhang, Chang Wang, Ting Lei, Hongli Liu, Yanchun Zhang
Xi'an People's Hospital (Xi'an Fourth Hospital), Affiliated People's Hospital of Northwest University, Xi'an, Shaanxi, China.
Department of Fundus Surgery (Division IV), Shaanxi Eye Hospital, Xi'an, Shaanxi, China.
Front Endocrinol (Lausanne). 2025 Jun 12;16:1557166. doi: 10.3389/fendo.2025.1557166. eCollection 2025.
This study aimed to develop and validate a universal nomogram for predicting referable diabetic retinopathy (RDR) in type 2 diabetes mellitus (T2DM) patients, using easily accessible clinical indicators for both community and ophthalmic outpatient populations.
A cross-sectional study was conducted with 1,830 T2DM patients from 14 communities in Xi'an, Shaanxi, China. Participants completed questionnaires, underwent physical exams, and ophthalmic assessments. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression identified key predictors for RDR. A nomogram was developed using multivariable logistic regression. Model performance was evaluated through area under the curve (AUC), accuracy, precision, recall, F1 score, Youden index, calibration curves, and decision curve analysis (DCA). The dataset was split into training (80%) and test (20%) sets, with external validation using 123 T2DM outpatients from Shaanxi Eye Hospital.
Seven key predictors were identified: serum creatinine, urea nitrogen, urine glucose, HbA1c, urinary microalbumin, diabetes duration, and systolic blood pressure. The nomogram exhibited moderate predictive accuracy, with AUCs of 0.730 (95% CI: 0.691-0.759), 0.767 (95% CI: 0.704-0.831), and 0.723 (95% CI: 0.610-0.835) for the training, test, and external validation sets, respectively. DCA showed that using the model is beneficial for threshold probabilities between 8% and 72%, supporting its broad clinical utility.
This nomogram, based on readily available clinical indicators, provides a reliable and scalable tool for predicting RDR risk in both community and ophthalmic settings. It offers a practical solution for early detection and personalized management of RDR, with broad applicability and clinical potential.
本研究旨在开发并验证一种通用列线图，用于预测2型糖尿病（T2DM）患者的可转诊糖尿病视网膜病变（RDR），该列线图使用社区和眼科门诊人群均可轻松获取的临床指标。
对来自中国陕西省西安市14个社区的1830例T2DM患者进行了一项横断面研究。参与者完成问卷调查、体格检查和眼科评估。单因素分析和最小绝对收缩和选择算子（LASSO）回归确定了RDR的关键预测因素。使用多变量逻辑回归开发了列线图。通过曲线下面积（AUC）、准确性、精确性、召回率、F1分数、约登指数、校准曲线和决策曲线分析（DCA）评估模型性能。数据集被分为训练集（80%）和测试集（20%），并使用来自陕西眼科医院的123例T2DM门诊患者进行外部验证。
确定了七个关键预测因素：血清肌酐、尿素氮、尿糖、糖化血红蛋白、尿微量白蛋白、糖尿病病程和收缩压。列线图显示出中等预测准确性，训练集、测试集和外部验证集的AUC分别为0.730（95%CI：0.691 - 0.759）、0.767（95%CI：0.704 - 0.831）和0.723（95%CI：0.610 - 0.835）。DCA表明，对于8%至72%的阈值概率，使用该模型是有益的，支持其广泛的临床实用性。
该列线图基于易于获得的临床指标，为社区和眼科环境中预测RDR风险提供了一种可靠且可扩展的工具。它为RDR的早期检测和个性化管理提供了一种实用解决方案，具有广泛的适用性和临床潜力。
