开发并评估一种可预测 2 型糖尿病伴威胁视力的糖尿病视网膜病变患者的风险预测模型。
Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy.
机构信息
Shenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong, China.
The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, Guangdong, China.
出版信息
Front Endocrinol (Lausanne). 2023 Aug 24;14:1244601. doi: 10.3389/fendo.2023.1244601. eCollection 2023.
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, = 0.000), BMI (OR = 0.942, 95% CI: 0.902-0.984, = 0.007), systolic blood pressure (SBP) (OR =1.014, 95% CI: 1.007-1.022, = 0.000), diabetes duration (10-15y: OR =3.126, 95% CI: 2.087-4.682, = 0.000; >15y: OR =3.750, 95% CI: 2.362-5.954, = 0.000), and glycated hemoglobin (HbA1C) (OR = 1.325, 95% CI: 1.221-1.438, = 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, =0.9742) and the testing set (Chi-square=7.6628, =0.4671).
CONCLUSION
The introduction of Age, BMI, SBP, Duration, and HbA1C as variables helps to stratify the risk of T2DM patients with VTDR.
目的
本研究旨在开发和评估一种基于非影像临床数据的列线图，用于预测 2 型糖尿病（T2DM）患者发生威胁视力的糖尿病视网膜病变（VTDR）的风险。
方法
基于中山大学中山眼科中心 2019 年开展的广东韶关糖尿病队列研究的基线数据，纳入 2294 例完整 T2DM 患者数据，随机分为训练集（n=1605）和测试集（n=689）。采用单因素和多因素 logistic 回归分析对训练数据集进行分析，筛选出独立危险因素，并构建预测 T2DM 患者 VTDR 风险的列线图。采用受试者工作特征（ROC）曲线和曲线下面积（AUC）评估训练集和测试集模型的区分度，Hosmer-Lemeshow 检验和校准曲线评估模型的校准度。
结果
多因素 logistic 回归分析结果显示，年龄（OR=0.954，95%CI：0.940-0.969， =0.000）、BMI（OR=0.942，95%CI：0.902-0.984， =0.007）、收缩压（SBP）（OR=1.014，95%CI：1.007-1.022， =0.000）、糖尿病病程（10-15 年：OR=3.126，95%CI：2.087-4.682， =0.000；>15 年：OR=3.750，95%CI：2.362-5.954， =0.000）和糖化血红蛋白（HbA1C）（OR=1.325，95%CI：1.221-1.438， =0.000）是 T2DM 患者发生 VTDR 的独立危险因素。基于上述变量构建了列线图。模型的区分度结果显示，训练集的 AUC 为 0.7193，测试集的 AUC 为 0.6897。Hosmer-Lemeshow 检验结果显示，训练集（卡方=2.2029， =0.9742）和测试集（卡方=7.6628， =0.4671）的预测概率与实际概率均具有较高的一致性。
结论
引入年龄、BMI、SBP、病程和 HbA1C 等变量有助于对 T2DM 患者发生 VTDR 的风险进行分层。
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