目的：分析筛选糖尿病患者发生视网膜病变的影响因素，建立列线图预测模型并进行验证分析。

方法：选取2013-01/2021-01国家人口健康科学数据中心数据仓储PHDA的《糖尿病并发症预警数据集》中的1 252例患者为研究对象，通过随机化将研究对象划分为建模组(n=941)和验证组(n=311)。采用单因素分析、LASSO回归和Logistic回归分析筛选影响糖尿病患者并发视网膜病变的影响因素，构建列线图预测模型，采用ROC曲线、校正曲线和Hosmer-Lemeshow检验对模型进行验证评估，采用临床决策曲线(DCA)评估模型的临床收益情况。

结果：年龄、高血压、肾病、收缩压(SBP)、糖化血红蛋白(HbA1c)、高密度脂蛋白胆固醇(HDL-C)、血尿素(BU)是糖尿病患者发生视网膜病变的影响因素。建模组AUC值为0.792(95%CI：0.763-0.821)，验证组AUC值为0.769(95%CI：0.716-0.822)； Hosmer-Lemeshow检验和校准曲线提示模型拟合度较高(建模组：χ²=14.520，P=0.069； 验证组：χ²=14.400，P=0.072)，DCA曲线结果显示，建模组和验证组的阈值概率分别为0.09-0.89和0.07-0.84，临床净收益较高。

结论：本研究构建包含年龄、高血压、肾病、SBP、HbA1c、HDL-C、BU的风险预测模型，该模型有较高的区分度和一致性，可用于预测糖尿病患者发生视网膜病变的风险。

METHODS: A total of 1 252 patients from the Diabetes Complications Early Warning Dataset of the National Population Health Data Archive(PHDA)between January 2013 to January 2021 were selected and randomly divided into a modeling group(n=941)and a validation group(n=311). Univariate analysis, LASSO regression and Logistic regression analysis were used to screen out the influencing factors of diabetic retinopathy, and a nomogram prediction model was established. The receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve were used to evaluate the model. The clinical benefit was evaluated by the decision curve analysis(DCA).

RESULTS: Age, hypertension, nephropathy, systolic blood pressure(SBP), glycated hemoglobin(HbA1c), high-density lipoprotein cholesterol(HDL-C), and blood urea(BU)were the influencing factors of diabetic retinopathy. The area under the curve(AUC)of the modeling group was 0.792(95%CI: 0.763-0.821), and the AUC of the validation group was 0.769(95%CI: 0.716-0.822). The Hosmer-Lemeshow goodness of fit test and calibration curve suggested that the theoretical value of the model was in good agreement(modeling group: χ²=14.520, P=0.069; validation group: χ²=14.400, P=0.072). The DCA results showed that the threshold probabilities range was 0.09-0.89 for modeling group and 0.07-0.84 for the validation group, which suggested the clinical net benefit was higher.

CONCLUSION: This study constructed a risk prediction model including age, hypertension, nephropathy, SBP, HbA1c, HDL-C, and BU. The model has a high discrimination and consistency, and can be used to predict the risk of diabetic retinopathy in patients with diabetes.