2型糖尿病视网膜病变风险的列线图预测模型研究

    Nomogram prediction model for the risk of type 2 diabetic retinopathy

    • 摘要:
      目的 探讨2型糖尿病视网膜病变的风险因素, 建立并验证一种辅助临床预测2型糖尿病病人视网膜病变的可视化工具。
      方法 回顾性分析1172例完善眼底检查的2型糖尿病病人的临床资料, 利用logistic回归分析, 筛选出有统计学意义的变量, 建立模型和列线图。使用ROC曲线下面积(AUC)、校正曲线和决策曲线分析(DCA)来评估预测模型的分类性能、准确性和临床实用性。
      结果 年龄小、糖尿病病程长、糖化血红蛋白水平高、餐后1 h C肽水平低、尿蛋白阳性和红细胞分布宽度变异系数大是2型糖尿病视网膜病变风险因素(P < 0.05~P < 0.01), 训练集的AUC为0.772, 验证集的AUC为0.784, Hosmer-Lemeshow拟合优度检验显示拟合优度尚可(P>0.05)。DCA显示此模型可选的阈值概率范围大, 相对安全。
      结论 构建的列线图风险预测模型具有良好的性能, 指出了2型糖尿病视网膜病变发生风险指标, 可为2型糖尿病视网膜病变早期诊断及预防提供依据。

       

      Abstract:
      Objective To explore the risk factors of type 2 diabetic retinopathy, establish and verify a visual tool to assist clinical prediction of type 2 diabetic retinopathy patients.
      Methods The clinical data of 1172 type 2 diabetes patients with fundus examination were retrospectively analyzed.Using logistic regression analysis, statistically significant variables were screened out, and model and nomogram were established.The classification performance, accuracy and clinical practicality of the prediction model were evaluated using the area under the ROC curve (AUC), correction curve and decision curve analysis (DCA).
      Results Age, long course of diabetes, high level of glycosylated hemoglobin (HbA1c), low level of C-peptide 1 hour after meal, positive urinary protein and large coefficient of variation of red blood cell distribution width were risk factors for retinopathy in type 2 diabetes (P < 0.05 to P < 0.01).AUC in training set was 0.772, AUC in validation set was 0.784, and Hosmer-Lemeshow goodness of fit test showed that the goodness of fit was acceptable (P>0.05).DCA showed that the optional threshold probability range for this model was large and relatively safe.
      Conclusions The nomogram risk prediction model has good performance, which has indicated the risk indicators for type 2 diabetic retinopathy and can provide basis for early diagnosis and prevention of type 2 diabetic retinopathy.

       

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