Department of Endocrinology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Department of Dermatology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Front Endocrinol (Lausanne). 2022 Nov 16;13:993423. doi: 10.3389/fendo.2022.993423. eCollection 2022.
This study aims to develop a diabetic retinopathy (DR) hazard nomogram for a Chinese population of patients with type 2 diabetes mellitus (T2DM).
We constructed a nomogram model by including data from 213 patients with T2DM between January 2019 and May 2021 in the Affiliated Hospital of Zunyi Medical University. We used basic statistics and biochemical indicator tests to assess the risk of DR in patients with T2DM. The patient data were used to evaluate the DR risk using R software and a least absolute shrinkage and selection operator (LASSO) predictive model. Using multivariable Cox regression, we examined the risk factors of DR to reduce the LASSO penalty. The validation model, decision curve analysis, and C-index were tested on the calibration plot. The bootstrapping methodology was used to internally validate the accuracy of the nomogram.
The LASSO algorithm identified the following eight predictive variables from the 16 independent variables: disease duration, body mass index (BMI), fasting blood glucose (FPG), glycated hemoglobin (HbA1c), homeostatic model assessment-insulin resistance (HOMA-IR), triglyceride (TG), total cholesterol (TC), and vitamin D (VitD)-T3. The C-index was 0.848 (95% CI: 0.798-0.898), indicating the accuracy of the model. In the interval validation, high scores (0.816) are possible from an analysis of a DR nomogram's decision curve to predict DR.
We developed a non-parametric technique to predict the risk of DR based on disease duration, BMI, FPG, HbA1c, HOMA-IR, TG, TC, and VitD.
本研究旨在为中国 2 型糖尿病（T2DM）患者开发一种糖尿病视网膜病变（DR）危险诺模图。
我们通过纳入遵义医科大学附属医院 2019 年 1 月至 2021 年 5 月的 213 例 T2DM 患者的数据，构建了一个诺模图模型。我们使用基本统计和生化指标测试来评估 T2DM 患者发生 DR 的风险。使用 R 软件和最小绝对收缩和选择算子（LASSO）预测模型对患者数据进行 DR 风险评估。使用多变量 Cox 回归检查 DR 的风险因素，以减少 LASSO 惩罚。使用校准图测试验证模型、决策曲线分析和 C 指数。使用 bootstrap 方法对列线图的准确性进行内部验证。
LASSO 算法从 16 个独立变量中确定了以下八个预测变量：疾病持续时间、体重指数（BMI）、空腹血糖（FPG）、糖化血红蛋白（HbA1c）、稳态模型评估-胰岛素抵抗（HOMA-IR）、甘油三酯（TG）、总胆固醇（TC）和维生素 D（VitD）-T3。C 指数为 0.848（95%CI：0.798-0.898），表明模型的准确性。在间隔验证中，对 DR 列线图决策曲线的分析可能会得出高得分（0.816），以预测 DR。
我们开发了一种基于疾病持续时间、BMI、FPG、HbA1c、HOMA-IR、TG、TC 和 VitD 的非参数技术来预测 DR 的风险。
