Department of Orthopedic Surgery, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Neonatology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Front Endocrinol (Lausanne). 2022 Jul 13;13:890057. doi: 10.3389/fendo.2022.890057. eCollection 2022.
To develop and validate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes mellitus (T2DM) and evaluate its clinical application value.
We retrospectively collected clinical data from 1,950 patients with T2DM from the Second Affiliated Hospital of Xi'an Jiaotong University between January 2012 and June 2021. The patients were divided into training cohort and validation cohort according to the random number table method at a ratio of 7:3. The independent risk factors for diabetic foot among patients with T2DM were identified by multivariate logistic regression analysis. Then, a nomogram prediction model was developed using the independent risk factors. The model performances were evaluated by the area under the receiver operating characteristic curve (AUC), calibration plot, Hosmer-Lemeshow test, and the decision curve analysis (DCA).
Multivariate logistic regression analysis indicated that age, hemoglobin A1c (HbA1c), low-density lipoprotein (LDL), total cholesterol (TC), smoke, and drink were independent risk factors for diabetic foot among patients with T2DM ( < 0.05). The AUCs of training cohort and validation cohort were 0.806 (95% CI: 0.775∼0.837) and 0.857 (95% CI: 0.814∼0.899), respectively, suggesting good discrimination of the model. Calibration curves of training cohort and validation cohort showed a favorable consistency between the predicted probability and the actual probability. In addition, the values of Hosmer-Lemeshow test for training cohort and validation cohort were 0.826 and 0.480, respectively, suggesting a high calibration of the model. When the threshold probability was set as 11.6% in the DCA curve, the clinical net benefits of training cohort and validation cohort were 58% and 65%, respectively, indicating good clinical usefulness of the model.
We developed and validated a user-friendly nomogram prediction model for the risk of diabetic foot in patients with T2DM. Nomograms may help clinicians early screen and identify patients at high risk of diabetic foot.
建立并验证 2 型糖尿病患者糖尿病足风险的列线图预测模型，并评估其临床应用价值。
回顾性收集 2012 年 1 月至 2021 年 6 月西安交通大学第二附属医院 1950 例 2 型糖尿病患者的临床资料，采用随机数字表法将患者分为训练集和验证集，比例为 7:3。采用多因素 logistic 回归分析确定 2 型糖尿病患者糖尿病足的独立危险因素。然后，使用独立危险因素建立列线图预测模型。通过受试者工作特征曲线（ROC）下面积（AUC）、校准图、Hosmer-Lemeshow 检验和决策曲线分析（DCA）评估模型性能。
多因素 logistic 回归分析表明，年龄、糖化血红蛋白（HbA1c）、低密度脂蛋白（LDL）、总胆固醇（TC）、吸烟和饮酒是 2 型糖尿病患者糖尿病足的独立危险因素（<0.05）。训练集和验证集的 AUC 分别为 0.806（95%CI：0.775∼0.837）和 0.857（95%CI：0.814∼0.899），提示模型具有良好的区分度。训练集和验证集的校准曲线显示预测概率与实际概率之间具有良好的一致性。此外，训练集和验证集的 Hosmer-Lemeshow 检验值分别为 0.826 和 0.480，提示模型具有较高的校准度。在 DCA 曲线中，当阈值概率设定为 11.6%时，训练集和验证集的临床净获益分别为 58%和 65%，提示模型具有良好的临床实用性。
我们建立并验证了一个用于 2 型糖尿病患者糖尿病足风险的易于使用的列线图预测模型。列线图可以帮助临床医生早期筛选和识别糖尿病足高危患者。
