Zhang Yi, Sun Xingyu, Cheng Cheng, Hou Nianzong, Han Shiliang, Tang Xin
Department of Orthopedics, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Department of Hand and Foot Surgery, Zibo Central Hospital, Zibo, Shandong, China.
Front Endocrinol (Lausanne). 2025 Jul 29;16:1610884. doi: 10.3389/fendo.2025.1610884. eCollection 2025.
This study aims to improve the surgical cure rate, develop interventions to reduce the incidence of postoperative nonunion or recurrence of diabetic foot wounds, and formulate an optimal prediction model to quantify the predictive risk value of antibiotic bone-cement failure in the treatment of diabetic foot.
The training and test sets were created once the cases were collected. Based on feature correlation, feature importance, and feature weight, LASSO analysis, random forest, and the Pearson correlation coefficient approach were used to identify the features. Artificial neural network, support vector machine, and XGBoost prediction models were built according to the selected optimal features. The receiver operating characteristic curve, precision-recall (PR) curve, and decision curve analysis were utilized to validate the performance of the models and select the optimal prediction model. Lastly, an independent test set was created to assess and determine the best model's capacity for generalization.
A comparative analysis revealed that the area under the curve (AUC) for the training set of the PRL-XGBoost prediction model was 0.85 and that for the test set was 0.71. This finding suggests that the model exhibits good predictive ability. Moreover, the PR-AUC value of the prediction model was 0.97, indicating that it demonstrates good resistance to overfitting. Additionally, the DCA curve showed that the PRL-XGBoost prediction model has significant application value and practicality. Therefore, PRL-XGBoost was found to be the most effective prediction model.
The findings from this study prove that γ-glutamyl transpeptidase, lipoprotein A, peripheral vascular disease, peripheral neuropathy, and white blood cells are the key indices that affect the surgical outcome. These parameters determine the nutritional and immune status of the lower limb endings, leading to ulceration, infection, and nonunion of the diabetic foot. Hence, the PRL-XGBoost prediction model can be applied for the preoperative evaluation and screening of patients with diabetic foot treated with antibiotic bone cement, resulting in favorable clinical outcomes.
本研究旨在提高手术治愈率，制定降低糖尿病足伤口术后骨不连或复发发生率的干预措施，并建立一个优化的预测模型，以量化抗生素骨水泥在糖尿病足治疗中失效的预测风险值。
病例收集完成后创建训练集和测试集。基于特征相关性、特征重要性和特征权重，采用套索分析、随机森林和皮尔逊相关系数法来识别特征。根据选定的最优特征构建人工神经网络、支持向量机和XGBoost预测模型。利用受试者工作特征曲线、精确率-召回率（PR）曲线和决策曲线分析来验证模型性能并选择最优预测模型。最后，创建一个独立测试集来评估和确定最佳模型的泛化能力。
对比分析显示，PRL-XGBoost预测模型训练集的曲线下面积（AUC）为0.85，测试集的AUC为0.71。这表明该模型具有良好的预测能力。此外，预测模型的PR-AUC值为0.97，表明它具有良好的抗过拟合能力。另外，DCA曲线显示PRL-XGBoost预测模型具有显著的应用价值和实用性。因此，PRL-XGBoost被认为是最有效的预测模型。
本研究结果证明，γ-谷氨酰转肽酶、脂蛋白A、外周血管疾病、外周神经病变和白细胞是影响手术结果的关键指标。这些参数决定了下肢末端的营养和免疫状态，导致糖尿病足出现溃疡、感染和骨不连。因此，PRL-XGBoost预测模型可用于接受抗生素骨水泥治疗的糖尿病足患者的术前评估和筛查，从而获得良好的临床效果。
