Zheng Liran, Chen Jiageng, Xu Wenyan, Ding Min, Li Juan, Tian Fenghua, Zhang Lei, Li Qianqian, Wang Shuai, Wang Zeyu, Ma Hairong, Cui Xuecan, Chang Bai, Wang Meijun
NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
Human Resources Department, Tianjin Medical University, Tianjin, China.
Front Endocrinol (Lausanne). 2025 Aug 1;16:1595471. doi: 10.3389/fendo.2025.1595471. eCollection 2025.
Diabetic Foot (DF), as a serious complication of diabetes, is closely related to major adverse cardiovascular events (MACE) and mortality. However, research on predictive models for the MACE risk in DF patients is not sufficient. The purpose of this study is to construct a prognostic model for the MACE risk in patients with diabetic foot ulcers and provide a reference tool for clinical individualized management.
This study retrospectively collected data of DF patients who were hospitalized and met the inclusion and exclusion criteria in a tertiary first-class comprehensive hospital mainly engaged in metabolic diseases in Tianjin from January 2018 to January 2020. The follow-up outcome was the occurrence of MACE within 5 years after discharge. Multiple imputation (MI) method was used to fill in the missing data. Based on the processed data, in terms of modeling methods, the top three frequently used methods were used. Logistic regression, random forest (RF) and support vector machine (SVM) were used respectively to analyze influencing factors. The performance of each model was compared by using confusion matrix, ROC curve and AUC value. The data set was divided into training set and test set according to the proportion of 80%/20%. Finally, the model effect was verified on the test set. The study finally included a total of 504 patients with DF. Among them, 147 cases (29.17%) experienced MACE events within five years. The AUC of the RF model in this study was 0.70, the AUC of the Logistic regression model was 0.62, and the AUC of the SVM model was 0.60.
All three models established in this research have good clinical predictive ability. Among them, the clinical prediction model based on RF has the best effect and can effectively predict the risk of MACE in DF patients, helping clinical medical staff formulate personalized treatment plans.
糖尿病足（DF）作为糖尿病的一种严重并发症，与主要不良心血管事件（MACE）及死亡率密切相关。然而，关于DF患者MACE风险预测模型的研究尚不充分。本研究旨在构建糖尿病足溃疡患者MACE风险的预后模型，为临床个体化管理提供参考工具。
本研究回顾性收集了2018年1月至2020年1月在天津一家主要从事代谢疾病的三级甲等综合性医院住院且符合纳入和排除标准的DF患者的数据。随访结局为出院后5年内MACE的发生情况。采用多重填补（MI）方法填补缺失数据。基于处理后的数据，在建模方法方面，使用了三种常用的方法。分别采用逻辑回归、随机森林（RF）和支持向量机（SVM）分析影响因素。通过混淆矩阵、ROC曲线和AUC值比较各模型的性能。数据集按照80%/20%的比例分为训练集和测试集。最后，在测试集上验证模型效果。本研究最终共纳入504例DF患者。其中，147例（29.17%）在5年内发生了MACE事件。本研究中RF模型的AUC为0.70，逻辑回归模型的AUC为0.62，SVM模型的AUC为0.60。
本研究建立的三种模型均具有良好的临床预测能力。其中，基于RF的临床预测模型效果最佳，能够有效预测DF患者发生MACE的风险，有助于临床医务人员制定个性化治疗方案。
