Correlation study on the risk of cardiovascular adverse events in diabetic foot patients based on machine learning - a retrospective cohort study - Zheng, Liran; Chen, Jiageng; Xu, Wenyan; Ding, Min; Li, Juan; Tian, Fenghua; Zhang, Lei; Li, Qianqian; Wang, Shuai; Wang, Zeyu; Ma, Hairong; Cui, Xuecan; et al. (Frontiers in Endocrinology, 2025)
IntroductionDiabetic 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.MethodThis 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.ConclusionAll 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.