This study investigates independent risk factors for postoperative internal fixation device infection in patients with maxillofacial fractures and proposes an early warning model based on the synthetic minority over-sampling technique (SMOTE) algorithm. A total of 1 104 patients who underwent surgical treatment for maxillofacial fractures at Oral and Maxillofacial Surgery Department, Affiliated Hospital of Nantong University from January 2021 to December 2024 were retrospectively analyzed. The patients were divided into two groups based on the presence of postoperative internal fixation device infection: the infection group (27 cases) and non-infection group (1 077 cases). Clinical data from both groups were collected and subjected to statistical analysis. Univariate and binary Logistic regression analysis were used to identify risk factors for postoperative internal fixation device infection in maxillofacial fractures. Subsequently, a Logistic regression model was established, and the dataset was improved based on the SMOTE algorithm to construct an early warning model with the improved dataset. The prediction performance of the models was compared and validated. Among the 1 104 patients who underwent surgical treatment for maxillofacial fractures, 27 cases of postoperative internal fixation device infections were identified, corresponding to an infection rate of 2.45% (27/1 104). Age, diabetes history, fracture severity, and oral hygiene status were all identified as risk factors for postoperative internal fixation device infections in maxillofacial fractures (all P<0.05). The prediction model based on the original data (P1). The prediction model based on the SMOTE algorithm (P2). Receiver operating characteristic (ROC) curve analysis shows that the area under curve (AUC) for the P2 model was 0.882, the P1 model was 0.861, indicating the superior predictive performance of the P2 model. The DeLong test results show that the difference in AUC between the two models was statistically significant (P<0.05). Age, diabetes history, postoperative fracture severity, and oral hygiene status are all risk factors for infections associated with internal fixation devices after maxillofacial fracture surgery. The proposed early warning model demonstrated good predictive performance. Medical professionals can utilize this model to effectively intervene and anticipate infections related to internal fixation devices after maxillofacial fracture surgery. 目的: 探索颌面部骨折术后内固定装置感染的独立风险因素，并基于合成少数类过采样技术（SMOTE）算法构建预警模型。方法: 选取2021年1月—2024年12月期间于南通大学附属医院口腔颌面外科进行诊治的颌面部骨折手术患者1 104例为研究对象，根据患者术后是否发生内固定装置感染分为装置感染组（27例）与非装置感染组（1 077例）。收集并分析2组患者的临床资料，运用单因素及二元Logistic回归分析方法筛选颌面部骨折术后内固定装置感染的危险因素，并进行Logistic回归分析，同时基于SMOTE算法改进数据集，构建改进数据集的预警模型，并对比验证模型的预测效能。结果: 1 104例颌面部骨折术后内固定装置感染者27例，其发生率为2.45%（27/1 104）。年龄、糖尿病史、骨折严重程度及口腔卫生状况均为颌面部骨折术后内固定装置感染的危险因素（P值均<0.05）;原始预警模型P1的受试者工作特征（ROC）曲线下面积（AUC）为0.861，基于SMOTE算法的预警模型P2的AUC为0.882，P2模型的预测效能优于P1模型。DeLong检验结果显示，2种模型在AUC上的差异具有统计学意义（P<0.05）。结论: 年龄、糖尿病史、骨折严重程度及口腔卫生状况均为颌面部骨折术后内固定装置感染的危险因素。本研究基于SMOTE算法构建的颌面部骨折术后内固定装置感染的预警模型具有较好的预测效能，医护人员可据此进行有效干预，以预判颌面部骨折术后内固定装置感染情况。.
使用 AI 将内容摘要翻译为中文，便于快速阅读
使用 AI 分析这篇文章的核心发现、关键要点和深度见解
由 DeepSeek AI 提供分析 · 首次使用需配置 API Key
arXiv · 2025-11-18
arXiv · 2024-05-28
arXiv · 2025-11-18
arXiv · 2022-05-17