Zhang Yanling, Long Kun, Gong Zhaojian, Dai Ruping, Zhang Shuiting
Department of Anaesthesiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
BMC Oral Health. 2025 Jan 30;25(1):165. doi: 10.1186/s12903-025-05555-9.
Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer.
A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated. The analysis encompassed 34 parameters, incorporating demographic and clinical characteristics, biochemical and hematological assay results, surgical-related data, hospitalization costs and stay in hospital. Six machine learning models were compared by the area under the receiver operating characteristic curve (AUC). The best-performing models were selected for further analyze, including feature importance evaluation and nomogram analysis, identifying key POF risk factors, and establish a comprehensive prediction model.
A total of 466 patients with surgery for oral cancer met the criteria, with an average age of (54.2 ± 11.1) years, including an POF group (n = 197) and a non-POF group (n = 269). The fever group has greater hospitalization costs, longer lengths of stay, and higher infection biochemical indicators (leucocyte ratio and erythrocyte sedimentation rate). Furthermore, Among the 6 machine learning models, logistic regression models performed best, with the higher AUC and accuracy. In univariate and multivariate logistic analysis showed that age, sex, reoperation, Charlson Comorbidity Index score (CCI), leukocyte, bleeding and blood transfusion were independent risk factors for POF of patients following surgery in oral cancer. Then seven variables were selected to establish the nomogram for predict the probability of POF by nomogram algorithm.
Postoperative fever patients following radical resection of oral cancer have greater burden. Machine learning algorithms can be effectively used to identify potential risk factors of POF, which may enhance individualized treatment plans in oral cancer patient during perioperative period.
术后发热（POF）是接受大手术患者的常见情况，对患者和外科医生而言仍然是挑战和负担。本研究旨在调查口腔癌手术后POF的发生率，识别风险因素，并建立基于机器学习的预测模型。
对727例连续接受口腔癌根治性切除术的患者进行回顾性研究。分析包括34个参数，涵盖人口统计学和临床特征、生化和血液学检测结果、手术相关数据、住院费用和住院时间。通过受试者操作特征曲线（AUC）下的面积比较6种机器学习模型。选择表现最佳的模型进行进一步分析，包括特征重要性评估和列线图分析，识别POF关键风险因素，并建立综合预测模型。
共有466例口腔癌手术患者符合标准，平均年龄为（54.2±11.1）岁，包括POF组（n = 197）和非POF组（n = 269）。发热组的住院费用更高、住院时间更长，感染生化指标（白细胞比率和红细胞沉降率）更高。此外，在6种机器学习模型中，逻辑回归模型表现最佳，AUC和准确性更高。单因素和多因素逻辑分析显示，年龄、性别、再次手术、Charlson合并症指数评分（CCI）、白细胞、出血和输血是口腔癌手术后患者POF的独立风险因素。然后选择7个变量通过列线图算法建立列线图以预测POF的概率。
口腔癌根治性切除术后的发热患者负担更大。机器学习算法可有效用于识别POF的潜在风险因素，这可能会加强口腔癌患者围手术期的个体化治疗方案。
