基于POSSUM评分的列线图模型对口腔癌术后肺部感染的预测价值

doi:10.19438/j.cjoms.2026.03.011

摘要/Abstract

摘要： 目的: 探讨口腔癌术后发生肺部感染的危险因素,并构建基于POSSUM评分的风险预测模型。方法: 采用回顾性队列研究设计,纳入2021年1月—2023年12月于蚌埠医科大学第一附属医院接受手术治疗的201例口腔癌患者,根据术后是否发生肺部感染分为感染组与未感染组。采用POSSUM评分系统评估患者围术期风险,通过单因素分析筛选潜在危险因素,多因素logistic回归确定独立危险因素。采用R语言rms程序包构建列线图预测模型,通过受试者工作特征（receiver operating characteristic,ROC）曲线、校准曲线及Hosmer-Lemeshow检验验证模型效能。结果: 201例患者中,术后肺部感染36例,无肺部感染165例。术后肺部感染发生率为17.9%,革兰阴性菌为主要病原菌。肺部感染组与未感染组患者是否气管切开、术后是否应用抗生素、住院天数、白蛋白、手术时间及POSSUM评分指标差异有统计学意义（P<0.05）。二元多因素logistic回归分析结果显示,是否气管切开、住院天数及POSSUM评分是口腔癌术后肺部感染的独立危险因素（P<0.05）。基于以上危险因素构建的风险预测模型的ROC曲线下面积为0.878,高于是否气管切开、住院天数及POSSUM评分的单独AUC值,同时具有较高的校准验证曲线可信度。Hosmer-Lemeshow拟合优度检验结果显示,列线图模型预测口腔癌术后发生肺部感染的一致性良好（χ²=4.064,P=0.851;df=8）。结论: 基于气管切开、住院天数及POSSUM评分构建的列线图模型,对口腔癌术后肺部感染具有良好的预测性能,可为临床早期识别高风险患者、制定个体化防控策略提供量化工具。

关键词: 口腔癌, 肺部感染, POSSUM 评分, 列线图, 风险预测

Abstract: PURPOSE: To explore the risk factors for postoperative pulmonary infection in patients with oral cancer and construct a risk prediction model based on the POSSUM score. METHODS: A retrospective cohort study was conducted, enrolling 201 patients with oral cancer who underwent surgical treatment at the First Affiliated Hospital of Bengbu Medical University from January 2021 to December 2023. The patients were divided into infection group and non-infection group according to the occurrence of postoperative pulmonary infection. The POSSUM scoring system was used to evaluate the perioperative risks of the patients. Potential risk factors were screened out by univariate analysis, and independent risk factors were identified by multivariate logistic regression analysis. The nomogram prediction model was established using the rms package in R language. The performance of the model was verified by the receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test. RESULTS: Among the 201 patients, totally 36 cases developed postoperative pulmonary infection and 165 cases had no infection, with the incidence rate of postoperative pulmonary infection being 17.9%. Gram-negative bacteria were the main pathogenic bacteria. Statistically significant differences were observed between the infection group and non-information group in terms of tracheotomy status, postoperative antibiotic application, length of hospital stay, albumin level, operation time and POSSUM score (P<0.05). Binary multivariate logistic regression analysis showed that tracheotomy status, length of hospital stay and POSSUM score were independent risk factors for postoperative pulmonary infection in patients with oral cancer (P<0.05). The area under the ROC curve (AUC) of the risk prediction model constructed based on the above risk factors was 0.878, which was higher than the AUC values of tracheotomy status, length of hospital stay and POSSUM score alone. Meanwhile, the model had high credibility in the calibration verification curve. The Hosmer-Lemeshow goodness-of-fit test indicated that the nomogram model had good consistency in predicting postoperative pulmonary infection in oral cancer patients (χ²=4.064, P=0.851; df=8). CONCLUSIONS: The nomogram model constructed based on tracheotomy status, length of hospital stay and POSSUM score has good predictive performance for postoperative pulmonary infection in oral cancer patients. It can provide a quantitative tool for clinical early identification of high-risk patients and formulation of individualized prevention and control strategies.

Key words: Oral cancer, Pulmonary infection, POSSUM score, Nomogram, Risk prediction

中图分类号:

R739.8

彭妍, 袁冯, 仝祎冉, 邹佳佳, 马改丽, 张子英, 郭君. 基于POSSUM评分的列线图模型对口腔癌术后肺部感染的预测价值[J]. 中国口腔颌面外科杂志, 2026, 24(3): 272-279.

Peng yan, Yuan Feng, Tong Yiran, Zou Jiajia, Ma Gaili, Zhang Ziying, Guo Jun. Predictive value of a POSSUM-based nomogram model for postoperative pulmonary infection in oral cancer patients[J]. China J Oral Maxillofac Surg, 2026, 24(3): 272-279.

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