基于术前资料的口腔颌面部间隙感染患者住院时间延长预测模型的建立

doi:10.19438/j.cjoms.2025.01.009

中国口腔颌面外科杂志 ›› 2025, Vol. 23 ›› Issue (1): 48-54.doi: 10.19438/j.cjoms.2025.01.009

基于术前资料的口腔颌面部间隙感染患者住院时间延长预测模型的建立

朱岩岩¹, 丁嘉慧¹, 吕中静², 郑纪伟², 蒋宁宁³, 张银银², 孙玉华²

1.徐州医科大学口腔医学院,江苏徐州 221000;
2.徐州医科大学附属医院口腔科,江苏徐州 221000;
3.徐州医科大学附属口腔医院,江苏徐州 221000

收稿日期:2024-02-03 修回日期:2024-03-29 出版日期:2025-01-20 发布日期:2025-01-23
通讯作者: 孙玉华,E-mail: yuhua.sun@xzhmu.edu.cn
作者简介:朱岩岩（1996-）,女,硕士,E-mail: 18096771621@163.com

Establishment of a prediction model for hospital stay extension in patients with oral and maxillofacial space infection based on preoperative data

ZHU Yan-yan¹, DING Jia-hui¹, LYU Zhong-jing², ZHENG Ji-wei², JIANG Ning-ning³, ZHANG Yin-yin², SUN Yu-hua²

1. School of Stomatology, Xuzhou Medical University. Xuzhou 221000;
2. Department of Stomatology, Affiliated Hospital of Xuzhou Medical University. Xuzhou 221000;
3. Affiliated Stomatology Hospital of Xuzhou Medical University. Xuzhou 221000, Jiangsu Province, China

Received:2024-02-03 Revised:2024-03-29 Online:2025-01-20 Published:2025-01-23

摘要/Abstract

摘要： 目的：分析口腔颌面部间隙感染（oral and maxillofacial space infection,OMSI）患者住院时间延长的危险因素,建立风险预测模型,为临床干预及管理提供参考。方法：回顾性收集2019年7月—2023年7月在徐州医科大学附属医院收治的265例OMSI患者。以住院时间的第75百分位数为分界点,分为住院时间延长组和正常组,比较2组患者术前临床资料的差异,通过Lasso回归和多因素logistic回归分析影响患者住院时间延长的相关因素,并基于此建立一种新型OMSI住院时间延长的风险评估模型,结合受试者工作特征曲线、Hosmer-Lemeshow校准曲线和临床决策曲线对模型进行评价。采用SPSS 26.0 软件包和R语言4.2.2对数据进行统计学分析。结果：将Lasso回归筛选出回归系数不为零的变量纳入多因素logistic回归,分析结果显示,基础疾病（OR=2.43,95%CI：1.25~4.70）、间隙数目（OR=1.67,95%CI：1.30~2.14）、纤维蛋白原（OR=1.31,95%CI：1.08~1.60）、IL-6（OR=1.01,95%CI：1.00~1.01）是OMSI患者住院时间延长的独立危险因素（P<0.05）。利用上述独立危险因素构建预测模型,预测评分模型的AUC为0.834（95%CI：0.780~0.888）,Hosmer-Lemeshow校准曲线检验提示预测模型拟合优度良好（P=0.4555）,决策曲线分析表明模型具有较高的临床实用性。结论：本研究构建的口腔颌面部间隙感染患者住院时间延长风险评估模型具有较好的预测效能,有助于早期识别长期住院的高风险患者,及时采取有效干预措施,减轻患者与医疗机构负担。

关键词: 口腔颌面部间隙感染, 住院时间, 风险预测模型, 术前资料

Abstract: PURPOSE: To analyze the risk factors for prolonged hospitalization in patients with oral and maxillofacial space infection(OMSI) and establish a risk prediction model to provide reference for clinical intervention and management. METHODS: A retrospective analysis was conducted on 265 patients with OMSI admitted to the Affiliated Hospital of Xuzhou Medical University from July 2019 to July 2023. Using the 75th percentile of hospitalization time as the cutoff point, the patients were divided into prolonged hospitalization group and normal group. The differences in preoperative clinical data between the two groups were compared. The factors influencing the prolonged hospitalization time were analyzed through Lasso regression and multivariate logistic regression. Based on this, a novel risk assessment model for prolonged hospitalization in OMSI was established. The model was comprehensively evaluated using receiver operating characteristic(ROC) curve, Hosmer-Lemeshow calibration curve, and clinical decision curve. SPSS 26.0 software package and R Lanuguage software 4.2.2 were used for statistical analysis of the data. RESULTS: The variables with nonzero regression coefficients selected by Lasso regression were included in the multivariate logistic regression analysis. The results showed that underlying diseases (OR=2.43, 95%CI: 1.25-4.70), number of spaces (OR=1.67, 95%CI: 1.30-2.14), fibrinogen(OR=1.31, 95%CI: 1.08-1.60), and IL-6(OR=1.01, 95%CI: 1.00-1.01) were independent risk factors for prolonged hospitalization in OMSI patients(P<0.05). Using these independent risk factors, a predictive model was constructed with an AUC of 0.834(95%CI: 0.780-0.888). Hosmer-Lemeshow calibration curve test indicated good fit of the prediction model(P=0.4555), and decision curve analysis showed that the model had high clinical utility. CONCLUSIONS: The risk assessment model for prolonged hospitalization in patients with oral and maxillofacial space infection constructed in this study has good predictive performance, which helps to identify high-risk patients for long-term hospitalization at an early stage and take timely and effective intervention measures to alleviate the burden on patients and medical institutions.

Key words: Oral and maxillofacial space infection, Length of hospital stay, Risk prediction model, Preoperative data

中图分类号:

R782.3

朱岩岩, 丁嘉慧, 吕中静, 郑纪伟, 蒋宁宁, 张银银, 孙玉华. 基于术前资料的口腔颌面部间隙感染患者住院时间延长预测模型的建立[J]. 中国口腔颌面外科杂志, 2025, 23(1): 48-54.

ZHU Yan-yan, DING Jia-hui, LYU Zhong-jing, ZHENG Ji-wei, JIANG Ning-ning, ZHANG Yin-yin, SUN Yu-hua. Establishment of a prediction model for hospital stay extension in patients with oral and maxillofacial space infection based on preoperative data[J]. China J Oral Maxillofac Surg, 2025, 23(1): 48-54.

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基于术前资料的口腔颌面部间隙感染患者住院时间延长预测模型的建立

Establishment of a prediction model for hospital stay extension in patients with oral and maxillofacial space infection based on preoperative data

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