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目的 构建并验证一个结合计算机断层扫描影像特征与临床指标的预测模型，用于早期评估颌面颈部间隙感染患者发生严重并发症的风险。方法 回顾性纳入2022年6月—2024年6月于甘肃省人民医院口腔颌面外科收治的96例颌面颈部间隙感染患者。收集患者的影像学资料（包括由于感染累及的间隙数量、病灶体积、强化特征等）与临床指标（包括年龄、糖尿病史、C反应蛋白、白细胞计数）。比较并发症组与非并发症组患者各项指标的差异。将单因素分析中差异具有统计学意义的变量纳入多因素Logistic回归分析，筛选独立危险因素并构建预测模型。采用受试者操作特征曲线及曲线下面积评估模型的区分效能。结果 并发症组患者在年龄、糖尿病患病率、C反应蛋白、白细胞计数、多间隙感染比例、感染灶体积、病灶强化程度，以及影像学上气体生成与液气平面出现的比例等方面，均显著高于非并发症组。多因素Logistic回归分析显示，年龄、合并糖尿病、C反应蛋白升高、白细胞计数升高、多间隙感染、感染体积增大，以及病灶强化是并发症发生的独立危险因素。结论 整合计算机断层扫描影像特征与临床指标的预测模型，有助于早期识别颌面颈部间隙感染中易发生并发症的高危患者。
Abstract：Objective To establish and validate a predictive model that combines computed tomography imaging features and clinical indicators for the early assessment of the risk of severe complications in patients with maxillofacial and deep neck space infections. Methods This study retrospectively enrolled 96 patients with maxillofacial and deep neck space infections admitted to The Department of Oral and Maxillofacial Surgery at Gansu Provincial Hospital between June 2022 and June 2024. Imaging data(including the number of involved spaces, lesion volume, and enhancement characteristics) and clinical indicators(such as age, history of diabetes, C-reactive protein, and white blood cell count) were collected and analyzed. The differences in various indicators between the complication group and the non-complication group were compared. Variables with statistical significance in univariate analysis were included in a multivariate Logistic regression analysis to screen for independent risk factors and construct the predictive model. The discriminatory performance of the model was evaluated using the receiver operating characteristic curve and the area under the curve. Results Patients in the complication group showed significantly higher values than those in the non-complication group in terms of age, prevalence of diabetes, C-reactive protein, white blood cell count, proportion of multi-space infections, lesion volume, degree of lesion enhancement, and the proportion of cases with gas formation and air-fluid levels on imaging. Multivariate Logistic regression analysis identified advanced age, comorbid diabetes, elevated C-reactive protein, elevated white blood cell count, multi-space infection, larger infection volume, and marked lesion enhancement as independent risk factors for complications. Conclusion The predictive model integrating computed tomography imaging features and clinical indicators can help identify high-risk patients prone to complications in the early stage of maxillofacial and deep neck space infections.
[1]WANG P H,HUANG Y L,LONG J. A five-year retrospective study of 746 cases with maxillofacial space infection in western China[J]. Infection and drug resistance,2022,15:5099-5110.
[2]ESHGHPOUR M,SABOURI M,LABAFCHI A,et al.Maxillofacial infections:a 5-year assessment of the epidemiology and etiology in an Iranian population[J]. Journal of maxillofacial and oral surgery,2024,23(6):1470-1477.
[3]宋巧，严佳，周韧，等.基于气道三维CT重建筛选颌面部间隙感染患者困难气道的预测指标[J].上海交通大学学报（医学版），2020,40(9):1263-1269.
[4]Zhang L, Wang Q, Li J, et al. The role of infection volume and enhancement pattern on contrast-enhanced CT in assessing the severity of deep neck infections[J]. European Journal of Radiology, 2020, 129:109089.
[5]WEI L S,OSMAN S,HATT M,et al. Machine learning for radiomics-based multimodality and multiparametric modeling[J]. The quarterly journal of nuclear medicine and molecular imaging,2019,63(4):323-338.
[6]张银银，朱岩岩，丁嘉慧，等. 144例口腔颌面部间隙感染并发症及入ICU监护的危险因素分析[J].中国口腔颌面外科杂志，2024,22(2):148-152.
[7]陈鹏，刘冰，魏博，等.老年口腔颌面颈部多间隙感染的诊治分析[J].中华老年口腔医学杂志，2021,19(3):145-148.
[8]孙静，徐亿普，田宝莹，等.糖尿病并发颌面颈部间隙感染患者临床资料的回顾性分析[J].兰州大学学报（医学版），2015,41(5):49-52.
[9]代天国，徐波，冉红兵，等.攀西地区115例重症口腔颌面颈部多间隙感染临床回顾性分析[J].实用口腔医学杂志，2023,39(2):260-263.
[10]华文哲，李娜，岳海泉，等.改良封闭负压引流术在口腔颌面部感染中的临床应用[J].兰州大学学报（医学版），2014,40(3):12-16.
[11]GAO W X,LIN Y,YUE H J,et al. Bacteriological analysis based on disease severity and clinical characteristics in patients with deep neck space abscess[J]. BMC infectious diseases,2022,22:280.
[12]李惠玲，李生梅，程丽东，等.口腔颌面部间隙感染病原学及其影响因素[J].中华医院感染学杂志，2022,32(8):1180-1184.
[13]CAPRIOLI S,TAGLIAFICO A,FIANNACCA M,et al.Imaging assessment of deep neck spaces infections:an anatomical approach[J]. La radiologia Medica,2023,128(1):81-92.
[14]FURUHOLM J,RAUTAPORRAS N,UITTAMO J,et al. Health status in patients hospitalised for severe odontogenic infections[J]. Acta odontologica scandinavica,2021,79(6):436-442.
[15]LIAO T I,HO C Y,CHIN S C,et al. Sequential impact of diabetes mellitus on deep neck infections:comparison of the clinical characteristics of patients with and without diabetes mellitus[J]. Healthcare,2024,12(14):1383.
[16]张银银，郑纪伟，朱岩岩，等. IL-6 IL-10 WBC/LYM在口腔颌面部间隙感染患者中的表达差异及临床价值[J].安徽医学，2024,45(9):1094-1099.
[17]KOÇR H,ABAKAY M A,SAYNİ. Determining the prognostic value of CRP and neutrophil lymphocyte ratio in patients hospitalized for deep neck infection[J].Brazilian journal of otorhinolaryngology,2024,90(6):101492.
[18]URECHESCU H,GHERAN-VIDA E,CUZIC C,et al.Inflammatory markers as predictors for prolonged duration of hospitalization in maxillofacial infections[J].Journal of clinical medicine,2023,12(3):871.
[19]RAUTAPORRAS N,UITTAMO J,FURUHOLM J,et al.Deep odontogenic infections-Computed tomography imaging-based spreading routes and risk for airway obstruction[J]. Journal of stomatology,oral and maxillofacial surgery,2023,124(4):101424.
[20]CORP F,KHOLEIF S,PERVEZ B,et al. Deep neck infections:a single-centre analysis of 100 cases in the West of Scotland[J]. British journal of oral and maxillofacial surgery,2025,63(7):509-513.
基本信息:
DOI：10.13885/j.issn.2097-681X.M20250127
中图分类号:R782.3
引用信息:
[1]侯大为,苟学立,边勤疆,等.基于CT影像学评估的颌面颈部间隙感染并发症预测模型及临床应用[J].兰州大学学报(医学版),2026,52(02):39-43+52.DOI:10.13885/j.issn.2097-681X.M20250127.
基金信息:
甘肃省自然科学基金资助项目（26JRRA736）; 兰州市科技计划资助项目（2020-ZD-30）
2025-02-11
2025
2026-01-22
2026
2025-02-27
1
2026-03-25
2026-03-25
2026-03-25