上海口腔医学

上海口腔医学 ›› 2025, Vol. 34 ›› Issue (6): 611-616.doi: 10.19439/j.sjos.2025.06.009

• 论著 • 上一篇    下一篇

基于logistic回归的口腔种植体周围炎预测模型构建及影响因素分析

沈林汉1, 俞浩2, 陈旭3, 李杨4   

  1. 1.南京大学医学院附属苏州医院 口腔科, 江苏 苏州 215100;
    2.江苏省疾病预防控制中心 慢性非传染病防治所, 江苏 南京 210009;
    3.南京医科大学附属口腔医院 牙周病科, 江苏 南京 210019;
    4.南京医科大学第四附属医院 口腔科, 江苏 南京 210031
  • 收稿日期:2025-07-24 修回日期:2025-09-01 发布日期:2025-12-30
  • 通讯作者: 李杨,E-mail: lynydsfy@163.com
  • 作者简介:沈林汉(1990-),男,硕士,主治医师,E-mail: 334130939@qq.com
  • 基金资助:
    江苏省卫生健康委科研课题（BJ24029）

Construction of a prediction model for peri-implantitis based on logistic regression and analysis of influencing factors

Shen Linhan1, Yu Hao2, Chen Xu3, Li Yang4   

  1. 1. Department of Stomatology, The Affiliated Suzhou Hospital of Nanjing University Medical School. Suzhou 215100;
    2. Chronic Non Communicable Disease Prevention and Control Institute of Jiangsu Provincial Center for Disease Control and Prevention. Nanjing 210009;
    3. Department of Periodontology, Affiliated Stomatological Hospital of Nanjing Medical University. Nanjing 210019;
    4. Department of Stomatology, The Fourth Affiliated Hospital of Nanjing Medical University. Nanjing 210031, Jiangsu Province, China
  • Received:2025-07-24 Revised:2025-09-01 Published:2025-12-30

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摘要： 目的:探讨牙列缺损患者口腔种植术后发生种植体周围炎的影响因素,构建并验证个体化预测模型。方法:回顾性纳入2021年9月—2025年3月于南京大学医学院附属苏州医院接受口腔种植术的患者,通过1∶1倾向评分匹配最终纳入感染组（确诊种植体周围炎）和未感染组各100 例。收集患者基线资料及血清白细胞介素17A（IL-17A）水平,采用二元logistic回归分析影响因素,基于筛选结果构建列线图预测模型,通过 Bootstrap验证、受试者工作特征（receiver operating characteristic,ROC）曲线及决策树模型评估预测效能。结果:感染组糖尿病、吸烟史、慢性牙周炎病史、种植体周围牙槽骨不良比例及IL-17A水平均显著高于未感染组（P<0.05）。Logistic回归显示,糖尿病、吸烟史、慢性牙周炎病史、种植体周围牙槽骨不良及 IL-17A 升高是种植体周围炎的独立危险因素（OR>1,P<0.05）。列线图模型 C-index为0.905,ROC 曲线下面积（area under the curve,AUC）为0.905（95%CI：0.865~0.946,P<0.001）,最佳截断值 48.80 分（特异度为0.880、敏感度为0.820、约登指数为0.700）。决策树模型显示 IL-17A 为首要预测因子,当 IL-17A>14.380 ng/L 时,种植体周围炎发生率达 87.50%。结论:糖尿病、吸烟史、慢性牙周炎病史、种植体周围牙槽骨状况及 IL-17A 水平是种植体周围炎的关键影响因素,构建的列线图模型预测效能优异,可用于术前个体化风险评估。

关键词: 种植体周围炎, 口腔种植, Logistic 回归, 列线图, 预测模型, IL-17A

Abstract: PURPOSE: To explore the influencing factors of peri-implantitis in patients with dentition defects after oral implant surgery, and to construct and validate a personalized prediction model. METHODS: Patients who underwent oral implant surgery in the Affiliated Suzhou Hospital of Nanjing University Medical School from September 2021 to March 2025 were included retrospectively. Through 1∶1 propensity score matching, 100 cases in the infected group (diagnosed with peri-implantitis) and 100 cases in the non-infected group were finally included. The baseline data of patients and serum interleukin-17A (IL-17A) levels were collected. Binary logistic regression was used to analyze the influencing factors. A nomogram prediction model was constructed based on the screening results, and the prediction performance was evaluated by Bootstrap validation, receiver operating characteristic(ROC) curve and decision tree model. RESULTS: The proportions of diabetes, smoking history, chronic periodontitis history, poor alveolar bone around implants, and IL-17A levels in the infected group were significantly higher than those in the non-infected group(P<0.05). Logistic regression showed that diabetes, smoking history, chronic periodontitis history, poor alveolar bone around implants, and elevated IL-17A were independent risk factors for peri-implantitis(OR>1, P<0.05). The C-index of the nomogram model was 0.905, the area under the ROC curve(AUC) was 0.905 (95%CI: 0.865-0.946, P<0.001), and the optimal cut-off value was 48.80 points (specificity was 0.880, sensitivity was 0.820, Youden index was 0.700). The decision tree model showed that IL-17A was the primary predictor; when IL-17A > 14.380 ng/L, the incidence of peri-implantitis reached 87.50%. CONCLUSIONS: Diabetes, smoking history, chronic periodontitis history, alveolar bone condition around implants and IL-17A level are key influencing factors of peri-implantitis. The constructed nomogram model has excellent prediction performance and can be used for preoperative individualized risk assessment.

Key words: Peri-implantitis, Oral implantation, Logistic regression, Nomogram, Prediction model, IL-17A

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