天津医药 ›› 2026, Vol. 54 ›› Issue (1): 57-63.doi: 10.11958/20252221

• 临床研究 • 上一篇    下一篇

拔除第一前磨牙正畸治疗后牙龈内陷的危险因素及预测模型的构建

陈敏1(), 夏莉1, 朱荣媛1, 王欣雨1, 季骏2   

  1. 1 泰州市第四人民医院口腔科(邮编225300)
    2 南京市口腔医院正畸科
  • 收稿日期:2025-06-06 修回日期:2025-09-10 出版日期:2026-01-15 发布日期:2026-01-19
  • 作者简介:陈敏(1981),女,副主任医师,主要从事口腔正畸治疗方面研究。E-mail:624189441@qq.com
  • 基金资助:
    江苏省口腔医学会中青年科研项目(JOA2022-7)

Risk factors and prediction model construction of gingival invagination after orthodontic treatment of extracted first premolar teeth

CHEN Min1(), XIA Li1, ZHU Rongyuan1, WANG Xinyu1, JI Jun2   

  1. 1 Department of Stomatology, the Fourth People's Hospital of Taizhou, Taizhou 225300, China
    2 Department of Orthodonitics, Nanjing Stomatological Hospital
  • Received:2025-06-06 Revised:2025-09-10 Published:2026-01-15 Online:2026-01-19

摘要:

目的 分析拔除第一前磨牙正畸治疗后牙龈内陷的危险因素,并构建相关预测模型。方法 回顾性收集在泰州市第四人民医院接受正畸治疗的患者272例(890个拔牙部位)并采用留出法以4∶1比例随机分为建模队列(218例共692个拔牙部位)和内部验证队列(54例共198个拔牙部位)。另选择同期在南京市口腔医院接受正畸治疗的患者133例(共计502个拔牙部位)作为外部验证队列。采用LASSO回归分析筛选关键变量,以患者是否发生牙龈内陷为因变量,进行多因素Logistic回归分析,基于独立危险因素构建列线图,并对其预测性能进行验证。结果 共纳入405例患者总计1 392个拔牙部位,其中862个(61.9%)拔牙部位发生牙龈内陷。LASSO回归分析共筛选出9个关键变量,Logistic回归进一步发现,拔牙后≥2周开始正畸治疗、薄龈生物型牙龈、距CEJ根尖4 mm处的颊骨厚度、牙根中段的颊骨厚度均为拔除第一前磨牙正畸治疗后牙龈内陷的独立影响因素(P<0.05)。受试者工作特征曲线分析结果显示,建模队列、内部验证队列、外部验证队列的曲线下面积分别为0.813(95%CI:0.777~0.850)、0.816(95%CI:0.745~0.888)和0.815(95%CI:0.782~0.847);Hosmer-Lemeshow检验结果显示,建模队列、内部验证队列、外部验证队列的模型拟合良好(χ2分别为5.768、5.719和5.727,均P>0.05);Kappa分析结果显示,建模队列、内部验证队列、外部验证队列的Kappa值均>0.6,预测结果与实际结果高度一致。结论 拔除第一前磨牙正畸治疗后牙龈内陷与多方面因素有关,根据相关危险因素构建的列线图预测模型能较准确地预测牙龈内陷的发生,可为牙龈内陷的早期防治提供参考。

关键词: 正畸学,矫正, 第一前磨牙, 牙龈内陷, 因素分析, 列线图

Abstract:

Objective To analyze the risk factors for gingival invagination after orthodontic treatment for extraction of the first premolar and to construct a relevant prediction model. Methods A total of 272 patients (890 tooth extraction sites) who received orthodontic treatment in Taizhou Fourth People's Hospital were retrospectively collected as research objects. By means of the set apart method, they were randomly divided into the modeling cohort (n=218, including 692 tooth extraction sites) and the internal validation cohort (n=54, 198 tooth extraction sites). Another 133 patients (with a total of 502 tooth extraction sites) who received orthodontic treatment in Nanjing Stomatological Hospital during the same period were selected as the external validation cohort. LASSO regression analysis was used to screen key variables. With the occurrence of gingival invagination as the dependent variable, multi-factor Logistic regression analysis was carried out. A nomogram prediction model was constructed based on independent risk factors, and its prediction performance was verified. Results In this study, 405 patients with 1 392 extraction sites were included, and gingival invagination occurred in 862 extraction sites, with an incidence rate of 61.9%. A total of 9 key variables were screened in the LASSO regression analysis. Results of multifactorial analysis showed that the initiation of orthodontic treatment at ≥2 weeks after extraction, thin gingival biotypic gingiva, the buccal bone thickness 4 mm from the apical CEJ root tip and the buccal bone thickness of the mid-root section of the root were independent risk factors for gingival invagination after orthodontic treatment of the first premolar (P<0.05). Results of the subjects' work characteristic curves showed that the AUCs of the modelling cohort, the internal validation cohort and the external validation cohort were 0.813 (95%CI: 0.777-0.850), 0.816 (95%CI: 0.745-0.888) and 0.815 (95%CI: 0.782-0.847), respectively. Results of the Hosmer- Lemeshow test showed that the χ2 for the modelling cohort, internal validation cohort and external validation cohort were 5.768 (P=0.673), 5.719 (P=0.685) and 5.727 (P=0.680), respectively, indicating good model fitting. Kappa analysis results showed that the Kappa values of the modeling queue, internal validation queue and external validation queue were all greater than 0.6, and the predicted results were highly consistent with the actual results. Conclusion Gingival recession after orthodontic treatment of the first premolar extraction is related to various factors such as orthodontic treatment time, gingival biotype and cheekbone thickness. The column-line diagram prediction model constructed based on the related risk factors can predict the occurrence of gingival invagination more accurately, which can provide a reference for the early prevention and treatment of gingival invagination.

Key words: orthodontics, corrective, first premolar, gingival invagination, factor analysis, nomogram

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