上海口腔医学

上海口腔医学 ›› 2024, Vol. 33 ›› Issue (4): 393-397.doi: 10.19439/j.sjos.2024.04.010

• 论著 • 上一篇    下一篇

基于logistic回归分析下颌第三磨牙近中、垂直阻生的危险因素

王蕊1, 邹慧儒2,3, 刘琪1, 常攀辉1   

  1. 1.首都医科大学附属北京口腔医院王府井院区 口腔综合科,北京 100050;
    2.南开大学医学院天津市口腔医院 中心实验室,天津 300041;
    3.天津市口腔功能重建重点实验室,天津 300041
  • 收稿日期:2024-03-08 修回日期:2024-04-09 出版日期:2024-08-25 发布日期:2024-09-03
  • 通讯作者: 邹慧儒,E-mail: zouhuiru@163.com
  • 作者简介:王蕊（1991-）,女,本科,护师,E-mail: 13716254376@163.com
  • 基金资助:
    天津市自然科学基金（18JCYBJC27000）

Risk factors for mesioangular and vertical impactions of mandibular third molars based on logistic regression analysis

WANG Rui1, ZOU Hui-ru2,3, LIU Qi1, CHANG Pan-hui1   

  1. 1. Department of Stomatology, Wangfujing Branch, Beijing Stomatological Hospital, Capital Medical University. Beijing 100050;
    2. Center Laboratory, Tianjin Stomatological Hospital, School of Medicine, Nankai University. Tianjin 300041;
    3. Tianjin Key Laboratory of Oral and Maxillofacial Function Reconstruction. Tianjin 300041, China
  • Received:2024-03-08 Revised:2024-04-09 Online:2024-08-25 Published:2024-09-03

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摘要： 目的：探讨下颌第三磨牙近中、垂直阻生的危险因素,并基于logistic回归分析构建预测模型。方法：收集2021年6月—2023年12月天津市口腔医院243颗下颌第三磨牙的临床资料,根据萌出类型分为萌出组和阻生组,阻生组包括近中阻生和垂直单侧阻生。单因素分析筛选出具有统计学意义的因素后,采用logistic回归分析方法进行多因素分析,进一步绘制列线图,预测下颌第三磨牙阻生的风险因素。采用SPSS 27.0软件包对数据进行统计学分析。结果：243颗下颌第三磨牙中,萌出组75例（30.86%）,阻生组168例（69.14%）。2组年龄、性别、牙根数、Co-Go、Co-Cop、W2、W3、W4和L相比,差异无统计学意义（P>0.05）。萌出组与阻生组Nolla、L-6缺失、L-E缺失、Co-Pog、Co-Go/Co-Pog、L6-MP、α和W1相比,差异有统计学意义（P<0.05）。多因素回归分析显示,Nolla、L-6缺失、L-E缺失、Co-Pog、Co-Go/Co-Pog、L6-MP、α和W1是下颌第三磨牙发生近中、垂直阻生的独立危险因素（P<0.05）。列线图构建显示较高的预测准确性。受试者工作特征曲线（ROC）分析结果表明,独立危险因素联合预测下颌第三磨牙近中、垂直阻生的曲线下面积（AUC）为0.924,95%CI为0.887~0.960,灵敏度为86.9%,特异度为86.7%。结论：Nolla、L-6缺失、L-E缺失、Co-Pog、Co-Go/Co-Pog、L6-MP、α和W1是影响下颌第三磨牙近中、垂直阻生的主要危险因素,利用logistic回归分析和列线图可有效预测阻生风险,为临床治疗提供科学依据。

关键词: 下颌第三磨牙, 近中阻生, 垂直阻生, 危险因素, Logistic回归分析

Abstract: PURPOSE: To explore the risk factors for mesioangular and vertical impactions of the mandibular third molars and to construct a predictive model based on logistic regression analysis. METHODS: Clinical data of 243 mandibular third molars collected from June 2021 to December 2023 at Tianjin Stomatology Hospital were classified into the eruption group and the impaction group, with the latter including mesioangular and vertical unilateral impactions. The clinical data were subjected to univariate analysis to screen for statistically significant factors, followed by multivariate analysis using logistic regression to further delineate risk factors for mandibular third molar impaction, with the construction of a nomogram for prediction.SPSS 27.0 software package was used for statistical analysis. RESULTS: Totally 243 mandibular third molars were included, and 75 (30.86%) were in the eruption group and 168 (69.14%) in the impaction group. No significant difference was found between the groups regarding age, gender, number of tooth roots, Co-Go, Co-Cop, W2, W3, W4 and L (P>0.05). Significant differences were observed between the eruption and impaction group concerning Nolla, L-6 missing, L-E missing, Co-Pog, Co-Go/Co-Pog, L6-MP, α and W1(P<0.05). Multivariate regression analysis revealed that Nolla, absence of L-6, absence of L-E, Co-Pog, Co-Go/Co-Pog, L6-MP, α and W1 were independent risk factors for mesioangular and vertical impactions of the mandibular third molars (P<0.05). The construction of nomogram demonstrated high predictive accuracy. Analysis of the receiver operating characteristic curve(ROC) indicated that the area under the curve(AUC) for the joint prediction of mesial and vertical impaction of the mandibular third molar by independent risk factors was 0.924, with a 95%CI of 0.887 to 0.960. The sensitivity was reported to be 86.9%, and the specificity was 86.7%. CONCLUSIONS: Nolla, absence of L-6, absence of L-E, Co-Pog, Co-Go/Co-Pog, L6-MP, α and W1 are major risk factors affecting the impaction of mandibular third molars. The use of logistic regression analysis and nomograms can effectively predict the risk of impaction, providing a scientific basis for clinical treatment.

Key words: Mandibular third molar, Mesioangular impaction, Vertical impaction, Risk factors, Logistic regression analysis

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