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A Prediction Model for External Root Resorption of the Second Molars Associated With Third Molars
Zhengwei Kou1, Wuyang Zhang2, Chen Li3
1Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, College of Stomatology, Chongqing Medical University, Chongqing, China; People's Hospital of Shenzhen Baoan District, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China.
Objectives:
The aim of this study is to investigate risk factors for external root resorption (ERR) of second molars (M2) associated with impacted third molars (M3), and to develop a prediction model that can offer dentists a reliable and efficient tool for predicting the likelihood of ERR.
Methods:
A total of 798 patients with 2156 impacted third molars were collected from three centres between 1 December 2018 and 15 December 2018. ERR was identified by cone beam computed tomography examinations. The effects of different risk factors on the presence/absence of ERR and its severity were analysed using Chi-square or Fisher test. Multivariate logistic regressive analysis with stepwise variable selection methods was performed to identify factors which were significant predictors for ERR and its severity. Subsequently, a prediction model was developed, and the model performance was validated internally and externally.
Results:
The overall incidence of ERR of second molars was 16.05%. The prediction model was established using six factors including position (upper/lower jaw), impact type, impact depth (PG: A-B-C), contact position, root number of M3, and age. In terms of internal validation, the prediction model demonstrated satisfactory performance, achieving an area under curve of 0.961 and a prediction accuracy of 0.907. As for external validation, the area under curve remained high at 0.953, with a prediction accuracy of 0.892.
Conclusion:
A risk prediction model for ERR was established in the present study. Position (upper or lower jaw), impact type, impact depth (PG: A-B-C), contact position, root number of M3, and age were identified as influencing variables which were significant predictors in the development of this predictive model. The prediction model showed great discrimination and calibration.
Clinical Relevance:
This prediction model has the potential to aid dentists and patients in making clinical decisions regarding the necessity of M3 extraction.
