Evaluating the risk factors of inferior alveolar nerve injury following removal of the mandibular third molars / 中华口腔医学杂志

ABSTRACT

Objective: To evaluate the risk factors of inferior alveolar nerve injury (IANI) after surgical removal of the mandibular third molars (M3) and present a new risk scoring system to predict the probability of IANI. Methods: Patients who underwent extraction of M3 in the Stomatology Hospital, Zhejiang University School of Medicine from April 2017 to December 2019 were involved. The investigators enrolled a sample composed of 949 mandibular third molars. Prediction model was used for univariate and multivariate analysis of gender, age, M3, inferior alveolar canal (IAC), and the contact between M3 and IAC, to assess the risk factors of IANI. Combined with the risk factors determined by the outcomes of prediction model, the risk scoring system was constructed. The diagnostic performance of each cut-off score was examined to conduct a risk stratification of IANI risk scores. The predictive ability and reliability of the model were evaluated. Results: In prediction model, twenty nine cases (4.4%, 29/664) experienced postoperative IANI. Number of root (P<0.01), depth of impaction (P<0.05), contact between M3 and IAC (P<0.01) and their contact position (P<0.05) were statistically significant as contributing risk factors of IANI. Specifically, the incidence of temporary IANI was higher in those who aged under 25 years (P<0.001), while female suffer more permanent injury (P<0.05). Based on the IANI risk scoring system, patients were stratified into low-risk, middle-risk and high-risk groups at cutoff scores of 3 and 4. The area under the receiver operator characteristic curve of the risk scoring system were 0.81 [95%CI (0.70-0.90), P=0.002] and 0.80 [95%CI (0.68-0.92), P=0.007] towards good discrimination. Conclusions: Age, gender, number of root, depth of impaction, and contact between M3 and IAC were risk factors of IANI. IANI risk scoring system might help in preoperative assessment, recognition of high-risk cases and decision-making to reduce IANI.