ABSTRACT
OBJECTIVES: The extraction of impacted mandibular third molars is a common surgical procedure often associated with complications including post-traumatic neuropathy. Previous work has focused on identifying confounding factors, but a robust preoperative risk prediction model remains elusive. METHODS: Using a dataset of 648 patients and 812 impacted mandibular third molars, we used least absolute shrinkage and selection operator (LASSO) to fit prediction models based on risk factors assessed at both the tooth and patient levels. In addition, we fitted multivariable logistic regression models with the Firth correction for generalized estimating equations (GEE). RESULTS: The LASSO model for post-traumatic neuropathy identified distoangular impaction of ≥ 45° (odds ratio [OR] = 2.9), proximity to the inferior alveolar nerve of ≤ 3 mm (OR = 1.9), disadvantageous curving (OR = 1.4), and psychiatric conditions (OR = 2.1) as predictors [area under the receiving operator characteristic curve (AUC) = 0.75]. Among other complications analyzed, the LASSO model for bleeding identified deep embedding or full impaction (OR = 1.8), psychiatric conditions (OR = 1.3), and age (OR = 0.9) as predictors (AUC = 0.64). These associations between predictors and postoperative complications were fundamentally reinforced by the corresponding GEE models. CONCLUSIONS: Our findings point to the predictability of post-traumatic neuropathy and bleeding based on tooth anatomy and patient characteristics, overall suggesting that preoperatively identifiable factors can predict the risk of adverse outcomes in the extraction of impacted mandibular third molars. CLINICAL SIGNIFICANCE: Mandibular third molar extraction is both a routine procedure and a leading cause of trigeminal neuropathy. Prevention of post-traumatic neuropathy, aided by individualized preoperative risk prediction, is of high clinical relevance.
