Preoperative Vitamin D as Predictor of MRONJ: A Retrospective Multivariate Analysis

TL;DR

Low vitamin D levels before surgery predict a higher risk of jaw complications in cancer patients on certain medications.

Contribution

Identifies preoperative vitamin D as an independent predictor of MRONJ in cancer patients undergoing dental extractions.

Findings

18 out of 61 patients developed MRONJ after dental extractions.

Low vitamin D levels were a strong independent predictor of MRONJ (OR = 8.74).

Extractions in the mandible were also a significant risk factor (OR = 7.94).

Abstract

Background: Medication-related osteonecrosis of the jaw (MRONJ) is a grave complication in patients with cancer treated with antiresorptive agents, particularly after invasive dental procedures. Identifying reliable clinical factors to assess MRONJ risk remains a clinical challenge. Methods: The retrospective observational study comprised 61 oncologic patients undergoing dental extractions during antiresorptive therapy. Preoperative serum levels of 25-hydroxyvitamin D and β-C-terminal telopeptide cross-link (β-CTx), along with relevant clinical variables, were measured. The analyses included comparative tests, multivariate logistic regression to detect independent predictors of MRONJ, and ROC curve analysis to assess the model’s predictive performance. Results: MRONJ was diagnosed in 18 patients (29.5%). Low preoperative vitamin D levels were significantly associated with MRONJ and remained an independent predictor in the multivariate analysis (OR = 8.74, p = 0.005). The mandibular extraction site was also identified as a significant risk factor (OR = 7.94, p = 0.007). In contrast, β-CTX levels, age, sex, cancer type, and the number of extracted teeth did not show a significant link to MRONJ development in this cohort. The comprehensive multivariate model demonstrated good discrimination capacity (AUC = 0.806). Conclusions: Preoperative vitamin D deficiency is an important independent predictor of MRONJ after dental extractions in patients with cancer receiving antiresorptive agents. Integrating metabolic biomarkers and clinical variables into predictive models may improve risk assessment and support the development of more effective preoperative prevention strategies.

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