Ling-Ying Wei, Ching-Ming Chiu, Sang-Heng Kok, Hung-Ying Lin, Wei-Yih Chiu, Hao-Hong Chang, Shih-Jung Cheng, Jang-Jaer Lee
The model demonstrates good discrimination and excellent "rule-out" utility in a real-world, low-prevalence setting, identifying low-risk patients (<12 points) for safe dental extraction, thereby facilitating evidence-based clinical decision-making.
BACKGROUND/PURPOSE: Although risk factors for medication-related osteonecrosis of the jaw (MRONJ) are known, clinical assessment prior to dental extraction remains largely subjective. We aimed to develop and externally validate a simple clinical risk scoring system to predict post-extraction MRONJ in osteoporotic patients receiving antiresorptive therapy (ART) and support evidence-based clinical decision-making.
MATERIALS AND METHODS: This retrospective cohort study used a derivation cohort (N = 1067 extractions, 2003-2022) to develop a 5- to 16-point risk score system based on five predictors (age ≥75, ART duration ≥24 months, bisphosphonate use, drug interruption <3 months, and extraction site). The model was validated in an independent cohort (N = 928 extractions, 2022-2024) using a pre-defined ≥12 cut-off. Performance metrics included the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
RESULTS: MRONJ prevalence was lower in the validation cohort (2.2% vs. 24.5%; P< 0.001), reflecting stricter drug interruption adherence (90.0% vs. 71.5%). The AUC was 0.86 and 0.75 in derivation and validation cohorts, respectively. At the ≥12 cut-off, sensitivity was 65.0%, specificity 67.5%, PPV 4.2%, and NPV 98.9%.
CONCLUSION: The model demonstrates good discrimination and excellent "rule-out" utility in a real-world, low-prevalence setting, identifying low-risk patients (<12 points) for safe dental extraction, thereby facilitating evidence-based clinical decision-making.