Prediction of Medication-Related Osteonecrosis of the Jaw in Patients Receiving Antiresorptive Therapy Using Machine Learning Models.

Abstract

Background: Medication-related osteonecrosis of the jaw (MRONJ) is a serious complication associated with the use of antiresorptive agents, impacting patient quality of life and treatment outcomes. Predictive modeling may aid in a better understanding of MRONJ development.

Purpose: The study aimed to evaluate machine learning (ML)-based models for predicting MRONJ in patients receiving antiresorptive therapy.

Study design, setting, sample: This retrospective in silico study analyzed electronic medical records from Thammasat University Hospital, covering the period from January 2012 to December 2022. The sample included subjects receiving antiresorptive therapy, excluding those with a history of radiation therapy or metastatic jaw disease.

Predictor variables: The primary predictor variable was the predicted probability of MRONJ development from the ML models.

Outcome variables: The outcome variable was MRONJ status coded as present or absent based on chart review.

Covariates: Covariates included demographic data, MRONJ occurrence, location and staging of MRONJ, comorbidities, diseases related to antiresorptive agents, types of antiresorptive agents, therapy duration, concurrent medications, blood calcium levels, and dental factors.

Analyses: Model performance was assessed via accuracy, sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve. Additionally, univariate and multivariate Cox regression analyses were conducted to identify factors significantly associated with MRONJ development. P ≤ .05 was statistically significant.

Results: The study analyzed data from 5,305 subjects with a mean age of 75 ± 11.1 years, predominantly female. MRONJ was observed in 81 cases (1.5%), with a median time to development of 33 months (interquartile range = 3). Among the 6 models tested, the best-performing model had an accuracy of 0.95 and an area under the receiver operating characteristic curve of 0.89-0.90. Significant predictors identified through Cox regression included metabolic syndrome (hazard ratio = 14.064, 95% confidence interval = 1.111-178.067, P = .041) and patients receiving intravenous pamidronate (hazard ratio = 5.932, 95% confidence interval = 1.755-20.051, P = .004), indicating their association with MRONJ development.

Conclusions and relevance: ML-based predictive and time-to-event models effectively predict MRONJ risk, aiding in the strategic prevention and management for patients undergoing antiresorptive therapy.