Developing a Clinical and Conventional Radiographic Tool for Vertical Root Fracture Prediction in Root-Filled Teeth.

AIM: To develop and validate a clinical and radiographic prediction model for diagnosing vertical root fracture (VRF) in endodontically treated teeth to assist clinical decision-making. METHODOLOGY: This cross-sectional diagnostic prediction study analysed data from 415 root-filled roots (130 confirmed VRF and 285 non-VRF) obtained from 327 patients who underwent endodontic microsurgery or surgical exploration. Fourteen candidate predictors, comprising patient demographics, tooth characteristics including clinical signs and symptoms, and radiographic features, were evaluated. A multivariable logistic regression model with backward stepwise elimination was developed to identify key predictors. Model performance was assessed using the area under the receiver operating characteristic curve (AuROC), calibration plots and internal validation was performed using a bootstrapping technique with 1000 replicates to obtain optimism-corrected estimates. RESULTS: The final prediction model retained seven predictors: age, tooth type, presence of sinus tract, probing depth ≥ 5 mm, post type, periapical radiographic status and root canal space-to-root width ratio. Among these, five predictors demonstrated statistically significant associations (p < 0.05): age (aOR 1.03), probing depth ≥ 5 mm (aOR 7.64), isolated perilateral radiolucency (aOR 21.56), halo radiolucency (aOR 8.67) and root canal space-to-root width ratio > 1/3 (aOR 3.28). The model demonstrated excellent discrimination with an AuROC of 0.84 (95% CI: 0.80-0.89). Internal validation provided an optimism-corrected AuROC of 0.81 (95% CI: 0.76-0.85) with an optimism-corrected calibration slope of 0.78 (95% CI 0.42-1.02). Risk stratification based on likelihood ratios effectively categorised patients into five risk levels, demonstrating a positive predictive value of 94.5% for roots predicted as 'highly suspect' category. CONCLUSIONS: The developed diagnostic prediction model, incorporating seven clinical and radiographic parameters, demonstrates excellent discriminative ability and good calibration. This tool enables accurate risk stratification of VRF in root-filled teeth, supporting clinicians in making evidence-based treatment decisions.