ArticleHead & face medicine2026
Tooth-level risk modeling and prediction of fixed orthodontic bracket bonding failure: a real-world retrospective cohort study.
Article in Head & face medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveBased on real-world retrospective cohort data, this study aims to identify risk factors for fixed orthodontic bracket bonding failure, develop a risk prediction model, and create a visualized online predictive tool for individualized clinical risk management.
methodsA total of 5,808 tooth positions from 286 patients undergoing fixed orthodontic treatment during January 2021-January 2024 were analyzed. Using tooth position as the analysis unit and patient as the clustering unit, a multivariable Logistic regression model was constructed via generalized estimating equations (GEE) to identify independent risk factors for bracket bonding failure. A standard Logistic regression model was established for prediction. Model performance was evaluated using ROC curve, Hosmer-Lemeshow calibration test, and Bootstrap resampling. A web-based dynamic prediction tool was developed based on the prediction equation.
resultsBonding failure occurred at 548 tooth positions (9.44%), involving 239 patients. The GEE model identified plaque index, gingival index, hard food biting, bruxism, molar position, ceramic bracket, and use of intermaxillary elastics as independent risk factors (all P < 0.05). The prediction model exhibited an AUC of 0.613 (95% CI: 0.588–0.633). The Hosmer-Lemeshow test yielded a P-value of 0.419, and the Bootstrap-corrected AUC was 0.6027. The developed online dynamic tool provided real-time outputs including the predicted failure probability for a single tooth position, corresponding risk level, and clinical recommendations.
conclusionBracket bonding failure is multifactorial, linked to oral hygiene, habits, tooth position, and bracket type. The well-calibrated prediction model and accompanying online tool can aid clinicians in risk assessment and support personalized treatment planning.
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